LangfuseReview

Overview

Langfuse is an open-source platform for watching and evaluating LLM applications. If your app sends prompts to a model and you need to know what happened, Langfuse keeps the trail. You'll see the prompt, the response, the token usage and how long it took.

The tracing is the foundation. Each request is logged as a structured trace, with the tool and retrieval steps included. That helps when something goes wrong and you need to find the step that caused it.

Langfuse also covers evaluation and prompt management. It is open source, so you can read how it works. You can score outputs with an LLM judge, run experiments, keep datasets, and build dashboards. It's built for LLM apps specifically, so it understands token usage and prompt-completion pairs without extra setup.

Key Features

• Application tracing. It captures prompts, responses, token usage, latency and tool or retrieval steps.
• Traces, sessions and observations. These are the core concepts for organizing what you see.
• Cost and token tracking. You can follow costs and token use through the observability features.
• AI engineering tools. LLM-as-a-Judge evaluation, prompt management, experiments, datasets and custom dashboards.
• Open source and self-hosted. You can run it yourself if you want the control.
• Free start. The Hobby plan is free, with no credit card needed to begin.
• Cloud and self-hosted pricing. The pricing page covers both options separately.

Pricing

Langfuse offers Hobby, Core, Pro and Enterprise plans. The pricing is based on monthly billable units, with some usage included in each plan. Extra units are billed separately, and Enterprise can use a yearly commitment. The pricing table on this page shows the current figures, so check them against your expected volume.

Pros

• If you need to see what each request did, structured traces give you that detail. You don't have to guess.
• Cost and latency problems are easier to track when token use and timing sit next to each trace.
• Evaluation workflows are included. You can judge outputs with LLM-as-a-Judge, run experiments and manage prompts.
• Teams that want to self-host can do it, because the project is open source.

Cons

• Pricing depends on units. Estimate your volume before you commit, or you'll be surprised by the first invoice.
• Core, Pro and Enterprise have different scopes and extra-unit rules. Read the plan rows before rollout.
• Enterprise buyers need a separate review, since the pricing page mentions yearly commitments.
• Langfuse is built for LLM apps, not for general infrastructure monitoring. Its own documentation describes it that way, so take the description at face value. Test the fit if you're replacing a general APM tool.

Best For

• AI product teams that need traces for prompts, responses, token use, latency and tool steps.
• Engineering teams that use LLM-as-a-Judge, prompt management, experiments, datasets or dashboards.
• Teams that want an open-source, self-hostable option for LLM observability.
• Buyers comparing observability and evaluation platforms on unit-based pricing.

vs Alternatives

Arize Phoenix and Braintrust are the alternatives named for observing and evaluating LLM apps. Compare their current product and pricing pages before you switch.

Verdict

Langfuse belongs in the AI observability and evaluation group. It covers tracing, prompt and response capture, token use, latency and cost, and it has the evaluation tools to go with them.

Start with the free Hobby plan. It's the easy way to see how your traces look. Then estimate your monthly units before you move to a paid plan. If you can self-host, the open source code gives you another option. Keep it simple at first.