Langfuse vs LiteLLM

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-09-29
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At a glance

DimensionLangfuseLiteLLM
Pricingfreemium · from Hobby $0/mofreemium · from Open Source $0
Best forAI engineering teams running multi-turn chat or coding agents in production, Enterprises needing self-hosting or a US/EU/JP data region for SOC 2, ISO 27001, or HIPAAPlatform teams giving the whole org one API to many LLMs, MCPs, and agents, Organizations tracking spend and enforcing per-team budgets
Standout featuresHierarchical tracing of LLM calls, tool invocations, and retrieval steps · Session tracking for multi-turn conversations and agentic workflows · Per-user token and cost tracking for multi-tenant billingOne OpenAI-compatible API to 140+ providers and 1,800+ models · Day-0 support for new model releases (Grok 4.7, Qwen3.8-Omni-Flash) · Rust-based core for lower latency and reduced memory overhead
Viability score89/10078/100
APIYesYes

Langfuse is the stronger pick for ai engineering teams running multi-turn chat or coding agents in production; LiteLLM fits better for platform teams giving the whole org one api to many llms, mcps, and agents.

Built from live tool data, last verified 2026-09-29.

Langfuse
Langfuse

Open-source LLM observability, prompt management, and evaluation for teams running AI agents in production.

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LiteLLM
LiteLLM

Self-hosted AI gateway putting 140+ providers, MCP servers, and agents behind one OpenAI-compatible API.

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Pricing
Freemium
Freemium
Plans
$0/mo
$29/mo
$199/mo
$300/mo
$2499/mo
$0
Custom (Annual)
Popularity
6.5k views
5.1k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
WebAPI
APICLI
Categories
📡 LLM Observability & Evals
🚦 LLM Gateways & Model Routers
Features
Hierarchical tracing of LLM calls, tool invocations, and retrieval steps
Session tracking for multi-turn conversations and agentic workflows
Per-user token and cost tracking for multi-tenant billing
Agent graphs visualizing complex agentic workflows
Responsive Trace Timeline with map-style zoom and colour-coded observation types
LLM-as-a-judge evaluators, including multi-modal and multi-message prompt support
Code/heuristic evaluators and custom evaluation scores
Backfill evaluator scores onto historical observations when attaching an evaluator to a rule
Human annotation queues for building golden datasets
Evaluator versioning, restore-as-draft, and template starters for chatbots and coding agents
Prompt versioning, labels, one-click deployments, and rollbacks
Prompt composability with server- and client-side prompt caching
Playground for testing prompts on real production inputs and comparing models
Datasets and Experiments via SDK or UI with side-by-side result comparison
Langfuse Assistant runs code over thousands of observations in a background sandbox
One OpenAI-compatible API to 140+ providers and 1,800+ models
Day-0 support for new model releases (Grok 4.7, Qwen3.8-Omni-Flash)
Rust-based core for lower latency and reduced memory overhead
Auto Router v2 with complexity, semantic, and adaptive routing
Router Plugins for custom routing signals
MCP server and agent access through the same gateway
Virtual keys, users, and teams with scoped model access
Spend tracking by key, user, team, and organization
Budget caps and per-tag budgets
Rate limits (RPM/TPM) with cooldowns
LLM fallbacks and load balancing across deployments
Shadow evaluations of Auto Router on sampled production traffic
Guardrails integrations (Presidio, Lakera, Aporia, Bedrock Guardrails)
Observability via Prometheus, Langfuse, and OpenTelemetry
Self-hosted and air-gapped deployment
Integrations
LangChain
Vercel AI SDK
LiteLLM
Pydantic AI
Google ADK
CrewAI
LiveKit
OpenAI
Anthropic
Amazon Bedrock
Azure OpenAI
Mistral AI
Google Gemini
xAI
PostHog
Vertex AI
AWS Bedrock
Cloudflare
Presidio
Lakera
Aporia
Langfuse
Prometheus
OpenTelemetry
GitHub MCP
S3

Frequently Asked Questions

Which is better, Langfuse or LiteLLM?

The best choice between Langfuse and LiteLLM depends on your specific use case — we compare them independently on features, current pricing, integrations, and real-world signals (with an on-demand sentiment scan available for each). See the side-by-side breakdown above to match them to your needs.

What are the main differences between Langfuse and LiteLLM?

The key differences include pricing model, feature set, platform support, and skill level requirements. Review the full comparison on RightAIChoice for a detailed breakdown.

Is there a free version of Langfuse or LiteLLM?

Check the pricing section in the comparison for the latest pricing details on both tools, including free tiers, trial options, and paid plans.

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Explore each tool further

Langfuse
View Langfuse reviewLangfuse alternatives
LiteLLM
View LiteLLM reviewLiteLLM alternatives

Browse these categories

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Last reviewed: May 12, 2026