Before you pick a tool, pick the decision it makes for you
Serving a model, reviewing agent work, running an agent unattended and hosting a model are four different jobs. Pick the one you are deciding on.
Model serving and model choice are different decisions
Baseten serves the model you name. ML.ai scores each task and picks the model for you.
The question to ask
Do you need the model choice made for you, or serving for a model you’ve already picked?
Same task, two setups
BasetenYou pick the model
Modelopen-70b-instruct
HardwareGPU you choose
Scale to zeroafter 15 min idle
Cold start30 to 90 s
call.py
Picks per request
call.py
Routing decisionAccepted
Tuned open modelClears bar
Mid-size open modelNot needed
Frontier modelNot needed
Assisting an agent is not the same as authoring a change
Entelligence routes and reviews around agents it doesn’t build itself. ML.ai reads your repo and writes the edit directly, gated by your approval.
The question to ask
Are you routing and reviewing an agent’s work, or asking one to write the change itself?
Same task, two setups
Entelligence.aiReviews and routes
PR reviewAutomatic on open
Routing policyBalanced or Eco
Cost visibilityPer-turn log
Writes the codeYour agent does
pull request
Asks before every edit
Edit checkout/cart.ts?
1 line changed, diff open on the left
The point of approval matters
Factory’s Droids can be configured to act unattended, up to pushing to git on their own. ML.ai holds every edit behind your approval, with no setting that removes that gate.
The question to ask
Where do you want a human to look before a change actually ships?
Same task, two setups
Factory.aiAutonomy you set
OffRead-only
LowEdits, low-risk commands
MediumReversible changes, commits
HighDocker, git push
terminal
Asks before every edit
Edit checkout/cart.ts?
1 line changed, diff open on the left
A hosted model is one part of an agent workflow
Fireworks hosts models you connect your own agent to. ML.ai is the agent, with its routing decision already built in, nothing separate to connect.
The question to ask
Are you hosting the model yourself, or looking for the agent already built on top of one?
Same task, two setups
Fireworks.aiYou pick the model
Catalog100+ models
ServerlessPay per token
DedicatedGPUs by the minute
Your agentRewired by FireConnect
call.py
Picks per request
call.py
Routing decisionAccepted
Tuned small modelClears bar
Mid-size open modelNot needed
Frontier modelNot needed
The perspectives below summarize ML.ai’s published comparisons, not an independent product benchmark.
A comparison is useful when the jobs match
Try today, or talk to us about what is next
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