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LiteLLM vs Together AI

LiteLLMTogether AI

Bottom line: LiteLLM for engineering teams juggling multiple LLM providers; Together AI for cost-conscious teams on open models.

LiteLLMLiteLLM

Open-source AI gateway to call 100+ LLM APIs in one format

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Together AITogether AI

Inference, fine-tuning, and GPU clusters for open models.

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Votes00
PricingFreemiumFreemium
CategoryCodingCoding
Tags
llm-gatewayopen-sourceapi-proxymodel-routingllmops
inferencefine-tuninggpu-cloudopen-sourcellm-api
Best for
  • Engineering teams juggling multiple LLM providers
  • Platform teams building an internal AI gateway
  • Startups wanting free multi-model routing
  • Cost-conscious teams on open models
  • ML teams that fine-tune
  • Startups scaling inference volume
Pros
  • Free, actively maintained open-source core with a large community
  • Supports 100+ providers through one OpenAI-compatible interface
  • Built-in cost tracking, budgets, and virtual keys
  • Load balancing, retries, and fallbacks for reliability
  • Can be fully self-hosted for data control
  • Large catalog of open and open-weight models
  • Competitive per-token pricing
  • Fine-tuning with weight ownership
  • Dedicated GPU clusters for scale
  • OpenAI-compatible API
Cons
  • Self-hosting means you own deployment, scaling, and maintenance
  • Advanced governance (SSO, RBAC, audit logs) requires the paid Enterprise tier
  • Enterprise pricing is negotiated and not fully transparent
  • Acting as a proxy adds an operational hop to debug when issues arise
  • Feature breadth can make initial configuration complex
  • Broad pricing surface across several product lines
  • You own quality and safety evaluation of open models
  • Dedicated clusters require commitment and planning
  • Less turnkey than closed frontier APIs
  • Model catalog and prices change over time

Comparison generated from each tool's listing. Add or remove tools above to change it.