RelayPlane vs Arize AI
Arize AI is an ML observability and LLM evaluation platform with Phoenix open-source tracing and a cloud platform for drift detection and evals. 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 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 platform
- OpenAI-compatible drop-in with one baseURL swap, zero code changes
Arize may work for you if you need:
- LLM evaluation pipelines: hallucination detection, relevance scoring, custom evals
- Distributed tracing with span-level visualization for multi-step LLM apps
- Embedding drift detection and model performance monitoring over time
- Phoenix OSS for local LLM tracing with LangChain or LlamaIndex
Feature Comparison
| Feature | RelayPlane | Arize AI |
|---|---|---|
| Product type RelayPlane sits in the critical path of every LLM request: it intercepts, routes, and logs each call transparently. Arize AI is an asynchronous observability layer that collects traces and runs LLM evaluations after requests complete. Arize does not proxy or route requests. | npm-native LLM proxy and gateway (local-first) | ML observability and LLM evaluation platform (Phoenix OSS + Arize cloud) |
| Install method RelayPlane ships as a standalone npm binary: one command and you are proxying requests. Arize requires a Python package, account setup for cloud, and OpenTelemetry-based instrumentation woven into your application code. Phoenix (the open-source version) requires running a local server and adding trace exporters to your code. | npm install -g @relayplane/proxy | pip install arize-phoenix or pip install arize (Python SDK), instrumentation required |
| No account required RelayPlane starts with zero signup, zero credit card, and zero cloud dependency. Phoenix open-source runs locally but still requires code instrumentation. Arize cloud requires account creation before traces are sent to the dashboard. | Phoenix OSS: no account. Arize cloud: account required | |
| Free tier RelayPlane has no request cap or free tier limit. Phoenix is Apache 2.0 licensed and free to run locally. The Arize cloud platform for production-scale LLM monitoring is a paid SaaS product with plans starting around $200/month. There is no free cloud tier for production use. | MIT open source, no usage limits, fully free to self-host | Phoenix OSS is free (Apache 2.0). Arize cloud plans start at approximately $200/month |
| Primary language support Arize and Phoenix are built around Python SDKs and OpenTelemetry instrumentation. The documentation, quickstarts, and framework integrations are Python-centric. 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 (arize and arize-phoenix PyPI packages) |
| No code changes required RelayPlane requires zero application code changes. Set OPENAI_BASE_URL=http://localhost:4100 and your existing code is automatically proxied. Arize and Phoenix require adding OpenTelemetry instrumentation, configuring trace exporters, and initializing the SDK in your application code 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. Arize and Phoenix 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. Arize does not route requests. It observes and evaluates 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. Arize tracks token usage as part of trace metadata, but cost tracking is a secondary feature within its observability platform rather than a primary routing or control mechanism. | ||
| No data leaves your machine RelayPlane runs entirely on localhost by default with zero external telemetry. Phoenix can run locally, but Arize cloud sends all trace data including LLM inputs and outputs to Arize servers. For regulated environments, the Arize cloud deployment requires trusting a third-party SaaS with your LLM request payloads. | Phoenix OSS: local only. Arize cloud: data sent to Arize servers | |
| Spend governance and budget limits RelayPlane can enforce spend limits and route away from expensive models when budgets are exceeded. Arize tracks token usage as observability data 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. Arize requires SDK calls and trace exporters woven into your application logic 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. Arize and Phoenix 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) | |
| LLM evaluation (evals) Arize and Phoenix offer a dedicated LLM evaluation framework: you can run hallucination detection, relevance scoring, toxicity checks, and custom evals over your traced requests. This is a core capability of the Arize platform. RelayPlane focuses on cost control and routing rather than post-hoc quality evaluation. | ||
| Distributed tracing and span visualization Arize Phoenix provides full OpenTelemetry-compatible distributed tracing with a span visualization UI. You can see the full call tree for a multi-step LLM application, with timing, inputs, outputs, and token counts per span. RelayPlane logs per-request metadata including model, tokens, cost, and latency, but does not reconstruct multi-span application traces. | Basic request logging | |
| Drift detection and model monitoring Arize was originally built for ML model monitoring and drift detection. It can alert when embedding drift, output distribution shifts, or performance degradation are detected over time. RelayPlane does not perform statistical monitoring of model outputs. | ||
| Framework integrations (LangChain, LlamaIndex) Arize and Phoenix have native OpenTelemetry auto-instrumentation for LangChain, LlamaIndex, OpenAI, and other Python frameworks. These integrations automatically capture multi-step traces. RelayPlane works with any framework by intercepting HTTP calls, but does not produce nested trace trees with named spans. | Works transparently via HTTP proxy | |
| Open source RelayPlane is MIT licensed end to end. Arize open-sources Phoenix under Apache 2.0 but the production Arize cloud platform and its model monitoring features are closed-source SaaS. | MIT licensed | Phoenix OSS is Apache 2.0. Arize cloud is closed-source SaaS |
Why Teams Choose RelayPlane When They Need Cost Control, Not Just Observability
A proxy intercepts requests. An observability SDK instruments code. These solve different problems.
Arize and Phoenix are asynchronous observability layers: you add OpenTelemetry instrumentation to your Python application, trace data is collected after requests complete, and you get a dashboard for evals and drift detection. 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. If you need to run hallucination evals or see span-level traces of a multi-step RAG pipeline, Arize helps. If you need to control what your application spends and which models it calls, you need a proxy.
npm install in 30 seconds vs pip install plus OpenTelemetry instrumentation
npm install -g @relayplane/proxy and you are proxying requests. RelayPlane requires zero changes to your application code. Arize and Phoenix require pip install, running a Phoenix server, configuring OpenTelemetry trace exporters, and adding instrumentation calls to your application code. For existing tools or services you do not control the source of, Arize 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 an afterthought.
Arize and Phoenix are built around Python SDKs. The documentation, quickstarts, framework integrations, and examples are all Python-first. JavaScript SDK support exists but lags behind the Python ecosystem. If your stack is Node.js, TypeScript, or a polyglot environment, you are working against the grain of the platform. 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-in cost control. Not just cost observation.
Arize is useful for understanding what your LLM application did. It captures token counts, latency, and output quality metrics in its dashboard. 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. For agentic workloads that can run for minutes and burn unexpected tokens, having a proxy that can cap spending in real time is meaningfully different from an observability platform that shows you what happened after the fact.
Arize Solves LLM Evaluation Problems. RelayPlane Solves Cost Control Problems.
Arize AI is a focused product for ML engineers and data scientists who need to evaluate and monitor LLM output quality at scale. Phoenix (their Apache 2.0 open-source tool) provides OpenTelemetry-compatible tracing for LangChain, LlamaIndex, and OpenAI applications with a local UI for trace inspection. The Arize cloud platform extends this with embedding drift detection, eval pipelines, and production monitoring. If your primary challenge is understanding whether your RAG pipeline is hallucinating or whether your LLM outputs have degraded since last week, Arize is worth evaluating.
But Arize cannot route requests, enforce budget limits, or intercept traffic from tools you do not control. It requires adding OpenTelemetry 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. 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.
Arize AI Pricing at a Glance
| Option | Price | Hosting | Account Required |
|---|---|---|---|
| Phoenix OSS | Free (Apache 2.0) | Self-hosted | No |
| Arize Cloud | ~$200+/month | Arize SaaS | Yes |
| RelayPlane | Free (MIT) | localhost | No |
Get Running in 30 Seconds
No account. No SDK instrumentation. No OpenTelemetry config: