[{"data":1,"prerenderedAt":140},["ShallowReactive",2],{"seo-verification":3,"blog-self-host-langfuse-on-a-vps-llm-observability-no-saas-bill-en":6},{"google":4,"bing":5},"EycwPY2XMyTkVzas3n1ygeNJFGAH513qrMjfDljzsMQ","",{"id":7,"slug":8,"slugs":9,"title":12,"excerpt":13,"readTime":14,"views":15,"isPinned":16,"publishedAt":17,"category":18,"categories":24,"featuredImage":26,"bgImage":27,"posterImage":28,"relatedSolution":29,"intro":32,"sections":33,"ctaTitle":83,"ctaBody":84,"ctaButton":85,"ctaUrl":86,"relatedPosts":87},177,"self-host-langfuse-on-a-vps-llm-observability-no-saas-bill",{"fr":10,"en":8,"ar":11},"self-host-langfuse-vps","استضافة-langfuse-على-vps-مراقبة-llm-بلا-فاتورة-saas","Self-Host Langfuse on a VPS: LLM Observability, No SaaS Bill","Self-host Langfuse on a VPS — the open-source LLM observability platform. Trace every AI call, evaluate prompts, manage versions and track costs, no SaaS bill.",7,0,false,"2026-07-12T00:00:00+00:00",{"id":19,"name":20,"slug":21,"color":22,"icon":23},1,"Artificial Intelligence","intelligence-artificielle","bg-purple-500\u002F10 text-purple-400","ia",[25],{"id":19,"name":20,"slug":21,"color":22,"icon":23},null,"\u002Fblog\u002Fcovers\u002Fbg.svg","\u002Fblog\u002Fcovers\u002Fself-host-langfuse-vps-poster.svg",{"categorySlug":30,"appSlug":31},"artificial-intelligence","langfuse","Every LLM-powered application eventually produces a question its developers can't answer from logs alone: why did that response degrade, which prompt version performed better, and where is the money going? Langfuse (MIT, ~30 k GitHub stars, v3.212.0) is the open-source answer: a full-stack observability platform for LLM applications that you deploy on your own VPS and connect to any model provider in minutes.",[34,38,48,51,76,80],{"type":35,"title":36,"body":37},"h2","Why LLM applications need dedicated observability","Traditional APM tools (Datadog, New Relic) capture HTTP latency and error rates — but they are blind to what happens inside an LLM call. A response that arrives in 800 ms might still be factually wrong, unhelpfully vague, or three times more expensive than yesterday's because a prompt regression slipped through code review. Langfuse solves this by treating each LLM interaction as a structured trace: it records the full prompt (including system message and conversation history), the completion, the model used, the token counts, the latency breakdown per span, and any evaluation scores your team attaches. You can then filter, compare, and reproduce any trace — individually or in aggregate.",{"type":39,"title":40,"items":41},"ul","What Langfuse gives you out of the box",[42,43,44,45,46,47],"Full-stack LLM tracing: prompt, completion, latency, cost and token count — including nested spans for agent chains (LangChain, LlamaIndex, Dify).","Prompt management hub: version-control your prompts, stage variants, A\u002FB test in production, promote the winner without a code deploy.","Evaluation framework: run LLM-as-a-judge, human annotation queues, or custom scoring functions on any trace or dataset.","Cost analytics: track token spend by model, endpoint, user and session — switch providers with data, not guesswork.","Dataset management: capture production traces as golden test sets for offline evaluation and regression detection.","Native SDK for Python and TypeScript, plus automatic integration with LiteLLM, LangChain, LlamaIndex, Dify, Haystack and VercelAI.",{"type":35,"title":49,"body":50},"What Langfuse runs on (and why it needs 4 GB RAM)","Langfuse v3 ships as a six-service Docker Compose stack: `langfuse` (Next.js frontend + API), `langfuse-worker` (background jobs and evaluations), `postgres` (application state), `clickhouse` (trace analytics — columnar storage optimised for high-cardinality time-series), `redis` (queue and cache), and `minio` (S3-compatible blob storage for media attachments). ClickHouse is the reason for the 4 GB minimum: its JIT compiler and vector execution engine need headroom. On a quiet VPS the full stack idles around 1.5–2 GB; under moderate production load plan for 4 GB, with 8 GB for sustained high-throughput tracing. All six services start from a single `docker compose up -d` and are managed by AWX in the ServOrbit one-click flow.",{"type":52,"title":53,"steps":54},"steps","Deploy Langfuse on your VPS in six steps",[55,58,61,64,67,70,73],{"title":56,"body":57},"Order a ServOrbit VPS (4 GB RAM minimum)","Any Ubuntu 24.04 plan at 4 GB or above works. An 8 GB plan is recommended if you plan to run persistent production traffic. Pick a domain or subdomain you control — Langfuse's NextAuth session cookies require a proper HTTPS domain.",{"title":59,"body":60},"One-click install from the marketplace","Open your ServOrbit control panel, go to Marketplace → AI & LLM Tools → Langfuse, and click Deploy. Enter your domain when prompted. Docker Compose pulls all six images and starts them; the web UI is ready on port 3000 within 60–90 seconds (ClickHouse first-boot initialisation takes the longest).",{"title":62,"body":63},"Open the web UI and create your first project","Navigate to `https:\u002F\u002Fyour-domain.com`. Langfuse shows the signup screen on first boot. Create your admin account, then go to Settings → Projects → Create project. Copy the **Public Key** and **Secret Key** from the project settings — you'll need them in your application.",{"title":65,"body":66},"Instrument your Python or TypeScript application","In Python: `pip install langfuse`, set `LANGFUSE_HOST=https:\u002F\u002Fyour-domain.com`, `LANGFUSE_PUBLIC_KEY` and `LANGFUSE_SECRET_KEY`, then decorate your LLM calls with `@observe()` or use `langfuse.trace()`. In TypeScript: `npm install langfuse`, initialise the client with your host and keys. Traces appear in the dashboard within seconds of the first call.",{"title":68,"body":69},"Enable automatic tracing if you use LiteLLM","If LiteLLM is already in your stack (which it likely is if you use the ServOrbit AI stack), add `success_callback = [\"langfuse\"]` to `litellm_config.yaml` and set the three Langfuse env vars. Every proxied LLM call — across all downstream models — is traced automatically, with cost and token data attached.",{"title":71,"body":72},"Run your first evaluation","Go to Traces, filter for a representative sample, click 'Add to dataset'. Open Datasets → your dataset → Run evaluation, choose LLM-as-a-judge with a prompt template ('Rate the relevance of this response on a scale of 1–5'), and submit. Scores appear on every trace in the set and feed into the aggregate analytics dashboard.",{"title":74,"body":75},"Logging in for the first time","The URL opens the Langfuse screen with a “Sign up” link: create your account, then your organisation and your first project in the wizard.",{"type":77,"title":78,"body":79},"tip","Pair with LiteLLM for complete AI cost visibility","The ServOrbit AI stack already includes LiteLLM as an OpenAI-compatible gateway. Connect Langfuse to LiteLLM with three env vars and you get a complete picture: LiteLLM enforces rate limits and routes between providers; Langfuse records every trace with full prompt, completion, model, tokens and cost. Together they give you a private, auditable AI operations layer — no SaaS middleman, no data egress.",{"type":35,"title":81,"body":82},"Langfuse vs Langsmith vs Helicone","Langsmith (LangChain's hosted product) and Helicone are the main SaaS alternatives. Both are excellent — and both require you to route your traces through their servers. For teams handling sensitive prompts (legal, financial, medical), or for compliance environments that prohibit third-party data egress, self-hosting is not optional. Langfuse gives you the same feature set (tracing, evals, prompt management, cost analytics) on infrastructure you control. The MIT licence also means you can read, modify and audit every line of the code your traces flow through.","Deploy Langfuse on your VPS","Get full LLM observability in one click — trace every AI request, manage your prompts and track costs, on infrastructure you control.","Deploy Langfuse","\u002Fmarketplace\u002Fartificial-intelligence\u002Flangfuse",[88,108,124],{"id":89,"slug":90,"slugs":91,"title":94,"excerpt":95,"readTime":96,"views":15,"isPinned":16,"publishedAt":97,"category":98,"categories":103,"featuredImage":26,"bgImage":27,"posterImage":105,"relatedSolution":106},156,"self-host-litellm-on-a-vps-your-private-llm-api-gateway",{"fr":92,"en":90,"ar":93},"self-host-litellm-vps","استضف-litellm-ذاتيا-على-vps-بوابة-api-خاصة-لنماذج-llm","Self-Host LiteLLM on a VPS: Your Private LLM API Gateway","Deploy LiteLLM on a ServOrbit VPS — a self-hosted, OpenAI-compatible gateway to Ollama, Anthropic, Azure and 100+ LLM providers, with spend tracking and rate limits.",4,"2026-01-21T00:00:00+00:00",{"id":14,"name":99,"slug":100,"color":101,"icon":102},"Self-hosting","self-hosting","bg-indigo-500\u002F10 text-indigo-400","cloud",[104],{"id":14,"name":99,"slug":100,"color":101,"icon":102},"\u002Fblog\u002Fcovers\u002Fself-host-litellm-vps-poster.svg",{"categorySlug":30,"appSlug":107},"litellm",{"id":109,"slug":110,"slugs":111,"title":114,"excerpt":115,"readTime":116,"views":15,"isPinned":16,"publishedAt":117,"category":118,"categories":119,"featuredImage":26,"bgImage":27,"posterImage":121,"relatedSolution":122},12,"how-to-host-localai-on-a-vps",{"fr":112,"en":110,"ar":113},"heberger-localai-vps","كيفية-استضافة-localai-على-خادم-vps","How to Host LocalAI on a VPS","Deploy LocalAI on your VPS as a drop-in replacement for the OpenAI API. Docker, GGUF models, chat, embeddings, images and reverse proxy.",3,"2026-06-08T00:00:00+00:00",{"id":19,"name":20,"slug":21,"color":22,"icon":23},[120],{"id":19,"name":20,"slug":21,"color":22,"icon":23},"\u002Fblog\u002Fcovers\u002Fheberger-localai-vps-poster.svg",{"categorySlug":30,"appSlug":123},"local-ai",{"id":125,"slug":126,"slugs":127,"title":130,"excerpt":131,"readTime":14,"views":15,"isPinned":16,"publishedAt":132,"category":133,"categories":134,"featuredImage":26,"bgImage":27,"posterImage":136,"relatedSolution":137},176,"self-host-openobserve-on-a-vps-unified-logs-metrics-and-traces",{"fr":128,"en":126,"ar":129},"self-host-openobserve-vps","استضافة-openobserve-على-vps-سجلات-ومقاييس-وتتبعات-موحدة","Self-Host OpenObserve on a VPS: Unified Logs, Metrics and Traces","Replace Elasticsearch + Grafana with OpenObserve: one Rust binary for logs, metrics, traces and dashboards at 140× lower storage cost. Full self-host guide.","2026-07-10T00:00:00+00:00",{"id":14,"name":99,"slug":100,"color":101,"icon":102},[135],{"id":14,"name":99,"slug":100,"color":101,"icon":102},"\u002Fblog\u002Fcovers\u002Fself-host-openobserve-vps-poster.svg",{"categorySlug":138,"appSlug":139},"monitoring-observability","openobserve",1787581012818]