What is ML.ai?
ML.ai is an AI coding agent that goes where your code goes. Use it inside VS Code and Cursor, in its own desktop app with no IDE required, from the terminal, or as a PR Reviewer that checks every pull request. Every surface runs on the same engine, with the same settings and the same memory of your repo.
Most coding agents send every step to the same expensive frontier model. ML.ai hands each step to a focused agent and picks the most cost-efficient model that meets your quality bar, which typically cuts model spend by 30 to 45% on the same work. Explore reads and explains the codebase.
Architect and Plan map the files, order of work and trade-offs. Only General can change code, and every edit or command waits for you to Allow or Deny it. The PR Reviewer summarizes each pull request, leaves line-level comments, and runs builds and tests before it recommends approve or request changes.
It never merges on its own.
Also included: reusable skills, background runs, MCP server support, a command safety classifier, and an access token that stays on your machine. ML.ai is a product of Pixis.ai, backed by SoftBank Vision Fund and General Atlantic.
ML.ai pricing
FreemiumML.ai offers a free plan with paid upgrades available. Plans start from ₹699/month.
Verify on the official pricing page.
View plansML.ai's key features
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Routes a code repository into a running deployment environment
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Automates repository configuration and environment setup
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Provides real-time logs and monitoring during routing
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Integrates with CI/CD pipelines to trigger automatic updates on repository pushes
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Offers access controls and rollback options to manage deployment state
ML.ai use cases
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Automate continuous deployment of a web application from a Git repository using ML.ai, linking version control to CI/CD pipelines, monitoring real-time deployment logs and health metrics during rollout, and performing instant rollback when issues are detected
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Package and deploy machine learning models reproducibly with ML.ai, automatically provisioning environments, recording configuration and deployment artifacts for auditability and retraining, while surfacing real-time logs, metrics and post-deployment reports
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Manage multi-environment team deployments with ML.ai, enforce role-based access controls and approval gates, synchronize branches to staging and production with automated environment setup, and retain recorded configurations and artifacts for compliance and troubleshooting
ML.ai user reviews
Would you recommend ML.ai?
Who is ML.ai for?
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Devops engineer
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Site reliability engineer (sre)
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Platform engineer
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Mlops engineer
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Software engineer
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Data scientist
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Release manager
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Qa engineer
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Engineering manager
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Security/devsecops engineer
ML.ai FAQ
Is ML.ai free?
Partly. ML.ai has a free plan with usage limits, and paid plans unlock the full feature set. Paid plans start at ₹699/month.
What are the best alternatives to ML.ai?
The closest alternatives to ML.ai are MLflow. You can compare them side by side in the alternatives section further down this page.
