Companies that use Langfuse

Source: Analysis of 100 job postings that mention Langfuse

I noticed that Langfuse purchases are primarily driven by technical leadership in AI and data teams. Directors and Heads of Data Infrastructure, AI, and ML Engineering (6% of roles) are the buyers, focusing on building scalable AI platforms and establishing LLMOps practices across their organizations. These leaders are hiring rapidly to support AI-first product strategies, with 94% of postings being individual contributor roles. The strategic priority is clear: operationalizing generative AI at scale while maintaining quality, compliance, and cost control.

The day-to-day users are overwhelmingly Machine Learning Engineers (36%) and Backend Engineers (20%) who are building production LLM systems. These practitioners use Langfuse for observability, prompt management, evaluation pipelines, and monitoring AI agent performance. Data Scientists (9%) leverage it for experiment tracking and model evaluation. The work spans RAG implementations, multi-agent orchestration, and integrating LLMs into existing products. Multiple postings mention Langfuse alongside tools like MLflow, LangChain, and LangGraph, positioning it as essential infrastructure for LLM development.

The core pain point is operationalizing AI reliably. Companies need to "ensure outputs remain factual, safe, and clinically aligned" and implement "robust monitoring and alerting systems to ensure AI solutions which are robust and cost effective." They want "observability and governance" for production AI systems and seek to "continuously refine agent prompts" while tracking "groundedness, accuracy, relevance, faithfulness" metrics. The emphasis on evaluation, tracing, and production readiness reveals that these teams have moved past experimentation and need serious tooling to ship AI features confidently.

Source: Analysis of Linkedin bios of 402 companies that use Langfuse

I noticed that Langfuse customers are overwhelmingly companies building AI-powered products as core business functionality, not just experimenting with AI on the side. These aren't traditional software companies adding a chatbot. They're building AI-native solutions: conversational intelligence platforms, AI-powered compliance tools, personalized video generation at scale, voice AI systems, and intelligent document processing. Many are in regulated industries like healthcare, financial services, and legal tech where AI reliability and observability matter intensely.

The funding and size data shows a sweet spot in the scaling phase. While there are a few large enterprises mixed in, the typical company has 11-200 employees and has raised seed to Series A funding. I see multiple companies in that critical growth moment: they've proven product-market fit, they're expanding beyond initial customers, and they're now operationalizing AI at scale. Companies like AIMon (pre-seed, 8 employees), Zango (seed, 14 employees), and Eyva (seed, 26 employees) represent the earlier end, while firms like Piano (Series D, 773 employees) show where successful ones scale to.

Source: Analysis of tech stacks from 402 companies that use Langfuse

I noticed something striking about Langfuse users: they're building serious AI infrastructure with a strong engineering-first mindset. The combination of N8N for workflow automation, HuggingFace for model deployment, and multiple monitoring tools tells me these are companies actually shipping AI products to customers, not just experimenting. They need observability and testing because they have real users depending on their systems.

The pairing of N8N and Langfuse is particularly revealing. N8N suggests these teams are automating complex workflows that involve multiple AI calls and integrations. They need Langfuse to trace what's happening across those chains when something goes wrong or costs spike. Similarly, the heavy presence of Metabase and Grafana shows these companies are obsessed with metrics and visibility. They're monitoring both their AI performance and their business metrics closely, which makes sense when you're burning tokens on every customer interaction.

The full stack screams product-led growth at early to mid-stage startups. These aren't enterprise companies with massive sales teams. The tools are open-source or developer-focused, suggesting lean engineering teams that need to move fast. Portainer and SonarQube indicate they're running containerized infrastructure and care deeply about code quality. They're probably 10 to 50 person teams building AI-native products where the AI isn't a feature but the core product itself.