Baseten vs Together AI

Detailed side-by-side comparison to help you choose the right tool

Baseten

🔴Developer

App Deployment

Baseten helps engineering teams deploy, autoscale, and monitor custom or open-source AI models behind production-ready inference APIs.

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Starting Price

Custom

Together AI

🔴Developer

AI Model Hosting & Inference

AI-native cloud for inference, fine-tuning, and dedicated GPU clusters, offering 200+ open-source and frontier-class models behind an OpenAI-compatible API plus reserved H100/H200/B200 capacity.

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Starting Price

$0.02/1M tokens

Feature Comparison

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FeatureBasetenTogether AI
CategoryApp DeploymentAI Model Hosting & Inference
Pricing Plans4 tiers142 tiers
Starting Price$0.02/1M tokens
Key Features
  • Cross-cloud GPU inference
  • Custom model deployment via Truss
  • Pre-optimized model library
  • Serverless inference APIs for open and proprietary model workloads
  • Batch Inference API for large asynchronous token processing jobs
  • Fine-tuning platform for shaping open models with private or domain data

💡 Our Take

Choose Baseten if you need to deploy custom or fine-tuned models with dedicated infrastructure, full control over scaling, and enterprise compliance. Choose Together AI if you want a shared serverless API for popular open-source LLMs with transparent per-token pricing and no infrastructure management.

Baseten - Pros & Cons

Pros

  • Transparent per-token and per-minute examples help teams model costs
  • Strong fit for teams moving from notebooks to production APIs
  • Enterprise options cover data residency and security-sensitive deployments

Cons

  • Pro and Enterprise require quotes, so total cost depends on volume and commitments
  • GPU inference still requires performance testing per model and workload
  • Overkill for teams that only need hosted frontier model APIs

Together AI - Pros & Cons

Pros

  • Breadth of open-weight model catalog (200+) with one OpenAI-compatible API
  • One account spans serverless, dedicated endpoints, fine-tuning, and reserved GPU capacity
  • Transparent per-token pricing — easy to model unit economics against closed providers
  • InfiniBand-backed GPU Clusters are credible for real training, not just inference

Cons

  • Frontier-class reasoning still lags closed models on the hardest benchmarks
  • Fastest single-model latency is sometimes beaten by Groq or Cerebras
  • Many model variants means model selection itself becomes a project
  • Dedicated endpoint cost calculations require attention to GPU type and utilization

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🔒 Security & Compliance Comparison

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Security FeatureBasetenTogether AI
SOC2✅ Yes
GDPR✅ Yes
HIPAA
SSO
Self-Hosted❌ No
On-Prem❌ No
RBAC
Audit Log
Open Source❌ No
API Key Auth✅ Yes
Encryption at Rest✅ Yes
Encryption in Transit✅ Yes
Data ResidencyUS
Data Retentionconfigurable
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