Langfuse vs LiteLLM

See which projects drive your usage

A new chart shows your organization's units over time, grouped by project or type, with a CSV export. On Langfuse Cloud it is on the Billing page; self-hosted deployments get a new Usage page.

Langfuse CLI 1.0 and new evaluator features

Langfuse CLI 1.0 released, along with new evaluator template gallery and reusable evaluators for production evaluations.

Deploying Langfuse Open-Source LLM Observability

This technical guide explains how to deploy Langfuse, an open-source observability platform for LLM applications, using Docker Compose. The deployment uses PostgreSQL for metadata, ClickHouse for trace and metrics analytics, Redis for cache/queueing, and S3-compatible object storage for media/exports, with Traefik and Let's Encrypt providing TLS. The article includes required prerequisites (Linux server 4 vCPU / 16GB RAM, Docker + Docker Compose, domain A record), step-by-step environment and docker-compose configuration, first-run setup (create organization/project and API keys), and a test-trace example using the Langfuse SDK and an OpenAI-compatible client. Publication date: 2026-08-12.

• Langfuse is an open-source observability platform for LLM applications that traces prompts/responses, tracks token usage and cost, and provides debugging analytics.
• The guide deploys Langfuse via Docker Compose using Traefik (TLS), PostgreSQL (metadata), ClickHouse (trace/metrics analytics), Redis (cache/queue), and S3-compatible object storage.
• Container images and versions shown include traefik:v3.7.0, postgres:17, clickhouse/clickhouse-server:26.5.1-alpine, and redis:7-alpine; Langfuse images used are langfuse/langfuse:3 and langfuse/langfuse-worker:3.

LiteLLM Release Carried Credential-Stealing Malware

A compromised release of LiteLLM, a widely used AI gateway, contained credential-stealing malware, marking one of the most significant supply chain attacks in recent weeks. The article discusses the broader implications for cybersecurity, highlighting how AI agents are increasingly used in both cyber attacks and defense. It provides guidance for security leaders, including prompts to audit AI key exposure and actions to secure accounts. The piece emphasizes the financial risk of stolen AI service keys, which can be used to purchase compute on the victim's bill. It also references emergency PHP fixes and a certificate authority's private key vulnerability, part of a 'Black October' wave of security incidents. The content is primarily oriented towards CIOs and CISOs, offering tactical advice and strategic talking points.

• A LiteLLM release contained credential-stealing malware.
• Microsoft documented AI-assisted attacks targeting hundreds of organizations.
• Emergency PHP fixes affect software beneath millions of websites.

Internet Faces Multiple Critical Security Threats

On October 21, 2026, multiple critical security vulnerabilities were disclosed, overwhelming cybersecurity teams. AI-discovered PHP vulnerabilities caused government websites worldwide, including in Europe, to go offline, prompting the NSA and PHP developers to issue urgent patches. Experts warn of a 'race to exploit' as AI can reverse engineer fixes. Additionally, Node.js patched four high-severity vulnerabilities, including remote code execution and TLS validation bypass; LiteLLM was compromised with malicious code on PyPI and Docker Hub; and Indian certificate authority eMudhra's private keys were compromised. Paul Krugman discusses these AI-powered threats, citing Zvi Mowshowitz's 'The Hackening' and Azeem Azhar's 'Wuhan moment,' suggesting a potential shift away from the open web to gated communities. He warns of short-term economic disruption but hopes for long-term productivity gains.

• AI-discovered critical PHP vulnerabilities on 2026-10-21 took government websites offline globally; urgent patches were issued.
• Node.js patched four high-severity vulnerabilities, including remote code execution and TLS validation bypass.
• LiteLLM was compromised with malicious code; malicious artifacts were published on PyPI and Docker Hub.

Introducing the LiteLLM ROI Calculator

Compare each engineer's LiteLLM gateway spend with the estimated effort of their merged pull requests. Open source and self-hosted.