---
title: Press & Media
description: Official press page for Langfuse, part of ClickHouse — the open source AI engineering platform. Latest news, company information, media assets, and press contacts.
---
# Press & Media
Want to talk about press opportunities? Email clemens@langfuse.com and we'll be in touch shortly. To keep in touch with Langfuse, follow us on [Twitter](https://x.com/langfuse), [LinkedIn](https://linkedin.com/company/langfuse), [GitHub](https://github.com/langfuse), and [Discord](https://langfuse.com/discord).
## Company Facts
Langfuse is the open source AI engineering platform, now part of [ClickHouse](https://clickhouse.com). Teams use Langfuse to trace, evaluate, and improve LLM applications and AI agents in production. The platform provides:
- **Observability & Tracing** — Production tracing, metrics, and analytics for LLM applications
- **Prompt Management** — Version control and deployment of prompts with integrated monitoring
- **Evaluations** — LLM-as-a-Judge evaluations, datasets, and structured evaluation processes
- **Collaboration** — Shareable views, custom dashboards, and team collaboration features
### Company
- **Founded:** 2023
- **Acquired by ClickHouse:** January 2026 ([announcement](/blog/joining-clickhouse))
- **Headquarters:** Berlin, Germany (Product & Engineering) and San Francisco, CA (GTM)
- **Y Combinator:** Winter 2023 batch (W23)
- **Team Size:** 16+ full-time employees
- **Open Source License:** MIT
- **Public Company Handbook:** [langfuse.com/handbook](/handbook/chapters/story)
### Funding History
- **$4M Seed Round** (November 2023) — Led by Lightspeed Venture Partners, General Catalyst (La Famiglia), and Y Combinator
- **Acquired by ClickHouse** (January 2026)
### Most Important Links
- **Acquisition Announcement:** [langfuse.com/blog/joining-clickhouse](/blog/joining-clickhouse)
- **Interactive Demo:** [langfuse.com/docs/demo](/docs/demo)
- **Company Handbook:** [langfuse.com/handbook](/handbook/chapters/story)
- **Changelog:** [langfuse.com/changelog](/changelog)
- **Enterprise:** [langfuse.com/enterprise](/enterprise)
- **Documentation:** [langfuse.com/docs](/docs)
- **Roadmap:** [langfuse.com/docs/roadmap](/docs/roadmap)
- **Blog:** [langfuse.com/blog](/blog)
## Why does Langfuse exist?
- Langfuse exists to accelerate the deployment of reliable, safe, explainable and cost-effective AI applications and agents.
- AI will create meaningful value for society and drive economic growth and we are still in the early days of seeing this impact. Over time, every successful company will be an AI company, with AI at the core of its strategy, value creation, and business processes. Most value creation will happen at the application-layer, split between incumbents and AI-native startups.
We're building an [integrated](/integrations) and [open](/handbook/chapters/open-source) tooling layer to help teams with:
- **Visibility & explainability** — _Langfuse Observability_ (production tracing, metrics, and analytics)
- **Collaboration across disciplines** — _Prompt management, shareable views & custom dashboards_
- **Evaluation & data operations** — _Langfuse Evaluations (evals, datasets, labeling)_
Langfuse is vendor-neutral, based on [OpenTelemetry](/integrations/native/opentelemetry), and available as cloud or self-hosted at production scale.
## Why do customers choose Langfuse?
Customers choose Langfuse because it is:
- The most used open-source AI Engineering platform [(blog post)](/blog/2024-11-most-used-oss-llmops)
- Model and framework agnostic with [100+ integrations](/integrations)
- Built for production and scale, backed by ClickHouse
- Designed for complex agents and multi-step workflows
- A complete toolbox for AI Engineering
- Incrementally adoptable — start with one feature and expand to the full platform over time
- API-first — all features are available via API for custom integrations
- Based on [OpenTelemetry](/integrations/native/opentelemetry) for interoperability
- Easy to [self-host](/self-hosting)
Langfuse is the most widely adopted LLM Engineering platform:
- Used by **50,000+** companies
- **34,321** GitHub stars
- **65M+** SDK installs per month
- **38M+** Docker pulls
- Trusted by **21 of the Fortune 50** and **129 of the Fortune 500**
Companies who trust Langfuse:
## Key Milestones [#key-milestones]
A more detailed timeline is available in our [handbook](/handbook/chapters/story).
### 2023
- **January:** Joined Y Combinator W23 batch, moved to San Francisco
- **August:** [Public launch](https://www.ycombinator.com/launches/J2s-langfuse-open-source-product-analytics-for-llm-apps) as open source project — Product Hunt Product of the Day
- **November:** Raised [$4M seed round](https://langfuse.com/blog/announcing-our-seed-round) led by Lightspeed Venture Partners, La Famiglia, and Y Combinator; Reached 1,000 GitHub Stars
### 2024
- **January:** Launched [first version](https://langfuse.com/changelog/2024-01-03-prompt-management) of Prompt Management feature
- **April:** Langfuse 2.0 launch — "The AI Engineering Platform" with evaluations, datasets, and playground. Product Hunt Product of the Day again
- **December:** [Langfuse v3](https://langfuse.com/blog/2024-12-langfuse-v3-infrastructure-evolution) — Major infrastructure upgrade, migrating the core data layer from PostgreSQL to ClickHouse
### 2025
- **February:** Opened San Francisco office
- **April:** Reached 10,000 GitHub Stars
- **May:** [Launch Week 3](https://langfuse.com/blog/2025-05-19-launch-week-3) with full-text search, saved/shared table views, and custom dashboards
- **June:** [Open sourced](https://langfuse.com/blog/2025-06-04-open-sourcing-langfuse-product) all product features under MIT license (LLM-as-a-Judge, playground, experiments, annotations)
- **June–August:** Shipped OpenTelemetry-based Python SDK v3 and JS SDK v4
- **August:** [Featured in Handelsblatt](https://www.handelsblatt.com/technik/ki/langfuse-dieses-start-up-analysiert-wie-gut-ki-agenten-wirklich-sind/100139548.html) covering the Langfuse story and journey
- **October:** Launch Week 4 with advanced filtering, team collaboration features, and Mixpanel integration
### 2026
- **January:** [ClickHouse acquired Langfuse](/blog/joining-clickhouse) — full team joined ClickHouse to keep building Langfuse with more resources behind performance, reliability, and enterprise readiness
- **March:** Shipped [observations-centric data model](/blog/2026-03-10-simplify-langfuse-for-scale) — 10x+ dashboard performance improvement, laying the groundwork for Langfuse v4
## The Team
The Langfuse team is part of ClickHouse and continues to build the Langfuse platform from Berlin and San Francisco.
| Name | Role | Social Links |
| ---------------------- | -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
| **Marc Klingen** | Co-Founder & CEO | [X](https://x.com/marcklingen), [LinkedIn](https://www.linkedin.com/in/marcklingen/), [GitHub](https://github.com/marcklingen) |
| **Max Deichmann** | Co-Founder & CTO | [X](https://x.com/maxdeichmann), [LinkedIn](https://www.linkedin.com/in/maxdeichmann/), [GitHub](https://github.com/maxdeichmann) |
| **Clemens Rawert** | Co-Founder & COO | [X](https://x.com/rawert), [LinkedIn](https://www.linkedin.com/in/rawert/), [GitHub](https://github.com/clemra) |
| **Marlies Mayerhofer** | Founding Engineer | [X](https://x.com/marliessophie), [LinkedIn](https://www.linkedin.com/in/marlies-mayerhofer/), [GitHub](https://github.com/marliessophie) |
| **Hassieb Pakzad** | Founding Engineer | [X](https://x.com/hassiebpakzad), [LinkedIn](https://www.linkedin.com/in/hassieb/), [GitHub](https://github.com/hassiebp) |
| **Steffen Schmitz** | Backend Engineer | [LinkedIn](https://www.linkedin.com/in/steffenschmitz/), [GitHub](https://github.com/Steffen911) |
| **Jannik Maierhöfer** | Growth Engineer | [X](https://x.com/jmaierhoefer), [LinkedIn](https://www.linkedin.com/in/maierhoefer/), [GitHub](https://github.com/jannikmaierhoefer) |
| **Akio Nuernberger** | Enterprise & Partnerships | [X](https://x.com/AkioNuernberger), [LinkedIn](https://www.linkedin.com/in/akio-nuernberger/) |
| **Nimar Blume** | Product Engineer | [X](https://x.com/nimarblu), [LinkedIn](https://www.linkedin.com/in/nimar/), [GitHub](https://github.com/nimarb) |
| **Valeriy Meleshkin** | Backend Engineer | [LinkedIn](https://www.linkedin.com/in/valeriy-meleshkin-5610ab58), [GitHub](https://github.com/sumerman) |
| **Lotte Verheyden** | Product Marketing Engineer | [X](https://x.com/lotte_verheyden), [LinkedIn](https://www.linkedin.com/in/lotteverheyden/), [GitHub](https://github.com/Lotte-Verheyden) |
| **Leonard Wolters** | Operations | [LinkedIn](https://www.linkedin.com/in/leonardwolters/), [GitHub](https://github.com/leonardwolters) |
| **Annabell Schäfer** | Product Marketing Engineer | [X](https://x.com/annabellschfr), [LinkedIn](https://www.linkedin.com/in/annabell-schaefer/), [GitHub](https://github.com/annabellscha) |
| **Tobias Wochinger** | Product Engineer | [LinkedIn](https://www.linkedin.com/in/tobiaswochinger/), [GitHub](https://github.com/wochinge) |
| **Ben Bachem** | Product Engineer | [LinkedIn](https://www.linkedin.com/in/ben-bachem/), [GitHub](https://github.com/bezbac) |
| **Mark Salpeter** | Product Engineer | [X](https://x.com/marksalpeter), [LinkedIn](https://www.linkedin.com/in/marksalpeter/), [GitHub](https://github.com/marksalpeter) |
| **Caleb Seeling** | Support Engineer | [LinkedIn](https://www.linkedin.com/in/calebloveseeling/), [GitHub](https://github.com/CodeCLS) |
| **Niklas Semmler** | Backend Engineer | [LinkedIn](https://www.linkedin.com/in/nsemmler/), [GitHub](https://github.com/niklassemmler) |
| **Nikita Kabardin** | Frontend Engineer | [X](https://x.com/nkabardin), [LinkedIn](https://www.linkedin.com/in/nkabardin/), [GitHub](https://github.com/nkabardin) |
| **Vera Yap** | Recruiting | [LinkedIn](https://www.linkedin.com/in/verayap/) |
| **Milana Gurbanova** | Integrations Engineer | [LinkedIn](https://www.linkedin.com/in/milana-gurbanova/), [GitHub](https://github.com/milanagm) |
| **Lysander Kiesel** | Product Engineer | [X](https://x.com/LysanderKie), [LinkedIn](https://www.linkedin.com/in/lysander-kiesel/), [GitHub](https://github.com/LysanderKie) |
| **Hande Kafkas** | Growth Engineer | [X](https://x.com/HandexxK), [LinkedIn](https://www.linkedin.com/in/handekafkas/), [GitHub](https://github.com/hande-k) |
| **You?** | Your life's work | [Open Positions](https://jobs.ashbyhq.com/langfuse) |
## Media Assets
### Logos
Official Langfuse logos, wordmarks, and downloadable brand packages are on the [Brand Assets & Guide](/brand) page.
### 2023: Founders during Y Combinator W23
### 2025: Team Offsite in Portugal
### 2026: Founder team
2026: Clemens
2026: Marc
2026: Max
## User Stories
Learn how leading companies use Langfuse to build production-grade AI applications.
### [How Canva built an Agentic Support Experience using Langfuse](/users/canva)
*Canva*
> "Langfuse hits the sweet spot between engineering requirements and empowerment of non-technical users to contribute their domain expertise."
### [Khan Academy uses Langfuse's AI Engineering platform to build Khanmigo AI](/users/khan-academy)
*Khan Academy*
> "Langfuse has enabled our developers to get extremely fast feedback. It's fundamental to how our developers understand their AI implementations."
### [Building AI Design Tools at Scale](/users/magic-patterns-ai-design-tools)
*Magic Patterns*
> "When a customer pings us, we instantly open Langfuse. This allows keeping support resolution under 8 minutes."
### [How Merck is serving 80+ AI project teams globally on Langfuse](/users/merckgroup)
*Merck*
> "Langfuse enables us to track every prompt, response, cost, and latency in real time, turning black-box models into auditable, optimizable assets."
### [AI-Powered first level support for 4 million merchants](/users/sumup)
*SumUp*
> "Building on Langfuse we saved 30% of external BPO cost by deflecting 50% of support conversations to AI."
### [How Hugging Face Scales Open-Source Model Discovery with Agents and Langfuse](/users/hugging-face)
*Hugging Face*
> "In just a few minutes you're up and running, and you're already able to see what's going on."
### [How Cresta traces multi-service AI agent pipelines with Langfuse](/users/cresta)
*Cresta*
> "Langfuse has made it a lot easier for us at Cresta to iterate on LLM behavior. It has sped up how quickly we can test, learn, improve the model, and build great AI agents."
### [How Ravenna uses Langfuse to build and debug AI agents for enterprise internal support](/users/ravenna)
*Ravenna*
> "Building a product like ours would be impossible without Langfuse. Our agents handle hundreds of different systems and request types across different companies. Knowing what they do and knowing if it is going well is irreplaceable."
### [From a manual spreadsheet to 12x higher experimentation velocity: how Slite observes and evaluates its AI agents with Langfuse](/users/slite)
*Slite*
> "Having this data set, having this whole test suite that a developer can run on their machine and quickly get results, it's a game changer for us."
### [How Evolve built a voice AI sleep coach, traced and evaluated on Langfuse](/users/evolve)
*Evolve*
> "I always say it's like the CMS of prompts. It's a game changer."
---
## Agent Instructions
This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/press.md`.
### Querying these docs
If the answer is not on this page, query the documentation instead of guessing:
- **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question:
```bash
curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?"
```
- **Index of every page**: , with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt).
### Before writing Langfuse code
- **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results.
- **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application.
- **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples.
- **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api `) to read or write traces, prompts, datasets, and scores from the terminal.
Found an error in these docs? Please open an issue at .