Langfuse vs SigNoz

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.

Deploying SigNoz with ClickHouse v25 and OTel Gotchas

A developer describes troubleshooting and deployment patterns for self-hosting SigNoz in 2026, focusing on ClickHouse v25 configuration changes, OpenTelemetry Collector incompatibilities, and Docker network isolation. Key recommendations include using config.d/users.d for ClickHouse overrides (instead of replacing the main config), enforcing a deterministic docker-compose boot sequence with one-shot migrator containers (signoz_init_clickhouse and signoz_telemetrystore_migrator), updating exporter names and image tags for the OTel collector (use signozclickhousemetrics and include the 'v' prefix), and relying on internal Docker network isolation rather than per-component passwords to simplify connectivity. The post emphasizes that maintaining self-hosted observability infrastructure requires ongoing engineering effort.

• Author self-hosted SigNoz for Verne Software to control telemetry data.
• ClickHouse v25+ changes require using config.d/ and users.d/ for overrides instead of replacing the main config file.
• SigNoz deployments must run one-shot containers (signoz_init_clickhouse and signoz_telemetrystore_migrator) to install UDFs and create required databases before starting collectors.

Integrating AI Agents with Self-Hosted SigNoz

An engineer describes building ArcNet to instrument and monitor an AI agent fleet using a self-hosted SigNoz instance. The write-up covers installation (SigNoz v0.133.0 via foundryctl), lessons about verifying emitted OpenTelemetry attributes (the Agno instrumentor emitted OpenInference conventions rather than gen_ai.*), turning guardrail results into structured span attributes for alerting, the need to use SigNoz's v5 alerts queries payload, using raw ClickHouse SQL panels as an escape hatch, and the distinction between telemetry (traces in SigNoz/ClickHouse) and replayable session transcripts (stored separately in SQLite). The author notes SigNoz MCP was unreliable in their setup and links code on GitHub.

• Author built ArcNet on self-hosted SigNoz for the Agents of SigNoz hackathon.
• SigNoz v0.133.0 was used and installed with foundryctl (casting.yaml + casting.yaml.lock produced reproducible deployments).
• The Agno instrumentor (openinference-instrumentation-agno) emitted OpenInference semantic conventions rather than OpenTelemetry gen_ai.* attributes, causing initially empty dashboards.

Instrumenting MERN E‑Commerce with SigNoz

A developer case study describing how the author instrumented a MERN-stack e-commerce application called Ram Store with SigNoz and OpenTelemetry during the Agents of SigNoz Hackathon 2026. The project used a self-hosted SigNoz instance (Docker) and the OpenTelemetry Node SDK with automatic instrumentation for Express, HTTP, and MongoDB, exporting via OTLP gRPC. The instrumentation enabled distributed traces, runtime metrics, and structured logs (Winston) correlated inside SigNoz. The post covers setup steps, observed telemetry (traces, metrics, logs), challenges (Docker networking, configuration), lessons learned, and planned improvements like custom dashboards and alerting. Project source code and a demo video are linked.

• Author integrated SigNoz into a MERN e-commerce application named Ram Store as part of the Agents of SigNoz Hackathon 2026.
• SigNoz was self-hosted locally using Docker to provide observability dashboards.
• OpenTelemetry Node SDK with automatic instrumentation was configured for Express, HTTP, and MongoDB, using an OTLP gRPC exporter.