RelayPlane vs Weights and Biases
Weights and Biases (W&B) is an MLOps platform for experiment tracking, model registry, and dataset versioning, with Weave as their LLM tracing and evaluation product. 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 building and governing production AI applications.
TL;DR
Choose RelayPlane when you want:
- npm install and running in 30 seconds with no account or SDK setup
- Real-time cost control and model routing in the request path
- Local SQLite cost tracking with no data leaving your machine
- Node.js and TypeScript native tooling, not a Python-first MLOps platform
- OpenAI-compatible drop-in with one baseURL swap, zero code changes
W&B may work for you if you need:
- Experiment tracking and run comparison for ML training workflows
- Model registry and dataset versioning with artifact lineage
- LLM tracing and evaluation pipelines via Weave
- Prompt version comparison with eval datasets for Python LLM apps
Feature Comparison
| Feature | RelayPlane | W&B |
|---|---|---|
| Product type RelayPlane sits in the critical path of every LLM request: it intercepts, routes, and logs each call transparently. Weights and Biases is an MLOps platform built for ML researchers to track training runs, compare experiments, and version datasets. Their Weave product adds LLM tracing and evals as an additional layer on top. W&B does not proxy or route requests. | npm-native LLM proxy and gateway (local-first) | MLOps platform for experiment tracking, model registry, dataset versioning, and LLM tracing (Weave) |
| Install method RelayPlane ships as a standalone npm binary: one command and you are proxying requests. W&B requires pip install, account creation, and wandb.init() calls in your training or inference code. Weave (their LLM tracing product) requires additional instrumentation via weave.init() and decorators applied to your LLM calls. | npm install -g @relayplane/proxy | pip install wandb (Python-first SDK), Weave: pip install weave |
| No account required RelayPlane starts with zero signup, zero credit card, and zero cloud dependency. Weights and Biases requires account creation before any data is logged. Both wandb and Weave send data to W&B servers by default, which requires authentication. | ||
| Free tier RelayPlane has no request cap or free tier limit. W&B offers a free personal tier for individual use. Teams using W&B in a collaborative setting pay $50 per user per month. The free tier has storage limits and restricted access controls. Enterprise pricing is negotiated separately. | MIT open source, no usage limits, fully free to self-host | Free for personal use (1 user), Teams plan at $50/user/month, Enterprise custom pricing |
| Primary language support W&B is built around a Python SDK. The documentation, quickstarts, LLM integrations, and Weave examples are all Python-centric. A JavaScript client exists but receives far less investment and has limited feature parity. RelayPlane is an HTTP proxy so any language that can make HTTP requests benefits automatically with no dedicated SDK needed. | Node.js proxy (any language via HTTP) | Python-first (wandb PyPI package), JavaScript SDK exists but is secondary |
| No code changes required RelayPlane requires zero application code changes. Set OPENAI_BASE_URL=http://localhost:4100 and your existing code is automatically proxied. W&B requires adding wandb.init() to your training scripts or weave.init() plus decorators around your LLM calls before any data is collected. | ||
| 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. W&B and Weave require SDK-level instrumentation in your application and do not operate as HTTP proxies. | ||
| Model routing and fallback RelayPlane routes requests to different models based on complexity and cost, with automatic fallback on provider failures. W&B does not route requests. It logs and compares what your application did, but cannot influence which model is called or switch providers on failure. | ||
| Local SQLite cost tracking RelayPlane logs every request's exact dollar cost in local SQLite with no data leaving your machine. W&B tracks token usage as part of Weave trace metadata, but cost tracking is incidental to its core purpose as an experiment and model tracking platform rather than a cost governance tool. | ||
| No data leaves your machine RelayPlane runs entirely on localhost by default with zero external telemetry. W&B and Weave send all logged data including LLM inputs and outputs to Weights and Biases servers. There is no self-hosted free tier for W&B. Enterprise self-hosting is available but at negotiated pricing. | ||
| Spend governance and budget limits RelayPlane can enforce spend limits and route away from expensive models when budgets are exceeded. W&B tracks usage metadata as observability data but has no mechanism to block, reroute, or cap spending on live LLM requests. | ||
| OpenAI-compatible drop-in RelayPlane exposes an OpenAI-compatible endpoint: set OPENAI_BASE_URL=http://localhost:4100 and your existing code works unchanged. W&B requires SDK calls and instrumentation hooks and does not expose a compatible LLM API endpoint. | ||
| Works with Claude Code and Cursor RelayPlane is designed for Claude Code, Cursor, Windsurf, and Aider with direct integration docs. W&B and Weave are application-level SDKs that cannot intercept traffic from AI coding assistants you do not control the source code of. | Not applicable (observability layer, not a proxy) | |
| Experiment tracking and run comparison Experiment tracking is the core purpose W&B was built for. You can log metrics, hyperparameters, and artifacts across training runs, compare them in the W&B dashboard, and reproduce any historical experiment. RelayPlane focuses on cost control and routing for production LLM inference, not ML training experiment management. | ||
| LLM tracing and evals (Weave) Weights and Biases built Weave as their LLM-specific product. Weave provides function-level tracing for LLM calls, evaluation pipelines with custom scorers, a dataset management UI, and an open-source library (wandb/weave on GitHub). If your primary challenge is evaluating LLM output quality and comparing prompt versions, Weave addresses that directly. | ||
| Model registry and artifact versioning W&B provides a model registry for storing, versioning, and promoting trained model weights across teams. Dataset versioning and artifact lineage are core W&B features that have no equivalent in RelayPlane. If you are iterating on fine-tuned models and need lineage tracking, W&B solves that problem. | ||
| Open source RelayPlane is MIT licensed end to end. W&B open-sources their Weave LLM tracing library under Apache 2.0 on GitHub, but the main Weights and Biases cloud platform is a closed-source SaaS product. Self-hosting Weave traces locally is possible with the open-source version, but most W&B features require the SaaS platform. | MIT licensed | Weave is open source (Apache 2.0 on GitHub). Core W&B platform is closed-source SaaS |
Why Teams Choose RelayPlane When They Need Cost Control, Not an MLOps Platform
A proxy intercepts requests. An MLOps SDK instruments training scripts. These solve fundamentally different problems.
Weights and Biases was built for ML researchers running training jobs: you call wandb.init(), log metrics during training, and compare runs in a dashboard. Their Weave product extends this model to LLM inference with function decorators and evaluation pipelines. RelayPlane is a synchronous proxy that sits in the request path at runtime: you change one baseURL and every LLM call flows through it, enabling real-time routing, cost tracking, and spend governance with no code changes. If you need to compare prompt versions or evaluate LLM output quality across a dataset, Weave helps. If you need to control what your production application spends on LLM calls and which models it calls, you need a proxy.
npm install in 30 seconds vs pip install plus wandb.init() plus Weave instrumentation
npm install -g @relayplane/proxy and you are proxying requests. RelayPlane requires zero changes to your application code. W&B requires pip install wandb, account creation, and adding wandb.init() or weave.init() plus decorators to your application code before any data is logged. For existing tools or services you do not control the source of, W&B instrumentation is not possible. RelayPlane's HTTP proxy approach works regardless of whether you own the code making the LLM calls.
Node.js and TypeScript native, not a Python research platform.
W&B is built around Python SDKs designed for ML researchers working in Jupyter notebooks and training pipelines. The documentation, quickstarts, and framework integrations are Python-centric. The JavaScript SDK exists but is not the focus of the platform. If your stack is Node.js, TypeScript, or a polyglot production environment, you are working against the grain of what W&B was designed for. RelayPlane is an npm package that ships as a Node.js binary and works natively with any HTTP client in any language via its OpenAI-compatible endpoint.
Built for production LLM apps, not ML research workflows.
Weights and Biases is optimized for the ML researcher workflow: experiment iterations, hyperparameter sweeps, model comparisons, and dataset versions. Weave brings this mental model to LLM development with eval datasets and function-level tracing. RelayPlane is built for teams shipping production LLM applications: real-time cost tracking, spend caps, model routing, and compatibility with every tool in your stack including Claude Code and Cursor. The $50/user/month Teams pricing reflects W&B's positioning as enterprise ML infrastructure, not a cost tool for a developer running LLM API calls.
W&B Solves MLOps and LLM Eval Problems. RelayPlane Solves Cost Control Problems.
Weights and Biases is a focused product for ML engineers and data scientists who need to track training experiments, version models and datasets, and evaluate LLM output quality. Their Weave product (open source on GitHub at wandb/weave) provides function-level tracing for LLM calls and evaluation pipelines for comparing prompt performance over labeled datasets. If your primary challenge is iterating on a fine-tuned model, running evals over a golden dataset, or comparing training runs, W&B and Weave are worth evaluating.
But W&B cannot route requests, enforce budget limits, or intercept traffic from tools you do not control. It requires adding wandb or weave instrumentation to your Python application. It does not work with Claude Code, Cursor, or other AI coding assistants because those tools do not expose instrumentation hooks. The Teams plan at $50/user/month reflects its positioning as enterprise ML infrastructure rather than a developer cost tool. If you want to start tracking and controlling LLM costs today across any framework, any language, and any tool you use, RelayPlane installs in one npm command and runs on localhost with zero account or code changes required.
Weights and Biases Pricing at a Glance
| Plan | Price | Users | Account Required |
|---|---|---|---|
| Free (Personal) | Free | 1 user | Yes |
| Teams | $50/user/month | Multiple | Yes |
| Enterprise | Custom | Unlimited | Yes |
| RelayPlane | Free (MIT) | Unlimited | No |
Get Running in 30 Seconds
No account. No SDK instrumentation. No wandb.init() or weave decorators: