Fireworks AI vs Letta
Letta scores higher on the AgentReady, 72/100 against 59/100. They differ on 3 of the 41 signals. Which ones decides whether an agent can adopt them without a person watching.
What each one is
Fireworks AI. Fireworks AI is the fastest platform for building with open source AI models, providing production-ready inference and fine-tuning with best-in-class speed, cost and quality.
Letta. Letta is an AI research lab in San Francisco building machines that learn.
Where Letta is ahead
Letta passes mcp discoverable, and Fireworks AI does not. That is discover, whether an agent can find the product at all without being told it exists.
It also holds adopt: mcp integration available. Fireworks AI misses it.
And on operate, agent compatibility verified. Fireworks AI misses it.
What neither does
Both fail structured api reference, openapi / spec quality, request examples provided, response examples provided, errors and status codes documented, no mandatory sales call, programmatic credential creation, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable. If your agent needs any of those, you will be building it yourself either way.
Score, pillar by pillar
The AgentReady splits into four pillars, scored separately, because a product can be easy to find and still impossible to adopt.
Discover. Letta leads 100 to 87. Fireworks AI misses mcp discoverable; Letta misses nothing.
Understand. Both sit at 38/100 here. Fireworks AI misses structured api reference, openapi / spec quality, request examples provided, response examples provided, errors and status codes documented; Letta misses structured api reference, openapi / spec quality, request examples provided, response examples provided, errors and status codes documented.
Adopt. Letta leads 81 to 65. Fireworks AI misses no mandatory sales call, programmatic credential creation, mcp integration available; Letta misses no mandatory sales call, programmatic credential creation.
Operate is whether an agent can run against it in production and recover when a call fails. Letta leads 67 to 47. Fireworks AI misses machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, agent compatibility verified; Letta misses machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable.
Pricing
Fireworks AI does not publish a machine-readable starting price and has a free tier. Letta starts at $0/mo and has a free tier.
| Fireworks AI plans | Letta plans |
|---|---|
| Serverless Inference Pay per token | Free $0 /month |
| Embeddings - up to 150M $0.008 / 1M input tokens | Pro $20 /month |
| Embeddings - 150M-350M $0.016 / 1M input tokens | API Plan $20 /month |
| Embeddings - Qwen3 8B $0.1 / 1M input tokens | Teams Pro $20 /seat/month |
| Training - Models up to 16B LoRA SFT: $0.50, LoRA DPO: $1.00, Full Param SFT: $1.00, Full Param DPO: $2.00 per 1M training tokens | Enterprise Custom |
| Training - Models 16.1B-80B LoRA SFT: $3.00, LoRA DPO: $6.00, Full Param SFT: $6.00, Full Param DPO: $12.00 per 1M training tokens | - |
| Training - Models 80B-300B LoRA SFT: $6.00, LoRA DPO: $12.00, Full Param SFT: $12.00, Full Param DPO: $24.00 per 1M training tokens | - |
| Training - Models >300B LoRA SFT: $10.00, LoRA DPO: $20.00, Full Param SFT: $20.00, Full Param DPO: $40.00 per 1M training tokens | - |
| On Demand Deployments Pay per GPU second | - |
Signal by signal
| Signal | Fireworks AI | Letta |
|---|---|---|
| AgentReady | 59 | 72 |
| Discovery | 87 | 100 |
| Understanding | 38 | 38 |
| Adoption | 65 | 81 |
| Operability | 47 | 67 |
| Public API | Yes | Yes |
| MCP server | No | Yes |
| OpenAPI spec | Unknown | Unknown |
| CLI | Yes | Yes |
| llms.txt | Yes | Yes |
| Self-serve signup | Yes | Yes |
| Free tier | Yes | Yes |
Which to pick
Letta clears more of the signals an agent needs, so it is the safer default for unattended use. Full profiles: Fireworks AI and Letta. Alternatives to each: Fireworks AI, Letta.
An agent can fetch this as data: POST /v1/compare {"slugs": ["fireworks", "letta"]}