The framework fee isn't the bill — it's a surcharge on the bill. CrewAI Cloud bills mostly by run volume in step tiers; LangGraph Cloud bills mostly by deployment count; self-hosted skips both but adds a server you have to run yourself. All three still pay the exact same LLM token cost underneath.
Total cost across run volume — where tiers jump
Same tokens/run and blended price as above, swept across run volume. Framework fee steps up in jumps; LLM cost scales smoothly underneath it.
| Runs/mo | LLM tokens | CrewAI Cloud | LangGraph Cloud | Self-hosted |
|---|
Why "which framework is cheaper" is the wrong first question
Every CrewAI-vs-LangGraph-vs-AutoGen comparison online stops at feature tables and sticker prices — free tier, $29/mo, $49/mo — without ever adding back the cost that actually dominates almost every real deployment: the LLM tokens each agent run consumes. A crew of 3-4 agents handing off context to each other on a single task can easily burn 5,000-15,000 tokens per run once you count each agent's own reasoning plus the conversation history it re-reads on every handoff. At even a moderate blended rate, that token cost outweighs a $29 or $49 monthly framework fee within the first few hundred runs of the month. The framework choice matters for developer experience, checkpointing, and observability — rarely for the bill itself, at least until volume forces a tier jump.
CrewAI Cloud vs LangGraph Cloud: two different billing shapes
CrewAI's cloud pricing scales with run count — you cross from Free to Starter to Professional as your monthly run volume rises, and each tier caps included runs before pushing you to the next. LangGraph Cloud (via the LangGraph Platform / LangSmith stack) scales primarily with deployment count instead — a single deployment on the Plus tier can often absorb far more run volume before you need a second deployment or an upgrade, because you're not paying per run, you're paying for the infrastructure slot. That means a low-volume, high-complexity workload (few runs, deep multi-step reasoning) and a high-volume, simple workload (many short runs) can land on very different relative costs between the two platforms even at similar total token spend — model your own shape rather than trusting a headline price comparison.
When self-hosting actually wins
Both frameworks are open source, so the code itself costs nothing to run yourself — the real cost is a VPS plus the operational surface area a managed cloud tier would otherwise absorb: checkpointing state so a crashed run can resume, retry logic, run-level observability, and uptime monitoring. For a side project or a low-volume internal tool, a small VPS is reliably cheaper than any paid cloud tier. The calculus flips once you need the reliability tooling a managed platform ships by default — at that point you're not really comparing $25/mo vs $49/mo, you're comparing $25/mo plus your own engineering time against a flat managed fee. Pair this with the AI agent swarm cost calculator to model the token cost of a specific multi-agent pattern (fan-out, debate, supervisor), or the MCP server cost calculator if the agent's tools are exposed over MCP rather than the framework's native tool-calling.