Langfuse Custom Dashboards - Get deep insights and evaluate your LLM application data

Having all debugging and analysis tools integrated in one platform saves so much time and hassle.

Hey Product Hunt 👋

I’m Clemens, co-founder of Langfuse. We are so excited to be live on Product Hunt again today, launching one of the most requested features yet: Custom Dashboards!

Building useful LLM applications and agents requires constant iteration and monitoring. Langfuse helps with tracing, evaluation, and prompt engineering tools that we’ve launched over the past two years (see our previous launches).

Custom Dashboards turn raw LLM traces into actionable insights in seconds. Spin up and save custom views that show the numbers you care about and keep every team on top of what matters most. This includes quality, cost, and latency metrics.

We work with thousands of teams building leading LLM applications. Based on this experience, we are launching a set of Curated Dashboards to help you get started:

• Cost Management: Average cost per user, total cost per model provider

• Latency Monitoring: P95 latency per model vendor, slowest step in agent applications

• Evaluation: User feedback tracking, LLM-as-a-judge values (correctness, hallucinations, etc.)

• Prompt Metrics: Identify high-performing prompts and prompt changes that caused issues

And because insights shouldn’t stay locked in a UI, we’re introducing a Query API endpoint. All traces visible in Langfuse can now be fetched and aggregated via this endpoint and piped into any downstream application. This enables you to:

• Build embedded analytics directly into your application

• Consume metrics in your analytics stack

• Power features like rate limiting or billing for your own users

Try it:

• ⭐ GitHub: https://github.com/langfuse/langfuse

• ⏯️ Interactive Demo: https://langfuse.com/demo

• 📒 Docs: https://langfuse.com/docs

• 💬 Discord: https://langfuse.com/discord

A few other things you get with Langfuse:

• 👣 Tracing: SDKs (TS, Python) + OTel + integrations for OpenAI, LangChain, LiteLLM & more

• ✏️ Prompt Management: version, collaborate, and deploy prompts

• ⚖️ Evaluation: dataset experiments, LLM-as-judge evals, prompt experiments, annotation queues

• 🕹️ Playground: iterate on prompts, simulate tool use and structured outputs

• 🧑‍🤝‍🧑 Community: thousands of builders in GitHub Discussions & Discord

Thanks again, PH community—your feedback shaped this release. We’ll be here all day; show us the dashboards you find most insightful and let’s keep building!

So excited for this launch folks! The Langfuse is truly one of the most impressive I've followed in ages, constantly listening and building the right tools for AI builders, this is just another milestone on this journey 🔥

Let's go! We love Langfuse to track ever little step of our ai agents even though Langfuse tells us that we spent way too much money on Anthropic and OpenAI :) Excited about creating our own dashboards to get the insights we need immediately

I hope every AI startup team improves performance with lower cost, with Langfuse!

Thanks for your effort :)

This update is so useful, I want to tell our technology partners about it quickly. I'm looking forward to seeing this feature updated in the system soon

Powerful all-in-one platform for LLM debugging and analytics! Love how it streamlines evaluation and iteration—essential for teams building AI apps. The custom dashboards are a game-changer. Excited to try this!

We've been using @Langfuse to give us visibility into all of the AI products we're building at @Pipedream (mcp.pipedream.com and string.com) and their product is awesome. I know integrating it was quick for our eng team, and the insight it provides is super powerful.

this is super useful... can this work for custom GPTs and the like?