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LiteLLM vs Groq

LiteLLMGroq

Bottom line: LiteLLM for engineering teams juggling multiple LLM providers; Groq for developers building latency-sensitive apps.

LiteLLMLiteLLM

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

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GroqGroq

Very fast LLM inference on custom LPU hardware.

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Votes00
PricingFreemiumFreemium
CategoryCodingCoding
Tags
llm-gatewayopen-sourceapi-proxymodel-routingllmops
inferencellm-apilow-latencyopen-sourcehardware
Best for
  • Engineering teams juggling multiple LLM providers
  • Platform teams building an internal AI gateway
  • Startups wanting free multi-model routing
  • Developers building latency-sensitive apps
  • Teams running AI agents
  • Voice and real-time product builders
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
  • Exceptional inference speed on supported models
  • Competitive per-token pricing
  • OpenAI-compatible API is easy to adopt
  • Free tier with no credit card
  • Good fit for agents and real-time apps
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
  • Limited to a curated catalog of open models
  • No hosting of arbitrary custom weights
  • Model lineup changes over time
  • Corporate turbulence in 2026 (Nvidia deal, down round)
  • Free-tier rate limits are modest

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