RelayPlane vs Langfuse
Langfuse is a YC-backed LLM observability platform for tracing, evals, and prompt management. RelayPlane is an MIT-licensed npm proxy that intercepts LLM requests for real-time cost control and model routing. Here is how they compare for teams who need to govern their LLM spend.
TL;DR
Choose RelayPlane when you want:
- npm install and running in 30 seconds with no account or Docker
- Real-time cost control and model routing in the request path
- Local SQLite cost tracking with no data leaving your machine
- OpenAI-compatible drop-in with one baseURL swap, no code changes
- Claude Code and Cursor cost tracking without SDK instrumentation
Langfuse may work for you if you need:
- Full distributed tracing with nested spans across LLM calls, tools, and retrieval
- LLM-as-judge evals, user feedback collection, and annotation queues
- Server-side prompt management with versioning and A/B testing
- Dataset management and pre-deployment experiment pipelines
Feature Comparison
| Feature | RelayPlane | Langfuse |
|---|---|---|
| Product type RelayPlane sits in the critical path of every LLM request: it intercepts, routes, and logs each call. Langfuse is an asynchronous observability layer that collects traces after requests are made. Langfuse does not proxy or route requests. | npm-native LLM proxy and gateway (local-first) | LLM observability platform (tracing, evals, prompt management) |
| Install method RelayPlane ships as a standalone npm binary: one command and you are proxying requests. Langfuse's SDKs instrument your code to send traces to their cloud or a self-hosted Docker deployment. Self-hosting Langfuse requires Docker, Postgres, and infrastructure setup. | npm install -g @relayplane/proxy | pip install langfuse or npm install langfuse (SDK only), Docker for self-hosting |
| No account required RelayPlane starts with zero signup, zero credit card, and zero cloud dependency. Langfuse cloud requires an account (no credit card needed for free tier). Self-hosting Langfuse requires no account but does require Docker infrastructure. | ||
| Free tier RelayPlane has no request cap or free tier limit. Langfuse Hobby gives 50,000 trace units per month with a 2-seat limit and 30-day data retention window. One unit equals one trace, observation, or score. | MIT open source, no usage limits, fully free to self-host | Hobby: 50,000 units/mo, 2 users, 30-day data retention (account required) |
| Pricing (entry paid tier) RelayPlane has no paid cloud tier. Langfuse Core is $29/month. Their Pro plan is $199/month (100k units/mo, 3-year retention, SOC2/ISO27001). Enterprise cloud starts at $2,499/month. Overage is $8 per 100k additional units across all paid plans. | MIT open source, free self-hosted | $29/month (Core: 100k units/mo, unlimited users, 90-day retention) |
| Model routing and fallback RelayPlane routes requests to different models based on complexity and cost, with automatic fallback on provider failures. Langfuse does not route requests. Their own blog recommends pairing Langfuse with a gateway like LiteLLM if you need routing. | ||
| Request interception (proxy mode) RelayPlane intercepts every LLM request transparently via a baseURL swap. No code changes needed beyond pointing your client at localhost:4100. Langfuse requires SDK integration or OpenTelemetry instrumentation in your application code. | ||
| Local SQLite cost tracking RelayPlane logs every request's exact dollar cost in local SQLite with no data leaving your machine. Langfuse tracks token usage and estimated costs on their cloud platform or your self-hosted instance, but this is passive observation rather than active spend control. | ||
| No data leaves your machine RelayPlane runs entirely on localhost by default with zero external telemetry. Langfuse cloud sends all trace data to their platform. Self-hosted Langfuse keeps data on your infrastructure but requires Docker and Postgres to set up. | Self-host only (Docker required) | |
| Spend governance and budget limits RelayPlane can enforce spend limits and route away from expensive models when budgets are exceeded. Langfuse tracks cost as an observability metric but has no mechanism to block, reroute, or cap spending on live requests. | ||
| OpenAI-compatible drop-in RelayPlane exposes an OpenAI-compatible endpoint: set OPENAI_BASE_URL=http://localhost:4100 and your existing code works unchanged. Langfuse requires SDK calls or OTel instrumentation woven into your application logic. | ||
| Works with Claude Code and Cursor RelayPlane is designed for Claude Code, Cursor, Windsurf, and Aider with direct integration docs. Langfuse is an application-level SDK that does not integrate with AI coding assistants at the IDE or CLI level. | Not applicable (observability layer, not a proxy) | |
| LLM tracing and distributed traces Langfuse provides full distributed tracing: nested spans for LLM calls, tool calls, retrieval steps, embeddings, and custom spans. RelayPlane logs per-request metadata including model, tokens, cost, and latency, but does not produce distributed trace trees. | Basic request logging | |
| Evals and LLM-as-judge scoring Langfuse has a built-in eval pipeline with LLM-as-judge scoring, user feedback collection, manual annotation queues, and custom eval functions. RelayPlane focuses on cost control and routing rather than output quality evaluation. | ||
| Prompt management and versioning Langfuse provides server-side prompt management with version control, environment labels, and deploy-without-redeploy. You can run A/B tests on prompt versions and link production traces back to specific prompt versions. RelayPlane does not manage prompts. | ||
| Datasets and experiments Langfuse lets you build datasets from production traces and run systematic experiments to compare prompt versions, models, or pipeline changes before deploying. RelayPlane does not have a dataset or experiment feature. | ||
| Open source license Both products are open source. RelayPlane is MIT licensed. Langfuse's core is MIT licensed with enterprise features (SCIM, audit logging, advanced RBAC) requiring a $500/month commercial license key for self-hosted deployments. | MIT | MIT (core), commercial license for enterprise self-host features |
| SDK languages RelayPlane is an HTTP proxy so any language that can make HTTP requests works without a dedicated SDK. Langfuse has native Python and JS/TS SDKs with deep integrations for LangChain, LlamaIndex, Vercel AI SDK, and OpenTelemetry backends. | Node.js (proxy intercepts any language via HTTP) | Python and JavaScript/TypeScript native SDKs, plus 50+ framework integrations |
Why Teams Choose RelayPlane When They Need Cost Control, Not Just Observability
A proxy intercepts requests. An observability SDK instruments code. These are different problems.
Langfuse and RelayPlane are not direct substitutes. Langfuse is an asynchronous observability layer: you add SDK calls to your code, traces are collected after requests complete, and you get rich dashboards for debugging and evaluation. RelayPlane is a synchronous proxy that sits in the request path: you change one baseURL and every LLM call flows through it, enabling real-time routing, cost tracking, and spend governance. Langfuse's own engineering blog recommends pairing it with a gateway product if you need routing or cost control.
npm install vs Docker, Postgres, and infrastructure setup
npm install -g @relayplane/proxy and you are proxying requests in under 30 seconds. Self-hosting Langfuse requires spinning up Docker containers, a Postgres database, and configuring the full stack before you can record a single trace. Langfuse cloud is faster to start but sends all your trace data off-machine and requires an account. RelayPlane runs entirely on localhost with one command and zero cloud dependency.
Built-in cost control. Not just cost observation.
Langfuse is excellent at showing you what you spent. It tracks token counts, estimated costs by model and user, and surfaces cost trends in dashboards. But it cannot stop you from spending. RelayPlane tracks cost and can enforce it: budget limits, routing away from expensive models when thresholds are hit, automatic fallback on cost overruns. If you want to see your LLM spend, Langfuse helps. If you want to control it, you need a proxy in the request path.
No code changes. One baseURL swap.
RelayPlane requires zero application code changes. Set OPENAI_BASE_URL=http://localhost:4100 and your existing OpenAI SDK calls, Claude Code sessions, and Cursor requests are automatically proxied. Langfuse requires you to wrap your LLM calls with the Langfuse SDK or instrument your application with OpenTelemetry spans. For existing applications or coding assistants you do not control the source of, Langfuse instrumentation is simply not an option.
Langfuse Solves Observability Problems. RelayPlane Solves Cost Control Problems.
Langfuse is a genuinely excellent product for teams building production LLM applications who need deep tracing, eval pipelines, and prompt management. Its distributed trace view, LLM-as-judge scoring, and server-side prompt versioning are best-in-class. If your primary challenge is understanding why your LLM outputs are wrong or how to systematically improve prompt quality, Langfuse is worth evaluating.
But Langfuse cannot route requests, enforce budget limits, or intercept traffic from tools you do not control. It requires SDK instrumentation in your application code. Langfuse's own blog recommends pairing it with a proxy or gateway when you need those capabilities. If you want to start tracking LLM costs in Claude Code or Cursor today without touching your codebase, without Docker, without an account, and without sending logs to a third-party platform, RelayPlane installs in one command and runs on localhost.
Langfuse Pricing at a Glance
| Plan | Price | Units/mo | Retention | Seats |
|---|---|---|---|---|
| Hobby | Free | 50,000 | 30 days | 2 |
| Core | $29/mo | 100,000 | 90 days | Unlimited |
| Pro | $199/mo | 100,000 | 3 years | Unlimited |
| Enterprise | $2,499+/mo | 100,000+ | 3 years | Unlimited |
Get Running in 30 Seconds
No account. No Docker. No code changes: