RelayPlane/proxy — Open source cost intelligence proxy for AI agents.

Description and use

**Brief Summary (2‑3 sentences)**
RelayPlane /proxy is an open‑source, MIT‑licensed cost‑intelligence proxy that sits in front of LLM providers and automatically routes requests to the cheapest suitable model, cutting inference spend by up to 80 %. It ships with a web dashboard, a policy engine, and built‑in support for 11 major AI providers, making it easy to prototype RAG, agent‑based, or other AI‑driven features without assembling a custom model stack.

**Value Proposition**

• **Immediate cost savings**: By evaluating price‑performance across providers in real time, the proxy reduces API bills dramatically while preserving response quality.
• **Plug‑and‑play AI capability**: Teams can add LLM functionality to existing services simply by pointing their client libraries at the proxy endpoint, avoiding the overhead of managing multiple SDKs or credentials.
• **Unified governance**: The built‑in policy engine lets you enforce usage limits, model selection rules, and audit logs from a single dashboard, which is especially useful for compliance‑heavy environments.

**Practical Adoption Path**

| Phase | Goal | Actions |
|------|------|---------|
| **1️⃣ Proof‑of‑Concept** | Validate that the proxy works with your current stack and delivers the promised cost reduction. | • Clone the repo and run docker compose up (or npm install && npm run dev).<br>• Follow the README to add API keys for the providers you already use.<br>• Update a single endpoint in a test micro‑service to point at http://localhost:3000/v1/chat/completions. |
| **2️⃣ Policy Tuning** | Align routing and cost policies with business requirements. | • Use the dashboard to define rules (e.g., “use Provider A for temperature < 0.7, fallback to Provider B otherwise”).<br>• Enable rate‑limiting and per‑user quotas. |
| **3️⃣ Staging Integration** | Verify stability under realistic load and monitor observability. | • Deploy the proxy to a staging Kubernetes namespace (Helm chart is provided).<br>• Enable Prometheus metrics and log aggregation.<br>• Run load‑tests against typical request patterns. |
| **4️⃣ Production Roll‑out** | Move to production with confidence. | • Harden security: rotate provider keys via secret manager, enable TLS termination, and run a vulnerability scan of the container image.<br>• Set up alerts on cost spikes and error rates.<br>• Gradually shift traffic (canary or blue‑green) from direct provider calls to the proxy. |

**Production Readiness Assessment**

• **Maturity**: Medium. The project is actively maintained (last commit 2026‑05‑10), has 170 ⭐ on GitHub, and a modest but growing community (26 forks). The core functionality (routing, dashboard, policy engine) is stable for internal prototypes and early‑stage production workloads.
• **Strengths**: Clear cost‑saving benefit, multi‑provider support out‑of‑the‑box, TypeScript codebase that integrates well with modern Node.js services, and an extensible policy layer.
• **Risks / Gaps**:
• **Security & compliance** – No formal security audit yet; you’ll need to review the container image, secrets handling, and network exposure before handling sensitive data.
• **Operational overhead** – Requires running a separate service (Docker/K8s) and keeping provider credentials up‑to‑date.
• **Support** – No dedicated commercial support; reliance on community contributions for bug fixes.

**Bottom Line**
RelayPlane /proxy is a practical way to add AI capabilities while slashing API costs, making it a strong candidate for prototyping and internal tooling. With a disciplined rollout—starting with a small PoC, tightening policy controls, and performing a security hardening pass—it can be promoted to production for non‑mission‑critical workloads, provided you allocate resources for ongoing maintenance and monitoring.

Problem

RelayPlane/proxy helps add AI capability without starting from a blank model stack.

Outcome

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.

Workflow

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Evidence

Open source cost intelligence proxy for AI agents. Cut costs ~80% with smart model routing. Dashboard, policy engine, 11 providers. MIT licensed.

Source