Fireworks is model hosting for an agent.ML.ai Code doesn’t need one.
Two different layers: infrastructure you plug your own agent into, versus an agent that already comes with its own routing built in. Pick Fireworks if you already have a coding agent and want to swap in a specific hosted model or run your own dedicated GPUs. Pick ML.ai Code if you want the agent and the routing decision handled as one product, with nothing to connect.
What each one actually is
ML.ai Code is an in-editor coding agent, built on ML.ai Inference, the harness that scores each request and routes it to whichever model clears the quality bar for the least cost. One ML.ai access token, no separate provider key, no connector CLI to run.
A model-hosting platform: 200+ models behind an OpenAI- and Anthropic-compatible endpoint, serverless/on-demand/reserved GPU deployment, and FireConnect, a CLI that reconfigures Claude Code, Codex, OpenCode, or Cursor to call Fireworks instead of their default provider. Fire Pass, a non-production-only offer for agentic coding use, is documented as experimental with pricing and model choice subject to change.
Count the pieces you have to put together yourself.
Fireworks hosts the models. The coding agent is someone else’s, and FireConnect is the CLI that rewires it to point at Fireworks. ML.ai Code is the agent, with its routing already inside it.
Steps before the task can start
Same task, both paths
“Fix this failing test”
Fireworks
Install a coding agent
Claude Code, Codex, Cursor
Run FireConnect
Rewrites that agent’s config
Pick a model to call
200+ hosted, you choose
Now the task can run
With ML.ai Code
Open the editor, start the task
Agent and routing, one product
running from step one
Fireworks hosts the models; the agent and the connector are pieces you bring yourself. ML.ai Code is the agent, with ML.ai Inference routing behind it, on one login.
Which one do you actually need?
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.
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.
Every claim, side by side
The same categories, compared row by row. Every line on both sides traces back to a real doc, pricing page, or published benchmark.
vsWhat it is
What it actually is
A model-hosting platform plus FireConnect, a CLI that reconfigures other companies’ coding agents to call Fireworks. No coding agent of its own.
Connecting to a coding agent
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.
Model access
Model catalog
200+ models (DeepSeek, GLM, Kimi, Qwen, Minimax, and others) behind an OpenAI- and Anthropic-compatible endpoint. You choose which to call.
Routing decision
Manual. You call a specific model by name; Fireworks doesn’t choose one for you outside of Fire Pass’s single router model.
Fine-tuning / training
A dedicated training product: guided fine-tuning up to custom reinforcement-learning loops, billed per 1M training tokens, every checkpoint deployable in seconds.
Deployment & pricing
Deployment options
Serverless (pay per token), on-demand dedicated GPUs (H100 through GB300, by the minute), or reserved capacity with guaranteed quotas.
Pricing transparency
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.
Compliance certifications
SOC 2 Type II, HIPAA, ISO 27001 (with the ISO 27701 privacy extension), and ISO 42001, per their own trust center.
| | |
|---|---|---|
| What it is | ||
| What it actually is | A coding agent (ML.ai Code) built directly on a routing harness (ML.ai Inference); one product, one login. | A model-hosting platform plus FireConnect, a CLI that reconfigures other companies’ coding agents to call Fireworks. No coding agent of its own. |
| Connecting to a coding agent | Nothing to connect. ML.ai Code is the agent; sign in once with an ML.ai access token. | 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. |
| Model access | ||
| Model catalog | 40+ models behind one OpenAI-compatible endpoint, routed to automatically. You never pick one. | 200+ models (DeepSeek, GLM, Kimi, Qwen, Minimax, and others) behind an OpenAI- and Anthropic-compatible endpoint. You choose which to call. |
| Routing decision | Automatic, per request: ML.ai Inference scores the task and routes it, per ML.ai Standard or High tier, without exposing the model. | Manual. You call a specific model by name; Fireworks doesn’t choose one for you outside of Fire Pass’s single router model. |
| Fine-tuning / training | 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. | A dedicated training product: guided fine-tuning up to custom reinforcement-learning loops, billed per 1M training tokens, every checkpoint deployable in seconds. |
| Deployment & pricing | ||
| Deployment options | Fully managed only. No self-hosted or dedicated-capacity option for ML.ai Inference today. | Serverless (pay per token), on-demand dedicated GPUs (H100 through GB300, by the minute), or reserved capacity with guaranteed quotas. |
| Pricing transparency | A published plan table with five tiers for ML.ai Code, free trial to Enterprise. ML.ai Inference’s own routing costs aren’t broken out separately. | 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. |
| Compliance certifications | SOC 2 Type II, ISO 27001, GDPR + CCPA, HIPAA on request, PCI DSS on dedicated capacity. | SOC 2 Type II, HIPAA, ISO 27001 (with the ISO 27701 privacy extension), and ISO 42001, per their own trust center. |
Where each one actually falls short
ML.ai’s own limits get the same weight as theirs, not a footnote after the sales pitch.
Where Fireworks.ai is limited
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.
Where ML.ai is limited
ML.ai Inference is fully managed only; there’s no self-hosted or dedicated/reserved GPU option the way Fireworks offers.
The model catalog behind ML.ai’s router is smaller (40+ vs. Fireworks’ 200+), and which model actually serves a request is never exposed.
No dedicated fine-tuning or training product exists for ML.ai; its own small-model tuning happens behind the scenes, not as something you configure.
ML.ai Inference’s own pricing isn’t broken out from ML.ai Code’s plan table the way Fireworks publishes per-token and per-GPU rates directly.
Questions worth asking
Try ML.ai Code today, or talk to us about what is next.
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