picks and tunes the modelFireworks runs 100+ models on serverless, on-demand or reserved GPUs, plus its own training product. ML.ai routes each request and tunes on your traffic.
Fireworks, model hosting
How Fireworks.ai works
picks the model per request
main.py
Routing decision model=auto
Summarize the ticket
Tuned small model
Mid-size open modelskipped
Frontier modelskipped
Cleared the quality bar
On Fireworks you name the model each call runs on. ML.ai scores each request across 40+ models and tunes on your traffic.
Same request, both products
“Summarize the ticket”
1Choose a model ID
2Serve the call
3Bill
ML.ai1Router scores this request
Tuned small modelPicked
Mid-size open modelSkipped
Frontier modelSkipped
2Answer returned
and Fireworks each dofireworks.py
A model-hosting platform: 100+ models behind a compatible endpoint, serverless, on-demand or reserved GPUs, and a dedicated fine-tuning product. You choose which model to call.
main.py
One endpoint in front of 40+ models. A router scores each request on cost and quality, and a smaller model is tuned on your traffic behind the scenes.
Use this if
You already have a coding agent and want to swap in a specific hosted or fine-tuned model, or need dedicated/reserved GPU capacity you control directly.
Use this if
You want one product: a coding agent whose routing is already built in, no separate model host to pick, connect, or pay for on top of it.
The same categories, compared row by row. Every line on both sides traces back to a real doc, pricing page, or published benchmark.
ML.aiFireworks.ai
A model-hosting platform plus FireConnect, a CLI that reconfigures other companies’ coding agents to call Fireworks.
No coding agent of its own.
ML.ai
A coding agent built directly on its own routing harness; one product, one login.
Fireworks.ai
Requires FireConnect to rewrite Claude Code, Codex, OpenCode, or Cursor’s own config to point at Fireworks; reversible byte-for-byte when turned off, but a setup step ML.ai has none of.
ML.ai
Nothing to connect.
ML.ai is the agent; sign in once with an ML.ai access token.
Fireworks.ai
100+ models (DeepSeek, GLM, Kimi, Qwen, Minimax, and others) behind an OpenAI- and Anthropic-compatible endpoint.
You choose which to call.
ML.ai
40+ models behind one drop-in API endpoint, routed to automatically.
You never pick one.
Fireworks.ai
Manual.
You call a specific model by name; Fireworks doesn’t choose one for you outside of Fire Pass’s single router model.
ML.ai
Automatic, per request: ML.ai scores the task and routes it, per ML.ai Standard or High tier, without exposing the model.
Fireworks.ai
A dedicated training product: guided fine-tuning billed per 1M training tokens, up to custom reinforcement-learning loops billed per GPU-hour, every checkpoint deployable in one click.
ML.ai
Not a separate product.
ML.ai’s own small models are continually fine-tuned on your traffic behind the scenes, not something you configure per model.
Fireworks.ai
Serverless (pay per token), on-demand dedicated GPUs (H100 through GB300, by the minute), or reserved capacity with guaranteed quotas.
ML.ai
Fully managed only.
No self-hosted or dedicated-capacity option for ML.ai today.
Fireworks.ai
Published per-token and per-GPU-minute rates by model and hardware, plus $1 in free credits to start.
Fire Pass’s own pricing is explicitly called experimental and subject to change.
ML.ai
A published plan table with five tiers, free trial to Enterprise.
Routing costs aren’t broken out separately.
Fireworks.ai
SOC 2 Type II, HIPAA, ISO 27001 (with the ISO 27701 privacy extension), and ISO 42001, per their own trust center.
ML.ai
SOC 2 Type II, ISO 27001, GDPR + CCPA, HIPAA on request, PCI DSS on dedicated capacity.
ML.ai’s own limits sit right beside theirs, with the same weight.
Fire Pass, their agentic-coding-specific offer, is documented as non-production-only and experimental, with pricing and the included model explicitly subject to change.
No coding agent of their own; using Fireworks with an existing agent requires running FireConnect to rewrite that agent’s own configuration first.
Model choice is manual: nothing routes a request to the right model for you outside of Fire Pass’s single router model, which itself isn’t for production use.
Mostly with the routing harness inside ML.ai, not with the coding agent. Fireworks has no coding agent of its own; FireConnect only reconfigures other companies’ agents (Claude Code, Codex, Cursor) to use Fireworks-hosted models instead.
No. ML.ai is the agent itself; sign in with an ML.ai access token and there’s nothing else to configure. FireConnect exists because Fireworks doesn’t build the agent side at all.
No. ML.ai routes automatically and doesn’t expose or let you choose the underlying model. On Fireworks, you call a specific model from its 100+ catalog by name.
Not directly; it’s a cheaper way to call one hosted model from inside an agent you already run, and Fireworks’ own docs mark it experimental and off-limits for production use. ML.ai is a full agent, production-ready, with routing already built in.
today, or talk to us about what is nextTry ML.ai in the editor you use today and approve every change it makes.
