ReliAPI
KikuAI Lab · Coding
ReliAPI is a self-hosted proxy that sits between your application and the HTTP or LLM APIs it calls. It works as a self-hosted API gateway for both plain HTTP and language-model traffic. It handles caching, non-streaming LLM idempotency, and budget guardrails so that repeated requests don't waste money and failed calls don't pile up. You run it yourself with Docker or a local Python setup, point it at targets like OpenAI, Anthropic, or Mistral, and talk to your own proxy instead of hitting providers directly. API reliability is the whole point here. It stays in beta, so treat it as a layer you evaluate rather than a finished managed service. The project says so on its own site.
About ReliAPI
What Is ReliAPI
ReliAPI is a small FastAPI service from KikuAI Lab that fronts your outbound HTTP and LLM traffic. The pitch is simple: instead of letting your app call OpenAI or any other API on its own, you route those calls through a proxy you control. That proxy can return a cached answer, refuse a duplicate request, or stop a runaway loop before the invoice arrives. It's a safety net for outbound calls.
It targets developers and small teams who keep watching their API bills climb without knowing why. A single bug, like a retry loop firing every second, can burn hundreds of dollars overnight. ReliAPI is built to catch that kind of thing at the request layer.
How common is that? More than you'd think. Retry storms and duplicate calls are easy mistakes to make and hard to notice.
The biggest limitation is maturity. ReliAPI is explicitly beta software, and the project says so on its own site. There's no SLA, no managed multi-upstream fallback, and idempotency applies to non-streaming LLM calls only, which means teams relying on streamed responses get noticeably less protection from the current feature set. It also runs as a self-hosted service, so Redis durability, target allow-lists, and provider quotas are your job, not the vendor's, and no support contract covers them.
Getting Started
- Clone the GitHub repository and copy
.env.exampleto.env. - Add the provider keys referenced in your
config.yamlto.env(for exampleOPENAI_API_KEY). - Start ReliAPI and its Redis service with
docker compose up -d --build. - Confirm the service is healthy by calling
curl http://localhost:8000/healthz. - Point your app's base URL at the local proxy and send an LLM or HTTP request through
/v1/proxy/llmor/v1/proxy/http.
Product Information
A quick look at ReliAPI's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Indie developers and small engineering teams
- Backend and platform engineers
- Teams evaluating LLM cost controls
Tasks
- Deduplicating repeated non-streaming LLM calls
- Caching HTTP GET and HEAD responses
- Setting hard and soft spend caps
Scenarios
- An agent or script that retries a failing call in a tight loop.
- A development or staging environment where you want visibility into LLM spend.
- A small product that calls several providers and needs one consistent proxy between them.
Key features
HTTP and LLM Proxy in One Service
ReliAPI exposes two proxy routes: /v1/proxy/http for generic HTTP targets and /v1/proxy/llm for configured language-model providers. You define targets in a config.yaml file, and the proxy applies your rules, such as caching or circuit limits, before forwarding the call. One place to change how every outbound request behaves. That's the upside.
Redis-Backed Caching
A TTL cache covers HTTP GET and HEAD requests plus non-streaming LLM responses. Set a cache window per target, and repeat calls inside that window return the stored result instead of hitting the provider again. For an API you call the same way hundreds of times a day, that alone can trim both latency and cost, which adds up over time.
Non-Streaming LLM Idempotency
The LLM route accepts an idempotency key, and Redis tracks it so the same logical request doesn't execute twice. The project is careful here: this covers non-streaming calls only, and it doesn't promise exactly-once execution or matching provider billing, so read the docs before you rely on it. Read it as protection against accidental duplicates, not a billing guarantee.
Budget Guardrails
Each target can carry soft and hard cost caps. Before a request goes out, ReliAPI estimates its cost; a soft cap can warn you and a hard cap can block the call. The caveat is that these are pre-request estimates, so they won't catch every cent a provider actually charges. Close enough for guardrails. Not for accounting.
Rate Limiting and Configurable Targets
Built-in request limits let you cap traffic per configured tier, and targets are defined in plain YAML rather than baked into code. Adding a provider or changing a base URL is a config edit. No redeploy. That keeps a multi-provider setup manageable. For teams chasing API reliability across several providers, this config-first design is the part that saves the most time.
Python and JavaScript SDKs
Official SDKs on npm (reliapi-sdk) and PyPI (reliapi-sdk) wrap the proxy routes for both languages. You can call proxy_http or proxy_llm from a few lines of code instead of hand-rolling requests, and there's a GitHub Action for CI workflows too.
Prometheus Metrics
A /metrics endpoint exposes Prometheus data for your existing monitoring stack. Track proxy traffic there. That matters if you want an alert when spend or error rates move, rather than discovering it on the invoice.
Pros and cons
Pros
- Puts caching, idempotency, and budget caps in one self-hosted service instead of scattered across app code.
- Config-driven targets make it straightforward to add OpenAI, Anthropic, or Mistral without code changes.
- Official Python and JavaScript SDKs plus a GitHub Action reduce integration effort.
- Prometheus metrics slot into monitoring setups teams already run.
Cons
- It's beta software with no SLA, so you're accepting rough edges and possible breaking changes.
- Self-hosting means you own Redis uptime, security, and target allow-lists; if Redis goes down, caching and idempotency stop working.
- Idempotency and caching cover the non-streaming path only, so streaming apps get less benefit.
- Budget caps rely on estimates and won't precisely match actual provider billing.
Frequently asked questions
ReliAPI is a self-hosted proxy that adds caching, idempotency, and spending limits to your HTTP and LLM API calls. You use it to cut duplicate requests, reuse cached responses, and stop runaway loops from driving up your provider bill.
Related content
Explore related tools, skills, and articles for ReliAPI.
ReliAPI Alternatives
Forefront
Forefront · CodingForefront is a web platform for building with open-source AI. It lets you fine-tune leading open-source language models on your own data, evaluate how they perform, and run them through an API or export them to host yourself. Developers who want the convenience of a closed-source platform but insist on owning their models and data are the target audience here.
Startkit
StartKit.AI · CodingStartkit is a boilerplate for building AI SaaS and AI wrapper products. Think of it as an AI startup boilerplate with the boring parts already wired up: authentication, Stripe and Lemon Squeezy payments, usage limits, transactional email, and an AI API starter that talks to OpenAI, Anthropic, Groq, or Llama. You clone the repo, set your price, and start on the part of your product that people actually pay for. It's Next.js under React and Tailwind, so most of the boilerplate code already feels familiar.
Testim
Tricentis · CodingTestim is an AI-powered test automation platform for building and running end-to-end tests across web, mobile, and Salesforce applications. It leans on machine learning to keep tests stable when an interface changes, so teams spend less time fixing broken selectors. Not bad for an automated testing tool you can start using today. You create tests by recording actions in a browser, then optionally add JavaScript when you need more control. It's a solid pick for busy QA teams.