Langfuse
Review
Langfuse helps AI engineers inspect and improve LLM-powered applications and agents. It combines end-to-end traces, prompt versioning and testing, datasets and evaluations, latency and token-cost analytics, and production monitoring. It can be used as a managed cloud service or self-hosted; it observes and evaluates agents rather than hosting their runtime.
Our take
A strong shortlist for teams that need to debug, evaluate and control the cost of LLM applications and agents. Especially attractive when open-source self-hosting matters; it is an observability and evaluation layer, not an agent runtime.
What it does
Langfuse helps AI engineers inspect and improve LLM-powered applications and agents. It combines end-to-end traces, prompt versioning and testing, datasets and evaluations, latency and token-cost analytics, and production monitoring. It can be used as a managed cloud service or self-hosted; it observes and evaluates agents rather than hosting their runtime.
Who should use it
AI engineers and product teams building LLM applications or agents who need visibility into quality, latency, model usage, cost, prompt changes, and evaluation results.
Who should think twice
Requires instrumentation or integrations in the application before traces are useful
Worth trying?
Yes — a free plan is listed, so you can test the workflow before paying.
Before you choose
TryThatTool Assessment
Our assessment brings together the tool’s capabilities, use cases, pricing, strengths and limitations. Genuine hands-on testing earns a dated Tested scorecard.
TryThatTool AssessedRecommendation: This page is a TryThatTool assessment, not a claim that we personally tested every workflow.
Key capabilities
These are listed product capabilities for discovery, not claims that TryThatTool has hands-on tested every feature.
Core capability
Workflow fit
Catalog signal
Practical usability
Discovery signal
Product fit
Useful for
- Traces nested LLM calls, tools, agents, retrieval, embeddings and evaluators in one workflow
- Prompt versioning, playground and controlled releases
- Datasets, experiments, online/offline evaluation and LLM-as-a-judge workflows
- Tracks token usage, latency and inferred or ingested model costs
- MIT-licensed core with free self-hosting option
- Requires instrumentation or integrations in the application before traces are useful
- Cloud usage is metered in units that include traces, observations and scores
- Does not host or execute AI agents; it monitors and evaluates them
- Production retention, throughput and security features vary by plan
Frequently asked questions
What is Langfuse used for?
Langfuse helps AI engineers inspect and improve LLM-powered applications and agents. It combines end-to-end traces, prompt versioning and testing, datasets and evaluations, latency and token-cost analytics, and production monitoring. It can be used as a managed cloud service or self-hosted; it observes and evaluates agents rather than hosting their runtime.
Does Langfuse have a free plan?
Our current listing indicates that Langfuse has a free plan. Check the official pricing page for current limits and feature availability before relying on it.
How much does Langfuse cost?
The listed starting price is Free; Cloud Core from $29/month. Our pricing classification is: Cloud Hobby free (50,000 units/month, 2 users); Core $29/month; Pro $199/month; Enterprise $2,499/month; self-hosted OSS free under MIT license.. Prices and plan limits can change, so confirm the current amount on the official website.
Who is Langfuse best for?
AI engineers and product teams building LLM applications or agents who need visibility into quality, latency, model usage, cost, prompt changes, and evaluation results.
What are the alternatives to Langfuse?
Alternatives listed on this page include Cursor, OpenAI Codex, Claude Code. Compare their use cases, pricing, and limitations before choosing.
Community reviews
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