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Langfuse vs Together AI

LangfuseTogether AI

Bottom line: Langfuse for teams wanting open-source LLM observability; Together AI for cost-conscious teams on open models.

LangfuseLangfuse

Open-source LLM observability and evaluation

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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-observabilityopen-sourcetracingevaluationprompt-management
inferencefine-tuninggpu-cloudopen-sourcellm-api
Best for
  • Teams wanting open-source LLM observability
  • Data-sensitive teams needing self-hosting
  • Prompt and evaluation workflows
  • Cost-conscious teams on open models
  • ML teams that fine-tune
  • Startups scaling inference volume
Pros
  • Open source with nearly all features MIT-licensed
  • Self-host the full product free, no seat or usage caps
  • Framework-agnostic (works with or without LangChain)
  • Strong tracing, prompt management, and evaluation
  • Managed cloud with a free tier available
  • 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 still requires running infrastructure
  • Enterprise compliance features are commercial
  • Cloud Pro tier jumps significantly in price
  • Focused on observability, not app building
  • Analytics depth may need tuning for large scale
  • 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.