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Route OpenAI-compatible vLLM endpoints through LiteLLM and add traces, latency, errors and usage visibility with Langfuse.
Before running commands in production, validate versions, backups, firewall rules and the rollback plan on your own infrastructure.
Clients see only the gateway; LiteLLM applies routing/policy, vLLM serves inference and Langfuse remains an independent observability layer. The gateway is CPU/RAM-oriented, vLLM GPU/VRAM-oriented and Langfuse storage/analytics-I/O oriented.
Beyond install commands, this guide covers architecture, capacity, security, troubleshooting and production operations as one workflow.
Clients see only the gateway; LiteLLM applies routing/policy, vLLM serves inference and Langfuse remains an independent observability layer.
Do not approve the Langfuse + LiteLLM + vLLM Observability Stack design merely because every service starts. Centralize API keys at the gateway, avoid direct public exposure of vLLM backends and restrict telemetry by role. Validate the real network and data path against Langfuse GitHub documentation before production.
The gateway is CPU/RAM-oriented, vLLM GPU/VRAM-oriented and Langfuse storage/analytics-I/O oriented.
For 502/504 chains, measure logs and latency at each hop before simply increasing gateway timeouts. Capacity testing should therefore use representative data and concurrent work on Langfuse + LiteLLM + vLLM Observability Stack; idle RAM alone is not a sizing decision.
The gateway is CPU/RAM-oriented, vLLM GPU/VRAM-oriented and Langfuse storage/analytics-I/O oriented. For GPU-accelerated workloads, benchmarks are not comparable unless model/data, concurrency and measurement window remain identical.
Keep the model/data, concurrency and measurement window identical across comparisons. For 502/504 chains, measure logs and latency at each hop before simply increasing gateway timeouts. Record failure rate and peak resource usage next to throughput.
Centralize API keys at the gateway, avoid direct public exposure of vLLM backends and restrict telemetry by role.
Access control for Langfuse + LiteLLM + vLLM Observability Stack is an architectural input rather than a post-deployment add-on. Clients see only the gateway; LiteLLM applies routing/policy, vLLM serves inference and Langfuse remains an independent observability layer. Database, worker, runtime or admin ports that do not need public exposure should remain private.
Health checks, timeouts, retries and fallback behavior must be tested under realistic load before production.
Use this operation as one release verification point: docker compose ps. For 502/504 chains, measure logs and latency at each hop before simply increasing gateway timeouts. If it fails, validate the rollback point before proceeding.
For 502/504 chains, measure logs and latency at each hop before simply increasing gateway timeouts.
To separate symptoms from root cause in Langfuse + LiteLLM + vLLM Observability Stack, record the last change first. The gateway is CPU/RAM-oriented, vLLM GPU/VRAM-oriented and Langfuse storage/analytics-I/O oriented. Then correlate service logs, dependency health and network reachability on the same timeline.
Health checks, timeouts, retries and fallback behavior must be tested under realistic load before production.
Health checks, timeouts, retries and fallback behavior must be tested under realistic load before production. Keep configuration, persistent data, secret inventory and restore order as separate runbook items, and review Langfuse GitHub release guidance before upgrades.
Route OpenAI-compatible vLLM endpoints through LiteLLM and add traces, latency, errors and usage visibility with Langfuse.
Choose Langfuse + LiteLLM + vLLM Observability Stack against the actual objective rather than product popularity: Route OpenAI-compatible vLLM endpoints through LiteLLM and add traces, latency, errors and usage visibility with Langfuse. The gateway is CPU/RAM-oriented, vLLM GPU/VRAM-oriented and Langfuse storage/analytics-I/O oriented. If those conditions are not yet known, start with a smaller PoC.
Clients see only the gateway; LiteLLM applies routing/policy, vLLM serves inference and Langfuse remains an independent observability layer. The gateway is CPU/RAM-oriented, vLLM GPU/VRAM-oriented and Langfuse storage/analytics-I/O oriented.
| Symptom / problem | Likely layer | First verification |
|---|---|---|
| Traces arrive but do not appear in the UI | For 502/504 chains, measure logs and latency at each hop before simply increasing gateway timeouts. | Correlate the relevant service log, dependency health and the last change on one timeline. |
| ClickHouse write queue becomes slow | The gateway is CPU/RAM-oriented, vLLM GPU/VRAM-oriented and Langfuse storage/analytics-I/O oriented. | Measure peak resources, concurrency and disk/network pressure in the same test window. |
| Worker stops processing events | Centralize API keys at the gateway, avoid direct public exposure of vLLM backends and restrict telemetry by role. | Verify public/private ports, authentication, TLS and secret scope from outside in. |
| Disk keeps growing after retention changes | Health checks, timeouts, retries and fallback behavior must be tested under realistic load before production. | Check version, config diff, persistent data and the rollback point together. |
Beyond install commands, this guide covers architecture, capacity, security, troubleshooting and production operations as one workflow.
Route OpenAI-compatible vLLM endpoints through LiteLLM and add traces, latency, errors and usage visibility with Langfuse.
Clients see only the gateway; LiteLLM applies routing/policy, vLLM serves inference and Langfuse remains an independent observability layer.
The gateway is CPU/RAM-oriented, vLLM GPU/VRAM-oriented and Langfuse storage/analytics-I/O oriented.
Centralize API keys at the gateway, avoid direct public exposure of vLLM backends and restrict telemetry by role.
Health checks, timeouts, retries and fallback behavior must be tested under realistic load before production.
For 502/504 chains, measure logs and latency at each hop before simply increasing gateway timeouts.
Beyond install commands, this guide covers architecture, capacity, security, troubleshooting and production operations as one workflow.
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curl -s http://127.0.0.1:8000/v1/modelsnvidia-smidocker compose psss -tulpnBeyond install commands, this guide covers architecture, capacity, security, troubleshooting and production operations as one workflow. The gateway is CPU/RAM-oriented, vLLM GPU/VRAM-oriented and Langfuse storage/analytics-I/O oriented.
Beyond install commands, this guide covers architecture, capacity, security, troubleshooting and production operations as one workflow.
Beyond install commands, this guide covers architecture, capacity, security, troubleshooting and production operations as one workflow.
Clients see only the gateway; LiteLLM applies routing/policy, vLLM serves inference and Langfuse remains an independent observability layer. The gateway is CPU/RAM-oriented, vLLM GPU/VRAM-oriented and Langfuse storage/analytics-I/O oriented.
Clients see only the gateway; LiteLLM applies routing/policy, vLLM serves inference and Langfuse remains an independent observability layer.
Centralize API keys at the gateway, avoid direct public exposure of vLLM backends and restrict telemetry by role.
The gateway is CPU/RAM-oriented, vLLM GPU/VRAM-oriented and Langfuse storage/analytics-I/O oriented.
Health checks, timeouts, retries and fallback behavior must be tested under realistic load before production.
For 502/504 chains, measure logs and latency at each hop before simply increasing gateway timeouts.
Route OpenAI-compatible vLLM endpoints through LiteLLM and add traces, latency, errors and usage visibility with Langfuse. Langfuse Self-hosting
Beyond install commands, this guide covers architecture, capacity, security, troubleshooting and production operations as one workflow. The gateway is CPU/RAM-oriented, vLLM GPU/VRAM-oriented and Langfuse storage/analytics-I/O oriented.