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Baseten

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Baseten is a platform for deploying and serving machine-learning models in production. Rather than a model marketplace, it focuses on giving teams dedicated, autoscaling infrastructure for their own models, with per-minute GPU pricing and an open-source packaging framework called Truss that standardizes how models ship. It also offers model APIs for popular open weights at rates it positions below the big closed providers. The company has raised around five hundred and eighty-five million dollars, including a Series E in early 2026 with NVIDIA investing, and has been reported in talks at a valuation well above its prior round, a sign of how hot AI inference has become. It's built for ML and platform engineers, so there's more setup than a plug-and-play endpoint, and dedicated capacity means you pay for the instances you keep online.

model deploymentmlopsinferencededicated gpuenterprisedeveloper tools

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Our take

Baseten gives ML teams dedicated, autoscaling infrastructure to deploy and serve their own models, with per-minute GPU pricing and its open-source Truss packaging tool. NVIDIA-backed and valued in the billions amid the inference boom. Best for production model serving, though it's engineer-facing and dedicated capacity bills for uptime.

Best for

ML and platform engineers deploying custom or open models on dedicated, autoscaling GPU infrastructure they control.

Pros

  • Dedicated, autoscaling model serving with per-minute GPUs
  • Open-source Truss framework standardizes deployment
  • Model APIs priced below major closed providers
  • Well-funded with NVIDIA backing

Cons

  • Engineer-facing; more setup than a hosted endpoint
  • Dedicated instances bill for idle uptime
  • Crowded inference market with aggressive pricing

How it compares

Where Together and Fireworks emphasize shared serverless inference, Baseten centers on dedicated deployment of your own models, closer to MLOps than a model marketplace.

Full review

Baseten is a platform for deploying and serving machine-learning models in production. Rather than a model marketplace, it focuses on giving teams dedicated, autoscaling infrastructure for their own models, with per-minute GPU pricing and an open-source packaging framework called Truss that standardizes how models ship. It also offers model APIs for popular open weights at rates it positions below the big closed providers. The company has raised around five hundred and eighty-five million dollars, including a Series E in early 2026 with NVIDIA investing, and has been reported in talks at a valuation well above its prior round, a sign of how hot AI inference has become. It's built for ML and platform engineers, so there's more setup than a plug-and-play endpoint, and dedicated capacity means you pay for the instances you keep online.

Where Together and Fireworks emphasize shared serverless inference, Baseten centers on dedicated deployment of your own models, closer to MLOps than a model marketplace.

Cloudkart Trust Graph

3.4/5
  • Actual Utility4/5

    Source: Initial LLM-authored rubric (backfill)

  • Ease of Use3/5

    Source: Initial LLM-authored rubric (backfill)

  • Pricing Fairness3/5

    Source: Initial LLM-authored rubric (backfill)

  • Reliability4/5

    Source: Initial LLM-authored rubric (backfill)

  • Differentiation3/5

    Source: Initial LLM-authored rubric (backfill)

Scored as of . Each score is versioned and auditable; vendors cannot buy it.

How this score is set

Editorial rubric
Primary signal — five dimensions, 3.4/5 average.
Community reviews
None yet.
Pricing verified
Not yet verified
Independence
Score set by our editorial team before any affiliate relationship is considered. No vendor can buy it.

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Frequently asked questions

Is Baseten free, and how much does it cost?
Baseten is a paid tool.
Who is Baseten best for?
ML and platform engineers deploying custom or open models on dedicated, autoscaling GPU infrastructure they control.
How is Baseten rated on Cloudkart.ai?
Baseten scores 3.4 out of 5 on the Cloudkart.ai rubric, which weighs actual utility, ease of use, pricing fairness, reliability and differentiation. Scores are set editorially and can never be bought.

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