Researched
This analysis is based on documentation, public user reports, and vendor materials — not yet on our own hands-on testing. How we rate
The affordable tier disappeared
For about six months, CrewAI had a sensible self-serve ladder: free for prototyping, $25 a month for small teams shipping something real, Enterprise above that.
The middle rung is gone. It was withdrawn in spring 2026, and what's left is a free tier capped at 50 workflow executions a month and a custom-quoted Enterprise plan with no published pricing.
That's a meaningful change for exactly one group of people: small teams with a working agent system that need slightly more headroom than free allows. They now have no product to buy. They either stay under the cap, self-host the open-source framework and build their own operational tooling, or enter an enterprise sales conversation they're too small for.
If you're comparing pricing pages right now, be aware that several third-party trackers still list the $25 plan. It's not there.
What free actually covers
Two separate free things, and it's worth keeping them apart.
The framework is MIT-licensed, has no usage caps of any kind, and is free forever in the way open-source software is free. You install it, write Python, run agents. Your only cost is model tokens.
AMP Basic is the managed platform's free tier. It includes the visual Studio editor, GitHub integration, unlimited deployments, and 50 workflow executions per month.
An execution is one run of a workflow — not one agent, not one tool call. A crew of five agents working through three tasks counts as one. That makes 50 more usable for development than it sounds, because iterating on a workflow burns them slowly.
It stops being usable the moment anything is scheduled. An agent running twice daily hits the ceiling around day 25. There's no option to purchase additional executions on the free tier; you wait for the reset or you talk to sales.
The bill that isn't on the pricing page
Platform pricing is a distraction here. Your actual CrewAI cost is model tokens.
Multi-agent architectures are expensive by construction. Agents pass context to each other, which means the same information gets re-sent repeatedly. They reason in steps. They retry when a tool call fails. A workflow that appears free on the platform side can produce a serious monthly LLM invoice at production volume.
The practical implication is straightforward and worth acting on: develop against cheap models. Get the orchestration logic right using the least expensive model that can complete the task, and only promote specific agents to frontier models where output quality demonstrably requires it. Teams that build everything against a top-tier model from day one tend to get an unpleasant surprise in week three.
When you eventually do enter an Enterprise conversation, walk in with measured execution volumes and measured token spend from a real pilot. That's a much stronger position than describing what you think you'll need.
Managed or self-hosted
Enterprise customers choose between two deployment shapes.
AMP is fully managed SaaS. CrewAI runs the infrastructure, you run the agents.
Factory is a containerized self-hosted option that deploys on-premises or into your own private cloud. It exists because a significant class of buyer cannot let agent workflows — and the business context those workflows consume — leave their environment.
If you're in regulated industry or handling data with residency requirements, Factory is the reason CrewAI is on your shortlist at all. If you're not, AMP is less work.
How we researched this
We researched CrewAI rather than deploying it in production, and the distinction matters more than usual here because the pricing landscape is unusually noisy.
The approach: check CrewAI's current published plan structure and open-source documentation, then cross-reference against independent analyses published between March and August 2026. Several of those verified their figures directly against the vendor's live pricing page — one in early July, another at the end of July — and both confirm the two-tier structure. Meanwhile other pages published in the same window still describe the removed Professional tier as current.
We've gone with the sources that state when and against what they verified. Where the company publishes adoption figures, we've flagged them as company-reported rather than presenting them as independently confirmed.
What research can't establish: how CrewAI's abstractions hold up on a genuinely messy real-world problem, how good the tracing is when a crew misbehaves in production, or whether the visual Studio is pleasant to work in after a week. Those need hands-on time we haven't put in.
Where it doesn't fit
You want a paid self-serve plan. There isn't one anymore. Free or Enterprise.
Nobody writes Python. The framework is Python-first and the Studio doesn't change that for anything non-trivial. Teams without Python capability should look at Lindy AI or Make instead.
Your problem isn't multi-agent. A great many tasks that get described as agent problems are actually one good prompt and a couple of API calls. Multi-agent orchestration adds latency, cost and failure modes. Reach for it when a single agent has genuinely failed, not before.
You need predictable spend. Between opaque Enterprise pricing and variable token consumption, forecasting a CrewAI bill is harder than forecasting most SaaS.
Against the alternatives
Against LangGraph: LangGraph gives you finer control over the execution graph and expects you to build more of it. CrewAI hands you role-based crews and task flows as first-class concepts. If your problem is naturally a team of specialists, CrewAI's structure is a gift. If it's an arbitrary state machine, LangGraph's flexibility wins.
Against n8n: n8n is visual workflow automation with AI nodes bolted on capably. CrewAI is agent orchestration with automation around it. For "when X happens, do Y," n8n. For "figure out how to accomplish Z," CrewAI.
Against Make: not competitors so much as neighbors. Make connects apps visually for non-developers. CrewAI orchestrates reasoning agents in code. Teams building agent systems sometimes use Make as the connective tissue that triggers them.
Against Lindy AI: Lindy is the no-code answer to a similar question. You describe an outcome and it assembles the agent. Far less control, far less setup. The right choice when you don't have or don't want engineering time.
Against Manus AI: Manus is a general-purpose autonomous agent product rather than a framework for building your own. Different layer of the stack entirely.
Pricing 2026
| Option | Cost | What you get |
|---|
| Open-source framework | Free (MIT) | No usage caps, full control, self-managed |
| AMP Basic | Free | Studio editor, GitHub integration, unlimited deploys, 50 executions/mo |
| AMP Enterprise | Custom quote | Governance, compliance, SSO, dedicated support, flexible overage |
| CrewAI Factory | Custom quote | Containerized self-hosted deployment, on-prem or private cloud |
| LLM tokens | Separate, and usually larger | Paid to your model provider, not to CrewAI |
Checked August 2026. CrewAI removed its $25/month Professional tier (100 executions, per-execution overage) in spring 2026 after launching it with AMP in October 2025; several third-party pricing pages still list it. Enterprise pricing is not published and requires a quote. Model token costs are separate and typically exceed platform fees at production scale. Verify current plans on CrewAI's pricing page.
Pilot on open-source plus AMP Basic. Together they let you validate a real use case end to end at zero software cost. Do that before any sales conversation.
Instrument token spend from day one. It's the number that will actually determine whether this is affordable.
Develop against cheap models. Promote to frontier models per-agent, only where quality requires it.
Bring measured volumes to the Enterprise call. A pilot with real execution counts negotiates far better than an estimate.
Our Verdict
CrewAI remains the fastest credible path from a multi-agent idea to something deployed and observable — provided you have Python engineers. The framework's opinions about how agents should be organized are good opinions, and they save real time compared with assembling orchestration from parts.
The pricing became binary this year. Free framework, free 50-execution tier, or a custom enterprise quote. The self-serve middle that served small teams shipping real workloads no longer exists, and that's a genuine loss for exactly the audience that was best served by it.
The platform fee is not your cost. Model tokens are. Any budgeting exercise that starts with CrewAI's plans rather than with projected token consumption is starting in the wrong place.
The open-source traction is real and independently visible, the community is active, and the company's enterprise adoption claims — while vendor-reported and worth normal skepticism — are consistent with what's happening on GitHub. This isn't a project at risk of disappearing.
For a Python team with a problem that genuinely wants multiple cooperating agents: start with the open-source framework today, at no cost, and find out whether the abstractions fit. For everyone else — teams without Python, or with problems a single good prompt would solve — this is more machinery than the job requires.
Note: AIVario earns no commission from CrewAI. This page is based on published documentation and independent research rather than a production deployment.
Best for: Python engineering teams, developers building multi-agent systems, organizations needing self-hosted agent infrastructure, technical teams prototyping agent workflows
Not ideal for: Non-technical teams, small teams needing a paid self-serve tier, simple single-agent tasks, anyone requiring predictable monthly spend
Bottom line: An excellent free framework with a genuinely useful free managed tier, and nothing in between that and enterprise. Start with open-source — the pilot costs you nothing but tokens.
- n8n — visual workflow automation with capable AI nodes, self-hostable
- Make — app-to-app automation that pairs well as connective tissue around agents
- Lindy AI — the no-code route to agent workflows
- Manus AI — a general-purpose autonomous agent rather than a framework
- Botpress — the right tool when the agent is a conversational interface
- Zapier — simpler integration layer for triggering agent workflows
Frequently Asked Questions about CrewAI
What does CrewAI cost now?
Two options, and neither is a normal subscription. The open-source framework is MIT-licensed and free with no usage caps — you install it, you run it, you pay only for the model calls it makes. The managed AMP platform has a free Basic tier that includes the visual Studio, GitHub integration, unlimited deployments and 50 workflow executions per month. Above that, it's Enterprise on a custom quote. The $25 Professional plan that many comparison sites still list was removed in spring 2026.
Wasn't there a $25 Professional plan?
There was, from the AMP launch in October 2025 until it was withdrawn in spring 2026. It carried 100 executions a month with overage pricing and a second seat. A number of third-party pricing trackers still list it, which is worth knowing if you're comparing sources — several pages published as recently as this year describe a tier you can no longer buy. Treat anything other than the vendor's current page or a written quote as potentially stale.
What counts as an execution?
One run of a workflow, not one agent action or one API call. A crew with five agents completing three tasks is a single execution, not fifteen. That makes the 50-per-month free allowance more generous than it first sounds for development work, where you're testing a handful of workflows repeatedly. It becomes restrictive quickly for anything scheduled or user-triggered — an agent that runs twice a day exhausts the free tier before the month ends.
What will this actually cost me?
Model tokens, overwhelmingly. Multi-agent systems are token-hungry by design — agents pass context between each other, reason through steps, and often retry. A workflow that costs nothing in platform fees can produce a substantial monthly LLM bill at production volume. The practical advice during development is to run cheap models and switch to frontier ones only where output quality demonstrably requires it. Budget for tokens first and platform fees second, because at scale that's the correct order of magnitude.
AMP or Factory?
Two deployment paths for Enterprise customers. AMP is the fully managed SaaS platform — CrewAI runs the infrastructure. Factory is a containerized self-hosted option that runs on-premises or inside your own cloud. Factory exists for organizations whose data governance rules won't allow agent workflows and their context to leave their environment. If you have those constraints, that's the conversation to have with their sales team.
How does CrewAI compare to LangGraph or AutoGen?
CrewAI's differentiator is opinionated structure. It models work as crews of role-defined agents collaborating on tasks, plus event-driven flows for cases needing tighter control. That gives you sensible defaults and a fast path to something working. Frameworks like LangGraph offer finer-grained control over execution graphs at the cost of writing more of the plumbing yourself. If your problem maps naturally onto 'a team of specialists working together,' CrewAI's abstractions save real time. If it doesn't, they'll feel like constraints.
Is it actually used in production?
The adoption figures the company publishes are substantial — over fifty thousand GitHub stars, billions of agent executions across the past year, and claimed penetration across a large share of the Fortune 500 as of their May 2026 disclosures. Vendor-reported numbers deserve normal skepticism, but the open-source traction is independently visible on GitHub and the community around it is genuinely active. This is not an early-stage project.
