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AACFlow против StackAI

AACFlow — ИИ-рабочее пространство, где команды создают, развёртывают и сопровождают ИИ-агентов визуально, в диалоге или кодом. Вот как AACFlow соотносится с StackAI по архитектуре платформы, возможностям ИИ, интеграциям, ценам, безопасности и поддержке. Каждый факт ниже подтверждён источником и датой, последняя проверка: .

AACFlow — ИИ-рабочее пространство для создания, развёртывания и сопровождения ИИ-агентов. На этой странице AACFlow сравнивается с StackAI по архитектуре платформы, возможностям ИИ, интеграциям, ценам, безопасности и соответствию требованиям, наблюдаемости и поддержке; используются факты с источниками и датами для тех, кто выбирает между двумя платформами.

Что такое AACFlow?

AACFlow is the AI workspace where teams build, deploy, and manage AI agents, connecting 1,000+ integrations and every major LLM to automate real work visually, conversationally, or with code.

Что такое StackAI?

StackAI is a proprietary, enterprise-focused visual platform for building, deploying, and governing AI agents, connecting LLMs and business systems through a drag-and-drop, low-code node builder.

AACFlow и StackAI: сравнение по возможностям

В разделах ниже AACFlow и StackAI сравниваются по платформе и развёртыванию, ценам, безопасности и соответствию требованиям, возможностям ИИ, интеграциям, наблюдаемости и поддержке.

Платформа и развёртывание

CompareAACFlow vs StackAI
AACFlow
StackAI
Тип конструктора
StackAI
Drag-and-drop node canvas plus Code Node (источник: Platform Overview - StackAI Docs, откроется в новой вкладке)Visual/low-code node-based workflow builder. A 2D canvas where builders drag and drop nodes and connect them to build a workflow, drawing from Input, Output, Core (e.g. AI Agent, Knowledge Bases), Apps/integration, and Utils/Logic node categories; supports a Code Node for custom logic (the older Python Code node is now deprecated in favor of it).
Порог входа
AACFlow
Low, plus natural-language Chat for non-technical users (источник: AACFlow Docs: Introduction (visual, natural-language, and API/SDK building), откроется в новой вкладке)Low for visual building; natural-language Chat surface (with a workflow-scoped Copilot) for non-technical builders. Chat lets users describe a workflow in plain language and have AACFlow scaffold or edit it; the visual canvas requires no code to connect blocks.
StackAI
Not documentedUnknown
Варианты развёртывания
AACFlow
Managed cloud, no single-tenant VPC option (источник: AACFlow Pricing Page, откроется в новой вкладке)Cloud-hosted (managed, multi-tenant SaaS) operated by AACFlow. No documented managed single-tenant/VPC hosting tier. AACFlow is sold as a managed cloud service; there is no dedicated-instance/VPC offering.
StackAI
Cloud, VPC/on-prem, AWS Marketplace (источник: StackAI Hosted - AWS Marketplace, откроется в новой вкладке)Cloud SaaS, VPC/on-prem (Enterprise), plus an AWS Marketplace listing. Agents deploy to chat, forms, APIs, Slack, Teams, or batch run; also listed on AWS Marketplace as "StackAI Hosted".
Шаблоны
AACFlow
(источник: AACFlow codebase: templates gallery and APIs removed, откроется в новой вкладке)No: the pre-built workflow template gallery (landing and in-workspace) was removed platform-wide. AACFlow now surfaces per-integration starter prompts instead of a template library.
StackAI
(источник: Customizable AI Workflow Templates - StackAI, откроется в новой вкладке)Yes, template library across business functions. Pre-built templates for finance/compliance, business operations, customer service/support, sales, and more.
Продвижение между средами
AACFlow
(источник: AACFlow Docs: Workspace Forks, откроется в новой вкладке)Yes: fork a whole workspace into a dev/qa/prod-style child, diff it, and promote or roll back changes in either direction. Credential and env-var remapping is required before every promote, so secrets are never silently copied across environments. Gated to the Enterprise plan.
StackAI
(источник: The Agentic Development Life Cycle - StackAI blog, откроется в новой вкладке)Yes: full dev/staging/production workspace promotion with PR-gated approval. Three default isolated environments (development, staging, production), each independently connectable to different data sources/APIs; changes flow via pull requests that must be reviewed and approved before promotion, with a central admin approval queue. Custom environments (QA, experimentation, demo, client-specific) can be added.
Контроль версий
AACFlow
Deployment rollback plus Copilot edit diff/revert (источник: AACFlow codebase: WorkflowDiffEngine, откроется в новой вкладке)Deployed-version history with rollback for every workflow; server-persisted checkpoint/revert and visual diff (accept/reject) specifically for Copilot AI edits. Manual drag-and-drop undo/redo is client-side/localStorage only (capped at 100 ops, 5 stacks), not server-synced across devices. Deployment history does not include an arbitrary version-to-version diff tool, and knowledge base documents have no version history.
StackAI
Version history, diff, and rollback (источник: The Agentic Development Life Cycle - StackAI blog, откроется в новой вкладке)Version history with diff/compare and rollback via revert; no branching or client-side undo/redo. Every save creates a full version snapshot; a compare tool diffs nodes, prompts/LLM config, and connections between versions. Any version can be reverted, which creates a new version and preserves history.
Совместная работа в реальном времени
AACFlow
(источник: AACFlow codebase: realtime presence handler (cursor/selection broadcast), откроется в новой вкладке)Yes: live multiplayer editing of the same workflow canvas, with real-time cursors, selection broadcasting, and synced concurrent edits over a dedicated realtime backend
StackAI
Unknown, not confirmed in official docsUnknown: a third-party review mentions "real-time collaboration features," but StackAI's own documentation does not confirm live, concurrent multi-user editing (synced cursors, selections, live edits) on the same workflow canvas.. StackAI documents workspace and folder sharing with role-based access to projects. That is async collaboration, not verified simultaneous co-editing with presence indicators.
Встроенное файловое хранилище
AACFlow
(источник: AACFlow codebase: file share modal (password/email/SSO modes), откроется в новой вкладке)Yes: a native Files area with folder hierarchy, link-based sharing (public, password, email OTP, or SSO auth), and a workspace-level Recently Deleted view covering workflows, tables, knowledge bases, files, and folders. Admins can restrict which share-auth modes (public/password/email/SSO) a permission group is allowed to use.
StackAI
(источник: Files Node docs, откроется в новой вкладке)No: the Files node is a per-workflow input for uploading a document as context for the LLM, not a persistent file store. Ongoing file access goes through Knowledge Base connectors to external storage like Google Drive, Dropbox, OneDrive, SharePoint, Box, S3, or Azure Blob. There is no native file system with its own folder hierarchy, link-based sharing, or a trash/recovery feature.. Workspace "folders" that exist in StackAI docs organize projects/permissions, not user files.
Встроенные таблицы данных
AACFlow
(источник: AACFlow Docs: Tables, откроется в новой вкладке)Yes: a native spreadsheet-like Tables feature (typed columns, not an external DB connector) with full keyboard support (arrow keys, Tab, copy-paste bulk load, Cmd/Ctrl+Z undo) and atomic per-row writes from multiple workflows at once. No public fixed row-limit figure is documented (guidance says paginate reads past ~100k rows); a workflow can also be wired to run per row via a "workflow column."
StackAI
(источник: Build Spreadsheet AI Agent blog, откроется в новой вкладке)No: the Table node lets a workflow upload a CSV or XLSX file and query it with LLM-generated SQL, but only as a one-off input to that workflow run. That's different from a persistent, spreadsheet-like data table shared across a workspace, with defined row/column limits and spreadsheet-style keyboard navigation.. There is no standalone "Tables" product surface with persistent grid storage independent of a single workflow run.
Редактор документов с форматированием
AACFlow
(источник: AACFlow codebase: rich markdown editor, откроется в новой вкладке)Yes: markdown files opened in the Files viewer render in an inline WYSIWYG-style rich markdown editor, with inline @-mention links to other AACFlow resources
StackAI
Unknown, not publicly documentedUnknown: StackAI has no documented inline rich-text or WYSIWYG markdown editor for documents stored in the platform. Other search results turned up only unrelated third-party products with similar names.
Подворкфлоу (композиция)
AACFlow
(источник: AACFlow Docs: Workflow block, откроется в новой вкладке)Yes: a Workflow block calls another saved workflow as a step, waits for it to finish, runs its latest deployed version, and maps parent variables into the child's input form. Self-references are blocked to prevent infinite recursion.
StackAI
(источник: Subflow Tools docs, откроется в новой вкладке)Yes: an AI Agent node can invoke a separately saved StackAI workflow as a Subflow Tool. The parent agent passes input into the subflow, waits for it to run to completion, and receives the subflow's output node result back before continuing, rather than only firing an async webhook-triggered run.. Subflow Tools are configured on the AI Agent node; each must connect to an output node to complete, and the docs describe subflows running "collaboratively" where one subflow's output can inform whether/how another is invoked. Whether a Subflow Tool can be reused unmodified across multiple parent workflows is unconfirmed.
Собственные блоки (повторное использование в организации)
AACFlow
(источник: AACFlow codebase: custom-blocks publish API (admin gate), откроется в новой вкладке)Yes: a user with admin access on a workflow's workspace can publish its deployed version as a named, iconed custom block that appears in the block toolbar for the whole organization. Inputs are read live from the source workflow's Start block; the publisher picks which outputs to expose. The block always runs the source workflow's latest deployed version, and its internal steps and intermediate values are never exposed to consumers. Enterprise-gated. Distinct from the Workflow (sub-workflow) block, which composes a workflow the same author controls, and from custom code/tool blocks, which run inline code rather than a full deployed workflow.
StackAI
(источник: StackAI Project Node docs, откроется в новой вкладке)No: StackAI has no feature for publishing a project as a named, iconed block that appears in a shared toolbar/library for every builder in the organization. The closest capability is the StackAI Project Node, which lets a builder reference another saved project by picking it from a dropdown inside their own workflow, and Subflow Tools, which do the same from an AI Agent node.. The Project Node docs describe manually matching the calling workflow's inputs to the target project's inputs (not an auto-derived input schema) and returning results as a single opaque JSON blob covering all of that project's outputs, reshaped afterward with the Output Node's Template feature, rather than the caller picking and naming individual outputs to expose. Neither the Project Node nor Subflow Tools docs describe the referenced project appearing as a distinct block in a shared, org-wide toolbar alongside built-in nodes, hiding its internal steps/credentials from the caller, or automatically tracking a separately published/deployed version of the source project.

Тарифы

CompareAACFlow vs StackAI
AACFlow
StackAI
Модель ценообразования
AACFlow
Credit-based billing, BYOK exempt from caps (источник: AACFlow Pricing, откроется в новой вкладке)Credit-based usage billing (Stripe), with bring-your-own-key exemption from metered caps
StackAI
Начальный платный тариф
StackAI
(источник: StackAI Pricing, откроется в новой вкладке)No self-serve paid tier. Only Free and custom-quote Enterprise. The pricing page shows only "Free" ($0/mo) and "Enterprise" (custom pricing); there is no self-serve paid mid-tier.
Бесплатный уровень
AACFlow
(источник: AACFlow Pricing, откроется в новой вкладке)No free tier: usage is paid for with prepaid wallet credits or a paid plan
StackAI
(источник: StackAI Pricing, откроется в новой вкладке)Yes: 500 runs/month, 2 projects, 1 seat, community Discord support
Собственный ключ (BYOK)
AACFlow
(источник: AACFlow Docs: Bring Your Own Key (BYOK), откроется в новой вкладке)Yes: bring-your-own-key support exempts usage from metered credit caps, and multiple keys stored for the same provider are automatically round-robin rotated, with automatic fallback past any key that fails to decrypt
StackAI
Not documentedUnknown

Безопасность и соответствие требованиям

CompareAACFlow vs StackAI
AACFlow
StackAI
Резидентность данных
AACFlow
Cloud region toggle is global, not per-customer (источник: AACFlow codebase: async job region resolution, откроется в новой вкладке)On AACFlow Cloud, async job execution has an internal US/EU region toggle, but it is deployment-wide, not a customer-selectable per-workspace residency option
StackAI
Not documentedUnknown
Ролевой контроль доступа
AACFlow
(источник: AACFlow Docs: Roles and Permissions, откроется в новой вкладке)Yes: admin/write/read workspace permissions, org-level admin/member roles, plus Enterprise-tier permission groups that allow/deny-list specific models, tools, and integrations per group on top of those base roles. See modelAndToolGovernance and credentialGovernance for the finer-grained permission-groups layer.
StackAI
Enterprise SSO plus least-privilege access (источник: StackAI Pricing, откроется в новой вкладке)Access controls and SSO on the Enterprise plan; least-privilege access to customer data internally. The Enterprise plan includes access control and SSO. The Trust Center states customer-data access is restricted on a least-privilege basis with unique personnel IDs and controlled non-console production access.
Журнал аудита
AACFlow
(источник: AACFlow codebase: auditLog table, откроется в новой вкладке)Yes: dedicated audit_log table plus workflow execution logs, exposed via a public /v1/audit-logs API (Enterprise plan), plus continuous SIEM/warehouse export to Datadog, S3, GCS, Azure Blob, BigQuery, or Snowflake via a data-drains dispatcher
StackAI
(источник: StackAI Platform Overview docs, откроется в новой вкладке)Yes: automatic logs of every run, capturing input/output, token usage, and runtime, queryable through a pull-based Analytics API (filterable by status, user, and date range — no run ID filter parameter is documented). The Analytics API is request/response only: a builder calls it to list flow runs or an org-level run summary, filtered by user_id, state (status), and date range. There is no documented continuous push/export of these logs to an external destination such as S3, BigQuery, Datadog, or a generic webhook sink, and no separate public audit-log API distinct from execution logs.
Дополнительное соответствие требованиям
AACFlow
StackAI
ISO 27001 certified; HIPAA audited, GDPR marketing-only (источник: StackAI Trust Center, откроется в новой вкладке)ISO 27001 certified, and audited against HIPAA in the same review cycle as its SOC 2 Type II audit, though the public Trust Center page itself lists only SOC 2 and ISO 27001, not HIPAA. The Trust Center confirms SOC 2 Type II and ISO 27001, DPAs with OpenAI and Anthropic, and a May 2025 penetration test with a Low risk rating. A separate StackAI blog post states the company "was also audited against HIPAA standards during the same period as the SOC 2 Type II audit." GDPR compliance is referenced on the Enterprise pricing page but has no dedicated audit source.
Управление моделями и инструментами
AACFlow
(источник: AACFlow codebase: PermissionGroupConfig (allowedModelProviders, deniedModels, deniedTools), откроется в новой вкладке)Yes: enterprise "permission groups" let an admin allow-list/deny-list specific LLM providers and models, and separately deny specific tools/integrations (or disable all MCP or custom tools) per group, layered on top of workspace admin/write/read roles. This does not control whether an LLM provider retains prompts. AACFlow offers no "zero data retention" mode or governed AI gateway. A separate, Enterprise-gated feature lets orgs set a log-retention window and redact PII, but that only controls how long AACFlow itself keeps execution logs.
StackAI
Not documentedUnknown
Управление учётными данными
AACFlow
(источник: AACFlow Docs: Roles and Permissions, откроется в новой вкладке)Yes: shared credentials (connected accounts, service accounts, workspace secrets) are their own nested permission level (Member/Admin) below organization and workspace roles, and enterprise permission groups can further allow-list specific integrations and restrict which file-share auth modes (public/password/email/SSO) a group may use. A user's personal environment variables/secrets are never shared or inherited by anyone, including org owners/admins.
StackAI
(источник: Workspace and Folder Access docs, откроется в новой вкладке)Yes: owners and admins can share a connection org-wide or restrict it to specific users or groups, separately from the four-tier role system (Admin, Editor, User, Viewer). StackAI recommends pairing private folders with restricted connections and knowledge bases for sensitive workflows.
Единый вход (SSO)
AACFlow
(источник: AACFlow Docs: Single Sign-On (SSO), откроется в новой вкладке)Yes: SAML 2.0 and OIDC single sign-on, with users routed to SSO by their email domain, plus SCIM 2.0 directory provisioning for Okta, Microsoft Entra ID, OneLogin, and JumpCloud that creates, updates, deactivates, and removes members and maps pushed groups to permission groups, workspace access, and the organization admin role
StackAI
(источник: AI Governance - StackAI Docs, откроется в новой вкладке)Yes: StackAI supports Single Sign-On, integrating with identity providers like Okta and Entra ID to inherit groups and permissions. Newly provisioned SSO users get a default role, and admins can require SSO for all interfaces org-wide.. Docs confirm SSO login and default-role auto-provisioning behavior, enabled per interface or enforced org-wide via admin policy. SSO configuration is distributed across these governance controls rather than a single dedicated SSO settings page, and SAML vs OIDC protocol details are not specified beyond the Okta/Entra ID integration.
Настраиваемая политика сессий
AACFlow
(источник: AACFlow Docs: Session Policies, откроется в новой вкладке)Yes: Enterprise organization owners and admins can set a max session lifetime (1 to 8,760 hours from sign-in, regardless of activity) and an idle timeout (48 to 8,760 hours without activity), applied to every member on every device. Both limits are optional; leaving them empty keeps the default of 30-day sessions that extend automatically while a member stays active. A separate "Sign out all members" action immediately revokes every member session in the organization except the admin's own, for use after a security incident or an offboarding wave. The 48-hour idle floor exists because session activity is recorded at most once per day, so a shorter window could sign out members who are actively working.
StackAI
Not publicly documented (источник: Authentication and MFA - StackAI Docs, откроется в новой вкладке)Not publicly documented: no admin-configurable session lifetime or idle timeout appears in StackAI's public documentation. Its Authentication and MFA page covers password login, org-wide MFA enforcement, and SSO, and the Feature Access page enumerating org-level admin settings (MFA, LLM access, app/tool availability, knowledge bases, advanced features) lists no session control.. Where SSO is used, StackAI states MFA enforcement happens at the identity provider, so an organization's effective re-authentication cadence is inherited from its upstream IdP rather than set in StackAI. No fixed session length is published either, and the Trust Center lists access-control and termination policies without any session-timeout control.
Проверенные собственные интеграции
AACFlow
(источник: AACFlow codebase: first-party block directory, откроется в новой вкладке)Yes: every one of AACFlow's 266 blocks is first-party authored and code-reviewed through the standard pull-request process in the main AACFlow repository; there is no public marketplace where an arbitrary third party can publish and have other users install executable tool code without going through AACFlow's own review. Custom code steps run inside AACFlow's own isolated-vm sandbox rather than as an installable third-party skill package, so the supply-chain trust boundary is AACFlow's codebase review, not an open registry.
StackAI
(источник: StackAI Apps documentation, откроется в новой вкладке)Yes: StackAI's 70+ app integrations (databases, cloud storage, CRMs, communication tools) are built and maintained by StackAI's own team, not an open community marketplace. Users needing an unlisted service fall back to a built-in Custom API node or connect their own MCP servers, rather than installing code published by other third-party users.. There is no public marketplace or community-node registry (like n8n community nodes) where outside developers publish installable integrations for other StackAI users. MCP support lets a workspace point at third-party MCP servers, but that is a user-configured connection to an external server the user chooses, not a shared plugin store with lighter vendor review. No StackAI-specific security incident involving its integrations or MCP connections appears in public sources.
Скрытие персональных данных
AACFlow
(источник: AACFlow Docs: Guardrails Block, откроется в новой вкладке)Yes: a Guardrails workflow block detects and blocks or masks PII (30+ entity types across the US, UK, and several other countries) via Microsoft Presidio, in addition to the org-level data-retention PII policy applied to stored data
StackAI
Guardrail guidance only, not a confirmed built-in feature (источник: AI Agent Guardrails guide, откроется в новой вкладке)Partial: StackAI's guardrails guidance recommends redacting personally identifiable information (PII) in inputs, retrieval, and logs as part of enterprise agent design, but StackAI's own security page does not itself assert a built-in PII detection/masking mechanism.
Настраиваемое хранение данных
AACFlow
(источник: AACFlow Docs: Data Retention, откроется в новой вкладке)Yes: Enterprise orgs can independently configure log retention, soft-deletion cleanup, and Chat/Copilot task cleanup (chats, runs, checkpoints, Inbox tasks) at 1 day to 5 years or Forever, applied org-wide with no per-workspace override
StackAI
(источник: StackAI Trust Center, откроется в новой вкладке)Yes: StackAI's Trust Center and security documentation state the org can configure data retention durations, backed by a documented Data Retention and Disposal Policy, rather than a single fixed platform-wide default.. Public sources describe the policy existing and retention being settable, but exact granularity (per-resource-type controls like execution logs vs soft-deleted items separately) was not independently confirmed.
White-label (собственный бренд)
AACFlow
(источник: AACFlow Docs: Whitelabeling, откроется в новой вкладке)Yes: Enterprise orgs can replace the logo, wordmark, brand name, and primary/accent theme colors across the workspace UI with their own
StackAI
Unknown, not publicly documentedUnknown: StackAI does not document letting customers replace its logo, product name, or theme colors across the workspace or deployed-app UI. Deployed chat interfaces can be styled/branded, but full workspace-level white-labeling is unconfirmed.. Marketing pages reference brand guidelines for StackAI's own brand, and chat widgets can be styled to match a customer's site, but no source confirms full white-label replacement of vendor branding.

Возможности ИИ

CompareAACFlow vs StackAI
AACFlow
StackAI
Поддержка нескольких LLM
AACFlow
21 providers plus dynamic-resolution aggregators (OpenRouter, LiteLLM, etc.) (источник: AACFlow codebase: PROVIDER_DEFINITIONS, откроется в новой вкладке)21 provider integrations (OpenAI, Anthropic, Google/Gemini, Azure OpenAI, Azure Anthropic, Groq, Cerebras, Mistral, xAI, Bedrock, Vertex, Ollama, OpenRouter, and more), with OpenRouter, LiteLLM, vLLM, and Ollama resolving models dynamically at runtime rather than from a fixed list, so effective model reach extends well beyond the 21 named providers. apps/sim/providers/models.ts defines 21 provider entries.
StackAI
(источник: LLMs Hosted on Azure & AWS Bedrock - StackAI Docs, откроется в новой вкладке)Yes, broad support across major LLM providers. The LLM node is provider-agnostic with a model dropdown, and StackAI docs confirm OpenAI models directly and via Azure hosting, plus AWS Bedrock-hosted models including Anthropic Claude, AI21, Cohere, and Amazon Titan. StackAI also documents data processing agreements with OpenAI and Anthropic.
Блоки рассуждений агента
AACFlow
(источник: AACFlow codebase: block registry, откроется в новой вкладке)Yes: dedicated agent, function-calling, RAG, code-execution, and evaluation blocks, not just data routing
StackAI
Not documentedUnknown
Создание на естественном языке
AACFlow
(источник: AACFlow Docs: Copilot, откроется в новой вкладке)Yes: Chat + in-editor AI Copilot can build and modify workflows from natural-language requests
StackAI
Not documentedUnknown
База знаний / RAG
AACFlow
(источник: AACFlow codebase: KB schema (pgvector + tsvector), откроется в новой вкладке)Yes: hybrid vector (pgvector) plus full-text (tsvector) search knowledge base, 11 supported file formats (csv, doc, docx, html, json, md, pdf, pptx, txt, xlsx, yaml), configurable chunking, plus 51 connectors that continuously sync external sources (Google Drive, Confluence, Slack, Gmail, GitHub, HubSpot, Linear, Jira, and more) into the knowledge base rather than a one-shot upload
StackAI
(источник: StackAI Docs Overview, откроется в новой вкладке)Yes: knowledge base / data loader connections. Connects to knowledge bases, tools, and business systems; the Enterprise plan includes all data loaders.
Поддержка MCP
AACFlow
(источник: AACFlow Docs: Using MCP Tools, откроется в новой вкладке)Yes: both MCP client (call external MCP servers) and MCP server (expose AACFlow workflows as MCP tools)
StackAI
(источник: MCP - StackAI Docs, откроется в новой вкладке)Yes: dedicated MCP node. An MCP node lets a workflow call a tool on a Model Context Protocol server, using public servers via URL connection or self-hosted/local MCP servers exposed via a tunnel (e.g. ngrok) for advanced users.
Оценка и ограничители
AACFlow
LLM-judge Evaluator plus Guardrails validation block (источник: AACFlow Docs: Evaluator Block, откроется в новой вкладке)Evaluator block (LLM-judge scoring against user-defined named metrics) and Guardrails block (JSON validity, regex, RAG/hallucination scoring, PII detection/masking). These are per-call scoring/validation primitives, not a batch golden-dataset eval-suite runner or A/B prompt-testing harness.
StackAI
Guardrail guidance, no dedicated product (источник: How to Design AI Agent Guardrails - StackAI insights, откроется в новой вкладке)Retrieval grounding, tool-call validation, and output enforcement are covered in vendor guidance, but there is no dedicated first-party evaluation or guardrails feature.
Участие человека (human-in-the-loop)
AACFlow
(источник: AACFlow Docs: Human in the Loop Block, откроется в новой вкладке)Yes: dedicated approval block that pauses a run and waits for a human-submitted "Resume Form," with durable pause/resume via persisted execution snapshots and notification hooks (e.g. Slack, email) carrying the resume link
StackAI
(источник: Introducing StackAI Human-in-the-Loop - StackAI blog, откроется в новой вкладке)Yes: dedicated pause-and-approve mechanism distinct from a simple delay step. A workflow pauses at a decision point and sends an approval request via Slack, Teams, email, or another connected channel. A human reviewer can approve, reject, or give feedback, and the gated action, such as sending an email, writing to a database, or provisioning access, only executes after approval. This checkpoint reduces the damage a hallucination or a mistaken tool call could cause.
Генеративные медиа
AACFlow
(источник: AACFlow codebase: Image Generator V2, откроется в новой вкладке)Yes: dedicated image (3 provider families incl. OpenAI, Gemini, Fal.ai proxy), video (5 provider families incl. Runway, Veo, Luma, Hailuo, Fal.ai proxy), text-to-speech (7 providers), and speech-to-text (5 providers) blocks
StackAI
(источник: Image Node - StackAI Docs, откроется в новой вкладке)Yes: image and audio generation nodes; no dedicated video generation node. A Text-to-Audio node uses ElevenLabs voice-synthesis models (e.g. eleven_multilingual_v2) for TTS; an Image node generates images from text prompts using models such as OpenAI DALL·E 3 or Stable Diffusion.
Динамическое использование инструментов
AACFlow
(источник: AACFlow codebase: agent tool resolution (pre-wired only), откроется в новой вкладке)No: an Agent block calls tools the workflow author explicitly added to it at build time, rather than browsing and picking from a broader pool (e.g. an entire MCP server catalog) at inference time. Runtime MCP "discovery" exists to resolve/refresh the schema of an already-configured tool. The model does not browse or choose from the server's full tool list.
StackAI
Not documentedUnknown
Автоматическая замена модели
AACFlow
(источник: AACFlow codebase: executeProviderRequest (no retry/fallback), откроется в новой вкладке)No: a failed or rate-limited LLM call is retried using AACFlow's own hosted API keys for the same model, rather than automatically switching to a different model or provider. A "fallback" comment in the provider layer refers to rotating among AACFlow's own hosted API keys for the same model, not switching models.
StackAI
Not documentedUnknown
Навыки агентов
AACFlow
(источник: AACFlow Docs: Agent skills, откроется в новой вкладке)Yes: named, reusable "Agent Skills" (built on the open Agent Skills / SKILL.md format) that agents load on demand via progressive disclosure, editable in-app or imported from a SKILL.md file or GitHub URL. Only the skill name and description sit in the agent's system prompt (~50-100 tokens each); the full instructions load into context only when the agent calls load_skill.
StackAI
(источник: Prompt Library docs, откроется в новой вкладке)Yes, but proprietary and platform-locked: a Prompt Library where builders save and reuse named prompts/instructions (e.g. a saved "Market Analyst Persona") across agents, rather than re-writing a one-off system prompt each time.. Documented as a prompt/instruction library stored inside a StackAI workspace, not an open, portable file format. StackAI's docs do not describe exporting a saved prompt as a standalone file or importing one from an external source or repository URL, the way some competitors build reusable skills on an open, version-controllable format. There is also no documented progressive-disclosure loading mechanism (only a short name/description surfaced until needed); the full prompt appears to load in full whenever it's attached.
Встроенное развёртывание чата
AACFlow
(источник: AACFlow Docs: Chat Deployment, откроется в новой вкладке)Yes: a workflow can be deployed as a public, shareable Chat interface with selectable auth (public, password, email OTP, or SSO), in addition to API and MCP deployment targets
StackAI
(источник: Platform Overview, откроется в новой вкладке)Yes: builders can publish a workflow or agent as a hosted Chat Assistant interface, alongside form, batch run, Slack, Teams, and API deployment targets. A chat widget can also be embedded on external sites via a copy-paste snippet.
Параллельное выполнение
AACFlow
(источник: AACFlow Docs: Parallel block, откроется в новой вкладке)Yes: a native Parallel block fans a run out into concurrent branches (fixed count or one per list item) and joins their results back into the workflow automatically. Contained blocks run concurrently instead of sequentially, either a fixed number of times or once per item in a list/collection, and each branch's output aggregates for downstream blocks.
StackAI
Partial, via parallel tool calls and loop mode (источник: Subflow Tools docs, откроется в новой вкладке)Partial: StackAI's core workflow builder is built around sequential and conditional (If/Else) branching rather than a dedicated deterministic fan-out/fan-in node. Concurrent execution shows up at the AI Agent node level, where the agent can call multiple Subflow Tools in parallel (e.g., checking several independent systems at once) and StackAI Project nodes can run in parallel under loop mode.. There is no standalone "split into parallel paths" or "parallel branches" node in the core logic node set (If/Else, Loop Subflow). Parallelism instead comes from agent-driven concurrent tool calls or parallel sub-project execution inside a loop, a narrower mechanism than a general-purpose fan-out/fan-in workflow node.
Протокол Agent2Agent (A2A)
AACFlow
(источник: AACFlow Docs: A2A integration, откроется в новой вкладке)Yes: a dedicated A2A block sends messages to, tracks and cancels tasks on, and discovers the capabilities of any Agent2Agent (A2A)-compliant external agent via its Agent Card
StackAI
(источник: StackAI documentation, откроется в новой вкладке)No: StackAI's documentation, changelog, and blog do not mention the Agent2Agent (A2A) protocol or Agent Cards.. StackAI documents MCP-style tool integration and Subflow Tools/StackAI Project nodes for composing agents, but nothing references the A2A open standard.
Блок цикла / итерации
AACFlow
(источник: AACFlow Docs: Loop block, откроется в новой вкладке)Yes: a Loop container block runs the blocks inside it repeatedly (For a fixed count, ForEach over a collection, While a condition holds, or Do-While), running iterations one after another; concurrent fan-out is a separate Parallel block
StackAI
(источник: Loop Subflow docs, откроется в новой вкладке)Yes: the Loop Subflow node iterates over a list of inputs, running its Loop branch once per item sequentially, with the current item exposed via a current_item variable. A separate Done branch runs once after all iterations finish, collecting any outputs explicitly emitted inside the loop.. This is a sequential, one-item-at-a-time iterator distinct from the parallel/concurrent execution StackAI exposes separately through parallel Subflow Tool calls or parallel Project runs inside a loop.

Интеграции

CompareAACFlow vs StackAI
AACFlow
StackAI
Интеграции
AACFlow
1,000+ integrations (266 blocks, ~3,900 tool actions) (источник: AACFlow codebase: BLOCK_REGISTRY, откроется в новой вкладке)1,000+ integrations counting individual API actions, built from 266 first-party blocks and roughly 3,900 underlying tool actions. AACFlow's landing page cites the "1,000+ integrations" figure; the block/tool-action counts are the same integration surface measured at a different level of granularity.
StackAI
70+ integrations (источник: StackAI Apps documentation (70+ apps and services), откроется в новой вкладке)70+ enterprise integrations. StackAI documentation states it connects to 70+ apps and services, including Notion, Airtable, AWS, BigQuery, GitHub, Google Workspace, HubSpot, MongoDB, and MCP. Some marketing pages cite a higher "100+" figure, but the documented apps list supports 70+.
Типы триггеров
AACFlow
Webhook, cron, chat, REST API, 61 app triggers, plus a diff-aware Tables trigger (источник: AACFlow Docs: Triggers overview, откроется в новой вкладке)Webhook, schedule/cron, chat, REST API, and event-based triggers for 61 apps (Slack, Gmail, GitHub, Stripe, etc.), plus a native row-level trigger on AACFlow Tables that fires on insert/update with an optional column watch-list and emits a before/after diff
StackAI
Scheduled triggers, outbound webhooks only (источник: Scheduled AI Workflows - StackAI insights, откроется в новой вкладке)Scheduled/time-based triggers and outbound webhook calls (e.g., to Make); no native inbound webhook trigger node. Supports scheduled workflows (daily/weekly/monthly automation) and a Make node that can POST to trigger a Make.com scenario. Deployment surfaces (how a finished workflow is exposed, distinct from triggers) include Form, Chat Assistant, API, Website Chatbot, Batch Run, Slack App, and Microsoft Teams.
Шаги с собственным кодом
StackAI
(источник: Python Code - StackAI Docs, откроется в новой вкладке)Yes: Python code node (being migrated to a newer Code Node). A Python Code node allows custom logic within workflows; StackAI's docs now note this node is deprecated in favor of a newer Code Node.
Настраиваемые песочницы для кода
AACFlow
(источник: AACFlow Docs: Function block - Sandboxes, откроется в новой вкладке)Yes: named sandboxes a workspace maintains and a Function block selects, each declaring a language, its pip or npm dependencies, optional Debian/APT system packages, and optional managed CLI tools from a catalog grouped by cloud, Kubernetes, infrastructure, deployment, data and storage, and security tools; on aacflow.io they require an active Max or Enterprise plan. Only workspace admins can create or edit sandboxes, in Settings → Sandboxes. On aacflow.io each specification is prebuilt into a reusable image so runs pay no install cost. Two sandboxes with the same language, dependency list, system packages, and managed CLI tools share one build. Every managed CLI entry uses a pinned, integrity-checked vendor artifact. With no sandbox selected, remote code runs on the dedicated Function base; import-free JavaScript stays in the local isolated runtime and ignores the sandbox selection either way.
StackAI
Packages only: per-node dependencies on the Code Node (источник: Code Node (Dependencies field) - StackAI Docs, откроется в новой вкладке)Partial: at the package layer only. The newer Code Node exposes a Dependencies field, documented as "a list of package names that get installed into the sandbox before your code runs," so builders declare third-party Python/TypeScript packages per node. The deprecated Python Code node it replaces is a fixed image restricted to a vendor-curated set of pre-imported libraries.. Code Node runs in an isolated sandbox with /home/user as the working directory; StackAI's docs contrast it with the Python Code node as "Allows importing custom libraries" versus "Restricted to pre-imported libraries." That older node's fixed set covers pandas, numpy, requests, BeautifulSoup, matplotlib, plotly, pypdf, sklearn, tiktoken, and weaviate among others, with no way to add to it. Dependency declaration is package-level only — StackAI does not document declaring OS-level system packages or preinstalled CLI binaries, and dependencies add sandbox startup time. Sandbox isolation is provided by E2B, which runs each execution as an isolated VM.
Развёртывание через API
AACFlow
(источник: AACFlow Docs: API Reference - Getting Started, откроется в новой вкладке)Yes: a public REST API (mostly under /api/, with /api/v1 reserved for logs and audit-log endpoints) supporting API-triggered workflow execution and deployment rollback. The API reference does not document SSE streaming, a resumable event buffer, a dedicated API-trigger block, or a chat-deployment surface as part of the REST API itself.
StackAI
(источник: API - StackAI Docs, откроется в новой вкладке)Yes: workflows publishable as a REST API with generated client snippets. Any flow can be exported and published as an API. Docs provide request snippets in Python, JavaScript, and cURL, authenticated via a Bearer token using a public API key generated in Settings → API Keys.
SDK и расширяемость
AACFlow
Official Python and TypeScript SDKs, plus MCP, code block, and A2A protocol (источник: AACFlow Docs: Python SDK, откроется в новой вкладке)Official client SDKs exist for Python (aacflow-sdk, pip install aacflow-sdk) and TypeScript/JavaScript (aacflow-sdk, npm install aacflow-sdk), both wrapping the REST API (x-api-key header) with methods like execute_workflow / executeWorkflow, retry-with-backoff, and usage-limit lookups. Extensibility is further extended by MCP (client + server), a sandboxed code-execution block (JS/Python), custom tools, and an Agent-to-Agent (A2A) protocol block for external agent interop
StackAI
Code snippets only, no SDK or marketplace (источник: API - StackAI Docs, откроется в новой вкладке)Client code snippets only (Python, JavaScript, cURL); no installable SDK package, plugin/custom-node dev kit, or community integration marketplace. Docs provide request snippets for calling a published flow's API, but there's no distributable SDK package, documented custom-node/plugin SDK, or marketplace of community-built integrations.
Публикация как MCP-сервер
AACFlow
(источник: AACFlow Docs: MCP Deployment, откроется в новой вкладке)Yes: any deployed workflow can be published as a tool on an MCP server (private, API-key protected, or public/no-auth), with ready-to-paste client config generated for Codex, Cursor, Claude Code, Claude Desktop, and VS Code
StackAI
(источник: StackAI MCP Server docs, откроется в новой вкладке)Yes: StackAI provides a hosted MCP server (mcp.stack.ai/mcp) that lets external MCP-compatible clients, such as Claude Desktop, Claude Code, or Cursor, run a published StackAI workflow as a callable MCP tool, passing inputs in and getting structured results back.

Наблюдаемость и надёжность

CompareAACFlow vs StackAI
AACFlow
StackAI
Трассировка и наблюдаемость
AACFlow
(источник: AACFlow codebase: TraceSpan tree builder, откроется в новой вкладке)Yes: execution logs include a per-block/per-span trace view (duration, cost, token counts, and latency stats like TTFT/TPS) with expandable nested iteration groups, plus a "View Snapshot" frozen copy of the workflow structure and block states at run time for debugging. This trace view is built directly into AACFlow rather than a raw export browsable in an external tool like Jaeger, and does not expose aggregate latency-percentile charts (p50/p95/p99). The run snapshot serves as a log-detail/debugging artifact rather than a resumable mid-run checkpoint.
StackAI
Usage dashboard and run logs, no span tracing (источник: StackAI Platform Overview docs, откроется в новой вкладке)Analytics dashboard with usage graphs and per-run execution logs; no dedicated span-level distributed tracing UI. An Analytics section shows workflow usage graphs and a full list of execution logs (input/output, token usage, runtime performance). There is no granular per-step span tracing for individual tool-call/LLM-call spans within a run.
Надёжность и повторные попытки
AACFlow
Tool-call retries (up to 10x); single-attempt job orchestration (источник: AACFlow codebase: ToolRetryConfig (exponential backoff), откроется в новой вкладке)Individual tool/API calls have configurable exponential-backoff retry (up to 10 attempts). The background job-orchestration layer itself retries only once by design. Durability instead comes from consecutive-failure tracking on schedules and the human-in-the-loop snapshot pause/resume mechanism. AACFlow does not offer guaranteed-once-only block execution, a failed-run holding queue for manual recovery, or a "replay a past execution with its original inputs" feature. The per-execution debugging snapshot serves as a log-detail artifact rather than a resumable mid-run checkpoint.
StackAI
Not documentedUnknown. No documented automatic retries, checkpointing, or replay of past executions with original inputs.
Оповещения о сбоях
AACFlow
(источник: AACFlow Docs: AACFlow Workspace Events trigger, откроется в новой вкладке)Yes: a sim_workspace_event trigger fires on run success/failure, deployments, and cost/latency spikes, wired to any notification block (Slack, email, webhook) for real-time alerting
StackAI
No proactive alerting documentedUnknown. No documented proactive failure or threshold alerting; only after-the-fact execution logs are available.
Выгрузка данных (data drains)
AACFlow
(источник: AACFlow Docs: Data Drains, откроется в новой вкладке)Yes: Enterprise orgs can continuously export workflow logs, job logs, or audit logs on a schedule to a customer-owned S3 bucket, GCS bucket, Azure Blob container, BigQuery table, Snowflake table, Datadog logs intake, or an HTTPS webhook. Each drain exports exactly one data source; multiple drains are created to export multiple sources. Viewing drain config/run history is restricted to org owners/admins.
StackAI
Unknown, not publicly documentedUnknown: StackAI does not document continuous export of execution, audit, or usage data to an external destination such as S3, BigQuery, Datadog, or a generic webhook sink. Only per-run API access and project export/import are documented.. Docs cover an API export view (calling a flow via POST) and project export/import, which are pull/one-shot mechanisms, not a continuous log-drain feature.
Асинхронное выполнение
AACFlow
(источник: AACFlow codebase: async execution handler, откроется в новой вкладке)Yes: a workflow can be triggered in fire-and-forget async mode, returning HTTP 202 with an execution ID immediately, then polled through the canonical execution resource across queued/running/terminal states. Async runs are tracked by execution ID through the same execution status endpoint used for durable logs rather than a separate queue-job resource.
StackAI
Partial: run status queryable later, no documented async API (источник: StackAI API Reference: Run Flow, откроется в новой вкладке)Partial: StackAI's Analytics API can list and filter runs by ID and by status (including pending, paused, resumed, completed, failed, and cancelled) after the fact, which supports a trigger-then-check-later pattern. But StackAI's docs don't describe an official async-trigger-plus-poll workflow for actually running a flow, the way some platforms document a job-queue API.. The API used to run a flow only documents a request/response call that waits for the result, with no explicit async job or webhook pattern. The separate Analytics API exposes a run ID and status field, including a pending state, that can be queried after submission, showing a run's status can be checked later, though this is inferred from the analytics endpoint rather than a documented async execution feature.
Лимиты выполнения
AACFlow
5-50 min sync; async defaults to 90 min, Enterprise up to 7 days (источник: AACFlow Docs: Concurrent Executions, откроется в новой вкладке)Plan-gated: synchronous runs time out at 5 minutes on accounts without a plan and 50 minutes on paid plans; async runs default to 90 minutes, while Enterprise policy can be configured up to 7 days; concurrency ranges from 10 to 1,000 executions per billing account depending on plan. A direct async API request can shorten the account policy for that request, but cannot extend it. Concurrency limits are published in the platform cost docs, where the Enterprise limit is 1,000 by default and customizable. A run that exceeds its time limit is terminated and marked failed with a timeout error.
StackAI
500 runs/month on Free tier; no published timeout/concurrency (источник: StackAI Pricing (500 runs/month on Free plan), откроется в новой вкладке)Partial: the only concrete published number is a usage quota, not a timeout or concurrency limit. The Free plan caps usage at 500 runs per month (2 projects, 1 seat), while Enterprise plans get custom or unlimited run allowances. No public documentation states a maximum single-execution duration or a cap on concurrent executions.. Neither the pricing page nor the API reference pages disclose a per-request timeout or a concurrent-execution cap. This is a gap in StackAI's public documentation, not a confirmed absence of limits.
Обработка частичных сбоев
AACFlow
(источник: AACFlow codebase: error-output edge routing, откроется в новой вкладке)Yes: any block can be wired to a dedicated error-output edge, so a failing step routes execution down an error-handling branch instead of always halting the entire run
StackAI
(источник: StackAI: Handling Errors & Fallback (Fallback Branch, Retry on Failure), откроется в новой вкладке)Yes: each node can have a 'Fallback Branch' (On Error) that, when enabled, lets the workflow keep going after that node fails instead of halting the whole run. It routes execution to an alternate path, such as returning a safe message, emitting a structured error, or notifying a human.. StackAI's documentation also describes a complementary 'Retry on Failure' setting (configurable max retries and retry interval) and an LLM Fallback Mode, and recommends layering them: retries first, then an LLM fallback, then the fallback branch.
Выполнение без присмотра
AACFlow
(источник: AACFlow codebase: trigger.dev background job backend, откроется в новой вкладке)Yes: scheduled, webhook, and chat-triggered runs execute as background jobs on trigger.dev workers, entirely on AACFlow's servers. No client device needs to be open, awake, or connected for a run to fire or complete; closing the browser tab or shutting down a laptop has no effect on a scheduled or triggered workflow.
StackAI
(источник: StackAI Self-Hosted Solutions page, откроется в новой вкладке)Yes: StackAI is a multi-tenant cloud SaaS (each org gets an isolated partition on shared AWS/Azure/GCP infrastructure, or a dedicated VPC/on-prem deployment on Enterprise), and scheduled, chat, form, Slack, Teams, and API-triggered runs are executed and logged (runtime, tokens, input/output) through that hosted infrastructure rather than a builder's own machine.. StackAI's docs do not use explicit language like 'no client device dependency' the way some competitors do; this is inferred from its documented deployment model (cloud SaaS or Enterprise VPC/on-prem) and its scheduled-workflow and execution-log features, none of which describe or require a desktop app, browser tab, or local session staying open.

Поддержка

CompareAACFlow vs StackAI
AACFlow
StackAI
Каналы поддержки
AACFlow
Community support plus Enterprise 'Dedicated Support' (источник: AACFlow Pricing Page, откроется в новой вкладке)Community plus an unquantified "Dedicated Support" flag on the Enterprise plan. Enterprise and pricing pages do not include CSM, onboarding/enablement, or professional-services details beyond a plan-comparison-table "Dedicated Support" flag.
SLA
AACFlow
(источник: AACFlow Pricing Page, откроется в новой вкладке)Yes: the Enterprise plan includes a "Dedicated Support" feature per the pricing plan-comparison table; no SLA terminology, response-time, or uptime figures are published on the enterprise or pricing pages
StackAI
Enterprise SLAs, terms undisclosedEnterprise plans include support SLAs; exact terms are not publicly documented
Сообщество
StackAI
Discord, docs, and StackAI Academy (источник: StackAI Academy, откроется в новой вкладке)StackAI community support (Discord, at discord.gg/sSbwawtNsV, not linked from stackai.com/academy), comprehensive docs at docs.stackai.com, and a StackAI Academy with tutorials and courses
Академия / обучение
AACFlow
(источник: AACFlow Docs: Academy, откроется в новой вкладке)Yes: AACFlow Academy is a dedicated structured-learning section of the docs site, separate from reference documentation and the API reference
StackAI
(источник: StackAI Academy docs, откроется в новой вкладке)Yes: StackAI runs a structured StackAI Academy with step-by-step lessons and courses covering platform overview, building workflows, knowledge bases, and agent building, plus a separate enterprise offering for AI-driven skills testing and certification.. Academy is lesson-based across multiple courses. Certification is a separate enterprise offering (skills testing and certification), not confirmed to be bundled into the core Academy itself.

Сильные стороны AACFlow

AI Copilot / Chat agent-building surface (источник: AACFlow Docs: Copilot, откроется в новой вкладке)

Chat builds and manages work across the workspace; in-editor Copilot edits a single workflow.

A workspace-wide natural-language surface (Chat) that can build workflows, manage data, and take actions across integrations, plus an in-editor Copilot scoped to building and editing a single workflow directly.

Hybrid semantic + keyword knowledge base (источник: AACFlow codebase: KB schema + file-parsers, откроется в новой вкладке)

Combines vector and full-text search with configurable chunking across 11 file formats.

Built-in RAG with pgvector embeddings and a generated tsvector column for combined vector + full-text search, plus a token-based chunker with configurable chunk size/overlap and 11 supported file formats (csv, doc, docx, html, json, md, pdf, pptx, txt, xlsx, yaml).

Native MCP client and server (источник: AACFlow Docs: MCP Deployment, откроется в новой вкладке)

Call external MCP servers as tools, or expose AACFlow workflows as an MCP server.

A dedicated MCP block lets any workflow call external MCP servers as a tool, and a serve/workflow-servers API surface lets AACFlow expose its own workflows as MCP servers.

Fork a workspace into dev, qa, and prod environments (источник: AACFlow Docs: Workspace Forks, откроется в новой вкладке)

Fork, diff, and promote environments with mandatory credential remapping.

Fork a whole workspace into a dev/qa/prod-style child environment, preview a diff, and promote changes bidirectionally. Credential and env-var remapping is required on every promote, so secrets never cross environments silently.

Human-in-the-loop approvals with durable resume (источник: AACFlow Docs: Human in the Loop Block, откроется в новой вкладке)

Pause a run for human approval and resume later via a durable snapshot link.

A dedicated block pauses a run and waits for a human-submitted approval form, backed by persisted execution snapshots so the run can resume later via a link, even after a server restart.

Live multiplayer canvas editing (источник: AACFlow codebase: realtime presence handler, откроется в новой вкладке)

Real-time cursors, selections, and synced edits on the same canvas.

Real-time cursors, selection broadcasting, and synced concurrent edits over a dedicated realtime backend, so a team can build the same workflow together at the same time.

Задокументированные ограничения StackAI

Not open source (источник: StackAI GitHub organization, откроется в новой вкладке)

Closed-source SaaS with no auditable or forkable codebase.

StackAI is a proprietary, closed-source commercial SaaS platform. Its GitHub organization (github.com/stackai) currently has no public repositories at all, so there is no self-hostable OSS codebase to audit or fork.

Free tier is very limited (источник: StackAI Pricing, откроется в новой вкладке)

Free plan caps at 500 runs, 2 projects, 1 seat.

The free plan caps usage at 500 runs/month, 2 projects, and 1 seat, with support limited to community Discord, well below what a team evaluating agent workflows at scale would need.

No published self-serve/mid-tier pricing (источник: StackAI Pricing, откроется в новой вкладке)

No mid-tier pricing. Only free or a custom Enterprise quote.

Beyond the free tier, StackAI publishes only a custom-quote Enterprise plan with no mid-market tier, so cost comparison requires contacting sales.

HIPAA/GDPR not documented on the Trust Center itself (источник: StackAI Trust Center, откроется в новой вкладке)

HIPAA is audited but GDPR compliance is undocumented outside marketing pages.

The public Trust Center page lists only SOC 2 Type II and ISO 27001. A separate blog post confirms StackAI was also audited against HIPAA, but GDPR compliance appears only on marketing/pricing pages (e.g. "SOC 2, HIPAA & GDPR compliance" on the Enterprise tier), with no dedicated audit evidence.

Итог

Choose AACFlow if you want an AI workspace that treats AI agents as first-class citizens: native multi-LLM support, real-time multiplayer editing, environment promotion (dev/qa/prod), human-in-the-loop approvals, and enterprise governance (SSO, credential-level permissions, audit logs) built in rather than bolted on.

Choose StackAI if you specifically need ISO 27001 certified, with a public Trust Center detailing pen tests and DPAs: StackAI publishes a Trust Center (trust.stackai.com) documenting ISO 27001 certification, third-party penetration test results, and DPAs with OpenAI and Anthropic. StackAI also holds a SOC 2 Type II audit, but so does AACFlow, so ISO 27001 is the actual point of difference here.

Часто задаваемые вопросы

AACFlow is an AI workspace where teams build, deploy, and manage AI agents visually, conversationally, or with code. StackAI is a proprietary, enterprise-focused visual platform for building, deploying, and governing AI agents, connecting LLMs and business systems through a drag-and-drop, low-code node builder. Teams considering a switch typically weigh pricing model, and how AI-native the platform's agent-building experience is.

Каждый агент вашей компании. Всё в одном месте.