Compare ML.ai with the tools you’re weighing
Two products, compared on their own ground: the ML.ai coding agent against other coding tools, and ML.ai Inference against model platforms.
Coding agent
, the coding agent
For developers and teams picking an agent for their editor. Install it and pick a plan from $10 a month.
The same accuracy for about a third of the cost
On the same 1,344 tasks, ML.ai averaged 78.8% at $0.031 per task. Claude Code with the Claude family averaged 78.4% at $0.085.
The question to ask
Do you need Claude models on every step, or the same score for less per task?
Same task, two setups
Claude CodeYou pick a Claude model
ModelChosen with /model
Permission modeManual up to Auto
Cost per task$0.085 (benchmark)
PlansPro $20 to Ultra $200
terminal
Asks before each edit
Edit checkout/cart.ts?
1 line changed, diff open on the left
Keep the editor, change who picks the model
On Cursor’s individual plans you pick the agent’s model for each task. ML.ai runs inside Cursor as an extension and routes each step for you on every plan.
The question to ask
Do you want to keep picking a model per task, or have it picked for you in the same editor?
Same task, two setups
CursorYou pick the model
Agent modelPicked per task
Auto routerTeams and Enterprise
Run modeAuto-review
Third-party modelsAPI list price
Cursor agent
Asks before each edit
Edit checkout/cart.ts?
1 line changed, diff open on the left
Where the approval happens matters
Factory’s Droids run at the autonomy level you set, up to High, where high-risk actions run without asking. ML.ai asks before each action by default.
The question to ask
Where do you want a person to look before a change goes through?
Same task, two setups
Factory.aiAutonomy you set
OffRead-only
LowEdits, low-risk commands
MediumReversible changes, commits
HighHigh-risk actions
terminal
Asks before each edit
Edit checkout/cart.ts?
1 line changed, diff open on the left
A router for agents, or the agent itself
Entelligence routes and reviews around agents your team already runs. ML.ai is the agent: it reads your repo and writes the edit, gated by your approval.
The question to ask
Are you routing and reviewing an agent’s work, or picking the agent that writes the change?
Same task, two setups
Entelligence.aiRoutes and reviews
PR reviewSelf-healing review
Routing policyBalanced or Eco
Works withClaude Code, Codex
Writes the codeYour agent does
pull request
Asks before each edit
Edit checkout/cart.ts?
1 line changed, diff open on the left
For enterprise
Inference, for your own model traffic
For teams running models inside their product. ML.ai Inference routes each request and proves the saving in a 30-day pilot.
Serving a model and choosing one are different jobs
Baseten serves the model you name. ML.ai Inference routes each request to the cheapest model that clears your quality bar.
The question to ask
Is your problem serving a model you’ve picked, or picking the right model for each request?
Same task, two setups
BasetenYou pick the model
Modelopen-70b-instruct
HardwareGPU you choose
Scale to zeroafter 15 min idle
Startupbilled per minute
call.py
InferencePicks per request
call.py
Routing decisionAccepted
Tuned modelClears bar
StandardNot needed
HighNot needed
A model platform and a managed router
Fireworks hosts models and lets you route across them. ML.ai Inference sits in front of your whole model stack and tunes a model on your traffic.
The question to ask
Do you want a platform to host and call models, or a router proven against your own quality bar?
Same task, two setups
Fireworks.aiHost and call models
ServerlessPer token
On-demand GPUsH100 to GB300
RouterFireRouter
Your agentConnected by FireConnect
call.py
InferencePicks per request
call.py
Routing decisionAccepted
Tuned modelClears bar
StandardNot needed
HighNot needed
A comparison is useful when the jobs match
Try today, or talk to us about what is next
Install the editor agent on your own machine, or book a call to talk through your team’s workloads