Langfuse vs LiteLLM

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

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At a glance

Dimension	Langfuse	LiteLLM
Best for	Teams debugging production LLM apps with tracing, evals, and prompt versioning.	Platform teams centralizing LLM access across providers with virtual keys and budgets.
Pricing	Free self-hosted (MIT), managed cloud has free 50k events/mo, Pro $59/mo, Team $499/mo.	Open source (MIT) and proxy are free; Enterprise from $5K/year for SSO, audit logs, SLA.
Setup complexity	Wrap LLM calls with decorator in Python/TS; self-host requires Docker Compose + Postgres + ClickHouse + Redis.	Install pip package or spin up proxy via Docker; routes through OpenAI-compatible interface.
Strongest differentiator	Deep observability with traces, evals, and prompt management in one open-source platform.	Unified API gateway for 100+ providers with virtual keys, budgets, and fallbacks.
Open source license	MIT, with self-hosted option for full platform.	MIT, with self-hosted proxy and SDK.
Integrations	80+ integrations including LLM SDKs, frameworks, and OpenTelemetry.	100+ providers via unified API; logs to Langfuse, Helicone, OpenTelemetry.

Langfuse vs LiteLLM are complementary rather than direct competitors. Langfuse wins for teams that need deep observability, debugging, and prompt management for production LLM applications. LiteLLM wins as a central AI gateway for organizations managing multi-provider access and cost control. The deciding factor: if you need traces and evals, choose Langfuse; if you need a unified API proxy with virtual keys and budgets, choose LiteLLM. Many teams use both together — LiteLLM proxy logs to Langfuse for observability.

Langfuse

Open-source LLM observability platform — traces, evals, prompts, datasets for production agents.

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LiteLLM

Unified Python SDK and proxy for 100+ LLM providers — one OpenAI-compatible API for all models.

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Pricing

Freemium

Plans

Free (MIT)

Free

$59/mo

$499/mo

Free (MIT)

From $5K/year

Rating

—

Popularity

0 views

Skill Level

Intermediate

API Available

Platforms

WebAPI

APICLI

Feature-by-feature

Langfuse vs LiteLLM: Core Capabilities

Langfuse focuses on observability: structured tracing of every LLM call (inputs, outputs, tokens, cost, latency), session views, prompt management with versioning, and LLM-as-judge evals. LiteLLM focuses on access: an OpenAI-compatible SDK/proxy that lets you call 100+ providers with one codebase, plus virtual key management, rate limiting, and cost tracking per team. Langfuse wins for debugging and quality assurance because of its deep trace inspection and evaluation workflows. LiteLLM wins for multi-provider routing and access control.

AI/Model Approach: Langfuse vs LiteLLM

Langfuse does not call models itself; it observes calls made through any SDK (OpenAI, Anthropic, LangChain, etc.) with decorators or middleware. LiteLLM acts as a proxy that actually routes requests to providers, normalizing differences in API formats. If you need to swap models without code changes, LiteLLM is the tool. If you need to understand what a model actually did, Langfuse is the tool. They complement each other: LiteLLM proxy can log every request to Langfuse for tracing.

Integrations & Ecosystem

Langfuse claims 80+ integrations including LangChain, LlamaIndex, Vercel AI SDK, LiteLLM, and OpenTelemetry. It captures traces from agent frameworks like LangGraph, AutoGen, and OpenAI Agents SDK. LiteLLM supports 100+ LLM providers through its unified API and can log to Langfuse, Helicone, or OpenTelemetry. Both are open source (MIT) and widely adopted. For integration breadth, LiteLLM wins on provider coverage; Langfuse wins on framework and observability integrations.

Performance & Scale

Langfuse uses ClickHouse for analytics and can handle high event volumes with sampling. The self-hosted version requires Postgres + ClickHouse + Redis, which adds operational overhead. LiteLLM’s proxy is lightweight (FastAPI + Postgres) and designed for low-latency routing. For high-throughput environments, LiteLLM is more straightforward to scale horizontally; Langfuse requires careful infrastructure tuning. Public benchmarks are not available for either.

Developer Experience & Workflow

Langfuse offers a decorator-based integration: wrap your LLM call and traces appear immediately. Its UI provides prompt playground, dataset management, and human annotation queues. LiteLLM provides a drop-in replacement for openai-python — just change the base URL. The proxy admin UI handles keys, budgets, and usage. Developers switching providers frequently prefer LiteLLM’s simplicity; teams debugging agent behavior prefer Langfuse’s rich trace visualization.

Pricing compared

Langfuse pricing (2026)

Langfuse offers a self-hosted version that is fully free under MIT license, with unlimited events and all features. The managed cloud has a Hobby tier (free, 50k events/month, 1 project), Pro tier ($59/month, 100k events/month, unlimited projects, evals, datasets), and Team tier ($499/month, unlimited events, SSO, priority support). Enterprise adds regional data residency, audit logs, and HIPAA compliance. There is no per-event overage disclosed; users may need to purchase higher tiers if they exceed limits.

LiteLLM pricing (2026)

LiteLLM’s core SDK and self-hosted proxy are open source (MIT) and free. The Enterprise tier starts at $5K/year and includes SSO, audit logs, priority support, and SLA. There are no usage-based tiers; the proxy can handle unlimited requests if self-hosted. This makes LiteLLM very cost-effective for high-volume organizations that can manage their own infrastructure.

Value-per-dollar: Langfuse vs LiteLLM

For small teams with low event volumes, Langfuse’s free cloud tier (50k events/month) provides excellent observability at no cost. LiteLLM’s free tier (self-hosted) is also free but requires infrastructure maintenance. For mid-sized teams needing both observability and multi-provider access, starting with Langfuse Pro ($59/month) and adding LiteLLM open source (free) is a common stack. For large enterprises with high volume and compliance needs, LiteLLM Enterprise ($5K+/year) plus Langfuse Enterprise (custom pricing) provides a robust gateway and observability layer. Overall, LiteLLM offers better value if you need multi-provider routing at scale; Langfuse provides superior observability per dollar for teams that primarily use one or two providers.

Who should pick which

Solo developer debugging an agent
Pick: Langfuse
Langfuse's free cloud tier (50k events/month) and structured traces help debug unexpected agent behavior with session views and step-level details.
Platform team managing 5 teams with different model providers
Pick: LiteLLM
LiteLLM proxy provides virtual keys, per-team budgets, and rate limits for each team, centralizing access and cost tracking across 100+ providers.
Mid-size startup needing observability + multi-provider flexibility
Pick: Langfuse
Langfuse Pro ($59/mo) offers evals and datasets; pair with LiteLLM open source for routing. But if budget forces one tool, Langfuse's free self-hosted version is more feature-rich for debugging.
Enterprise requiring SSO and audit logs for all LLM calls
Pick: LiteLLM
LiteLLM Enterprise ($5K+/year) includes SSO, audit logs, and SLA, meeting enterprise governance needs out of the box.

Frequently Asked Questions

Can I use Langfuse and LiteLLM together?

Yes, many teams use both. LiteLLM proxy can log all requests to Langfuse for tracing and evaluation. This gives you unified access (LiteLLM) plus observability (Langfuse) in one stack.

Which one is better for debugging LLM agents?

Langfuse is better for debugging agents. It captures hierarchical traces from frameworks like LangGraph, AutoGen, and OpenAI Agents SDK, showing step-level latency, cost, and inputs/outputs.

Which one is better for routing requests across multiple LLM providers?

LiteLLM is built for this. It provides a single OpenAI-compatible API for 100+ providers, with fallbacks, retries, and per-provider configuration.

Do either of these tools have a free tier?

Yes. Langfuse offers a free cloud tier (50k events/month) and a fully free self-hosted version. LiteLLM's SDK and self-hosted proxy are free; only the Enterprise tier (from $5K/year) costs.

What is the learning curve for Langfuse vs LiteLLM?

Both are developer-friendly. Langfuse requires wrapping LLM calls with a decorator; traces appear immediately in the dashboard. LiteLLM is a drop-in replacement for openai-python; just change the base URL. LiteLLM is slightly easier to start with if you already use OpenAI's SDK.

Can I self-host both tools?

Yes. Langfuse self-hosts via Docker Compose with Postgres, ClickHouse, and Redis. LiteLLM proxy self-hosts via Docker or pip with Postgres. Both are MIT licensed.

Which tool is better for cost tracking per team?

LiteLLM is better. It tracks cost per virtual key, team, and user, and can enforce monthly budgets. Langfuse tracks cost per trace but does not have per-team budgeting features.

Does Langfuse support evaluations?

Yes, Langfuse includes LLM-as-judge evals, user feedback capture, annotation queues, and dataset-based regression testing. LiteLLM does not have built-in evaluation workflows.

Does LiteLLM support prompt management?

No, LiteLLM does not include prompt management. Langfuse provides prompt versioning, deployment, and rollback. For prompt management, use Langfuse.

Are there any providers LiteLLM does not support that Langfuse can observe?

LiteLLM supports 100+ providers; Langfuse can observe any LLM call via its decorator or OpenTelemetry, regardless of provider. So Langfuse can observe calls to providers not in LiteLLM's list, but only if the call is instrumented.

Last reviewed: May 12, 2026