Inference routes your trafficFireworks runs open models on serverless or on-demand GPUs, with training and its own router. ML.ai Inference routes your existing traffic and proves the saving in a 30-day pilot.
Fireworks, model hosting
How Fireworks.ai works
Inference picks the route per request
main.py
Routing decision tier: Standard
Summarize the ticket
Tuned model
Standardskipped
Highskipped
Cleared the acceptance bar
Fireworks serves the model you call, or the one its router predicts. ML.ai Inference routes the request, checks it against your bar and logs the trace.
Same request, both products
“Summarize the ticket”
1Pick a model or router
2Serve the call
3Bill
ML.ai1Scores the request
Tuned modelPicked
StandardSkipped
HighSkipped
2Checks the bar
Inference and Fireworks each dofireworks.py
A model-hosting platform: open models behind a compatible endpoint, serverless or on-demand GPUs, a training product, FireRouter, and FireConnect to point Claude Code, Codex or Cursor at it.
main.py
A router in front of your whole model stack, including frontier APIs and private endpoints. Each request is checked against your acceptance bar, and a model tuned on your traffic takes over what repeats.
Use this if
You want to host, fine-tune or call open models yourself, with GPU capacity you control and published per-token and per-GPU rates.
Use this if
You want routing across the models you already use, frontier and private, checked against your quality bar and proven on your traffic before you commit.
The same categories, row by row. Every line on both sides comes from a doc, pricing page or published benchmark.
ML.aiFireworks.ai
A model-hosting platform with serverless and on-demand GPU inference, training, and its own router.
ML.ai
A managed router between your app and your model stack: route, verify and learn.
Fireworks.ai
FireRouter predicts a model per user turn, across Fireworks open models and closed models you have keys for, with a preference from max intelligence to max savings.
ML.ai
Each request goes to the cheapest route that clears your acceptance bar, under a policy you set: Automatic, Frontier only or Self-hosted only.
Fireworks.ai
A training product, billed per GPU-hour, that you run yourself.
ML.ai
ML.ai mirrors your traffic, trains a model on your patterns, and moves traffic only once it clears your bar.
Fireworks.ai
Not part of the inference API.
Checks are yours to build.
ML.ai
Input PII controls, prompt-injection checks, output moderation and an audit trail, with a trace for every answer.
Fireworks.ai
Published rates: per token for serverless, and per GPU time for on-demand H100, H200, B200, B300 and GB300.
ML.ai
Agreed in a 30-day pilot, against a baseline and targets set up front.
Fireworks.ai
SOC 2 Type II, HIPAA, and ISO 27001, 27701 and 42001, per its own announcements.
ML.ai
SOC 2 and ISO 27001, as listed on ml.ai.
ML.ai’s own limits sit beside theirs, with the same weight.
Checks such as PII controls and output moderation aren’t part of the hosted API, so you build them yourself.
Fine-tuning is a separate product you run and pay for per GPU-hour.
FireRouter routes across models you call through Fireworks.
Closed models need your own provider keys.
FireRouter picks a model per turn across what Fireworks serves. ML.ai Inference sits in front of your whole stack, checks each route against your own acceptance bar, and tunes a model on your traffic.
No. The pilot starts in shadow mode on top of your existing calls, so nothing changes in your code until you move traffic.
Not today. Pricing is agreed in the pilot, against your baseline cost and the targets you set.
pilotCost, quality and rollout targets are agreed before a single real request moves.
