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Fine-Tuning

MLOps and deployment · by Together AI

Fine-tune open-source models for production workloads, using the latest research techniques.

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What it does

Fine-tune open-source models for production workloads, using the latest research techniques. Improve accuracy, reduce hallucinations, and control behavior — without managing training infrastructure.

  • Inference
  • Fine-tuning
  • GPU clusters
  • Model shaping
  • Pre-training
Category
MLOps and deployment
Users
AI-native teams, Enterprise developers, Production AI workloads
Public API
Not offered

Not yet disclosed: deployment options, platforms, languages and support channels.

Pricing

  • Free plan$0
  • Usage-based$0.34
    • With a $4.00 minimum per job

Free trial on paid plans

Prices as published by Together AI, checked Oct 2026.

Security and compliance

As reported by Together AI. Ask for audit reports before you buy.

  • SOC 2 Type IICompliant
  • Zero data retention (ZDR): Together does not store inputs or outputs by default; temporary caching may be used for performance unless otherwise configured.
  • Training opt-in: Data sharing for training other models is opt-in and not enabled by default; configurable in Organization Settings under Privacy.
  • Organization-level privacy toggles: Admins can control storing prompts/responses, allowing data for training, and allowing passthrough models.
  • Passthrough model isolation: Non-passthrough models run on Together's own infrastructure with no call-out to model authors; passthrough is a separate organization-level toggle.
  • VPC-based deployments and private networking available for enterprise customers.
  • OIDC authentication supported for GPU Clusters.
  • Single sign-on (SSO) listed under Administration features.

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