Show HN: AI-Gateway – Open-source semantic caching proxy to reduce LLM API costs

Description and use

**Brief Summary (2‑3 sentences)**
AI‑Gateway is an open‑source semantic‑caching proxy that sits between your application and any LLM API, automatically storing and reusing embeddings of past requests to cut down on repeated token usage and API costs. By handling the cache logic out‑of‑the‑box, it lets teams add generative‑AI features—such as RAG pipelines or autonomous agents—without having to build a custom model stack from scratch. The project is actively maintained (last update 2026‑06‑22) and targets prototyping or internal tooling use cases.

**Value**

• **Cost Savings:** Re‑using semantically similar prompts reduces the number of calls to expensive LLM endpoints, directly lowering cloud spend.
• **Speed & Latency:** Cached responses are served instantly, improving user experience for repetitive queries.
• **Rapid Experimentation:** Developers can plug the proxy into existing codebases and start testing retrieval‑augmented generation or agent workflows without training or hosting their own models.

**Practical Adoption Path**

1. **Evaluate Fit** – Clone the repo, run the provided Docker compose or local server, and point a small test client at the proxy using your preferred LLM provider’s API key.
2. **Configure Caching Policy** – Adjust similarity thresholds, TTLs, and storage back‑ends (Redis, PostgreSQL, etc.) to match your workload’s tolerance for stale results.
3. **Integrate** – Replace direct LLM calls in your code with the proxy endpoint; most SDKs require only a URL change.
4. **Monitor & Tune** – Use the built‑in metrics dashboard (or export logs to Prometheus) to track hit‑rate, cost savings, and latency, then iterate on similarity thresholds.
5. **Hardening for Production** – Add authentication, rate‑limiting, and observability; lock down the storage backend and run the proxy behind your API gateway or service mesh.

**Production Readiness**

• **Maturity:** Medium. The codebase is recent and functional for prototypes, but integration signals are sparse, and documentation is limited.
• **Dependencies:** Relies on a vector store (e.g., Redis Vector, Pinecone) and an LLM provider; ensure you have a stable hosting environment for both.
• **Risks:** Limited community support, unknown long‑term maintenance cadence, and potential licensing ambiguities—perform a license audit and review open issues before committing to production.
• **Recommendation:** Deploy in a staging environment first, validate cost‑saving metrics, and perform a security review. If the cache hit‑rate is high and the operational overhead is acceptable, promote to production with the usual safeguards (auth, monitoring, fallback to direct LLM calls).

Use in a workflow

Show HN: AI-Gateway – Open-source semantic caching proxy to reduce LLM API costs helps add AI capability without starting from a blank model stack.

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

Limitations and review notes

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