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Atlas by Comet Lab
Capability layer

Workflow and automation tools, compared

Workflow engines run multi-step processes across your systems, with a model as one decision step among many. The choice turns on who builds the workflow, where the data is allowed to go, and how long each run lasts.

Learn the idea Build vs. buy AI: the assembly tier on Horizon

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At a glance

Every tool in this layer, with where it fits and the facts that usually decide it.

Tool Where it fits Licence Self-hostable Hosted API Maturity
n8n Visual workflow engine Best when a process spans many SaaS tools and you want it self-hosted inside your own infrastructure, with AI as one decision step. Source available Yes Yes Mature
Activepieces Visual workflow engine A fit when you want a genuinely open-source, self-hosted, no-code automation tool with a permissive licence. Open source Yes Yes Growing
Apache Airflow Data pipeline orchestration The default when scheduled, dependency-aware data pipelines feed an AI system and a data team already knows the tool. Open source Yes Yes Mature
Dify LLM app platform A fit when a team wants to build and ship LLM apps quickly on one self-hosted platform, without wiring the stack together themselves. Open source Yes Yes Established
Inngest Durable execution A fit when developers want durable, event-driven background jobs without standing up a queue and a workflow cluster themselves. Open source Yes Yes Growing
Make Hosted no-code automation A good fit when an automation needs real branching and data shaping but the team still wants a visual, no-code tool. Proprietary No Yes Mature
Prefect Data pipeline orchestration A good fit for Python data teams who want orchestration that feels like ordinary code rather than a separate framework. Open source Yes Yes Established
Temporal Durable execution The right backbone when AI workflows are long-running, span external waits, and absolutely must not lose state when something restarts. Open source Yes Yes Mature
Trigger.dev Background jobs A strong fit for TypeScript teams running long AI jobs (model calls, pipelines) that outlast a normal serverless request. Source available Yes Yes Growing
Windmill Script-to-workflow platform A fit when a team has a pile of useful scripts and wants them scheduled, shareable, and wrapped in light UIs without building a platform. Source available Yes Yes Growing
Zapier Hosted no-code automation Best for getting a cross-app automation working immediately, especially for teams with no engineering capacity to spare. Proprietary No Yes Mature
Flowise Not reviewed yet. Open source Yes
Langflow Not reviewed yet. Open source Yes

How to choose

Start from your situation. Where a line says “our”, it comes from Comet Lab’s own builds.

  1. A no-code team, and a hosted service is acceptable

    Zapier gets a cross-app automation working fastest; Make handles more branching and data shaping. Both cost more as volume grows.

  2. The workflow must run inside your own infrastructure

    n8n is where we start when a client wants to own and edit the workflow later; Activepieces is the fully open-source option, with a permissive licence.

  3. Runs are long and must survive failure

    Temporal is our pick when a workflow has to survive real-world failure. Inngest and Trigger.dev give developers durable background jobs written as code.

  4. Scheduled data pipelines feed the AI system

    Airflow is the default where a data team already knows it; Prefect suits Python teams who want orchestration that feels like ordinary code.

  5. An LLM app on one platform, without wiring the stack

    Build and ship LLM apps on a visual canvas. It gets constraining once you need fine control over each layer.

  6. A pile of useful scripts

    Turns them into scheduled, shareable workflows with light UIs, without building a platform.