Are AI API relay services safe for production?

Use official APIs, official cloud platforms, or self-hosted gateways first for production. Anonymous relay services can be useful for small experiments, but they create account, privacy, model-authenticity, billing, and service-term risks.

What is the difference between an AI router and an API relay?

A model router usually provides documented routing, provider selection, and billing across many model providers. A relay may simply expose an OpenAI-compatible endpoint, sometimes with less transparency about upstream accounts, data handling, or model availability.

Can I use a relay with Cursor, Claude Code, or other coding agents?

Many tools support OpenAI-compatible base URLs, but compatibility does not remove privacy or terms risk. Avoid sending private repositories or customer data through services you have not audited.

AI API Relay Directory

Searchable directory

Compare relays, routers, cloud APIs, and gateways

This table is a practical shortlist, not a paid ranking. It separates first-party APIs, regulated cloud platforms, documented routers, self-hosted gateways, and China-market relay services because the operational risk is different even when all of them expose an OpenAI-compatible endpoint.

Search service or use case

Category

Max risk

Service	Type	Best fit	Risk	Why it matters	Link
OpenAI API	Official API	Production GPT apps, Codex-style tooling, direct billing	Low	Use direct official billing when the workload involves private user data, company code, fine-tuning, or long-term reliability.	Pricing
Anthropic API	Official API	Claude agent workloads, code review, long-form reasoning	Low	Best for teams that need Claude support, official terms, predictable invoices, and direct access to prompt caching.	Pricing
Google Gemini API	Official API	Gemini apps, multimodal workloads, Google ecosystem	Low	Use official Gemini endpoints for production apps, grounding features, context caching, and enterprise Google Cloud controls.	Pricing
OpenRouter	Model router	Testing many hosted models through one API	Medium	Useful when you want model breadth, unified billing, and OpenAI-compatible calls, but still verify provider routing, privacy, and markup.	Docs
Vercel AI Gateway	Model gateway	Vercel apps that need provider routing and observability	Low	A strong option if the app already runs on Vercel and the team wants a documented gateway rather than ad hoc proxy code.	Docs
Cloudflare AI Gateway	Gateway layer	Observability, caching, rate limits, provider control	Low	Good when you keep your own provider keys but want logging, analytics, caching, and centralized control at the edge.	Docs
LiteLLM Proxy	Self-hosted gateway	Teams that want one internal OpenAI-compatible endpoint	Low	Good for internal routing if you can operate it yourself, store secrets safely, and monitor provider failures.	Docs
Portkey	AI gateway	Production observability, retries, guardrails, enterprise control	Low	Better framed as an AI gateway than a relay. Use when governance and monitoring are more important than lowest price.	Docs
SiliconFlow	China model platform	China-accessible hosted models and OpenAI-compatible API	Low	More suitable than anonymous relays when you need a China-market platform with public docs and model catalog.	Site
Volcengine Ark	Cloud model platform	Doubao and China cloud deployment paths	Low	Use this class of provider when compliance, invoices, account ownership, and Chinese infrastructure matter.	Product
Alibaba Cloud Model Studio	Cloud model platform	Qwen/DashScope workloads and enterprise cloud controls	Low	A direct cloud path for Qwen-family workloads, especially when a Chinese cloud account and receipts are required.	Docs
Baidu Qianfan	Cloud model platform	ERNIE/Wenxin model access and Chinese enterprise stacks	Low	Consider it for enterprise-style Chinese deployments where account ownership and cloud governance matter.	Product
302.AI	Relay marketplace	Small tests that need many AI tools and model endpoints	Medium	Useful for experiments, but audit pricing, upstream model identity, privacy policy, invoice needs, and refund rules before scaling.	Site
AiHubMix	Relay marketplace	China-market OpenAI-compatible experiments	Medium	Do not send sensitive code until you have verified service terms, data handling, model mapping, uptime, and billing behavior.	Site
GuijiAPI	Relay marketplace	Small-balance OpenAI-compatible relay tests	Medium	Registration presents it as an API relay. Treat it as an experiment path until terms, data handling, model mapping, uptime, and billing export are audited.	Register
SmewAI	AI API gateway / relay	Sub2API-style gateway trials and non-sensitive tool tests	Medium	The site frames itself as an AI API gateway. Useful to test custom base URL workflows, but verify upstream routing, model identity, billing, and data policy before real traffic.	Register
API2D	Legacy relay	Compatibility tests and low-value experiments only	High	Older relay-style services can be convenient, but they are not a default for private repositories or production user data.	Site

Showing all relay entries.

Decision rule

Use the lowest-risk endpoint that solves the access problem

Domestic AI API relay services are popular because they can reduce friction: one balance, one base URL, many model names, fewer overseas payment problems, and compatibility with tools that accept an OpenAI-style endpoint. That convenience is real. It is also exactly why the risk needs to be named before the site recommends anything.

The safest order for a serious project is simple: official API first, official cloud platform second, documented model router third, self-hosted gateway when you have operations capacity, and anonymous or lightly documented relay only for disposable experiments. The more sensitive the prompt is, the higher you should move up that chain. Company source code, customer conversations, medical notes, legal documents, and paid product traffic should not be routed through a service you cannot audit.

Price alone is not a reliable signal. Some relay prices look attractive because the service is batching, routing to cheaper models, using promotional upstream accounts, or charging differently for context, images, search, cache, and retries. A cheap endpoint that silently maps a premium model name to a weaker model can ruin a coding-agent benchmark. A cheap endpoint that loses access during a launch can cost more than official billing.

Cursor and coding agents

Relays are especially risky for coding-agent workflows

Most developer interest in domestic relay services is not casual chat. It is Cursor, Claude Code, Codex-like agents, custom IDE tools, and automation scripts that send repository context. These prompts often include filenames, API design, business logic, private stack traces, internal roadmap notes, and sometimes secrets accidentally copied into context. That makes coding-agent traffic different from a throwaway prompt asking for a slogan.

If you use a relay with a coding agent, start with a synthetic repository and a synthetic task. Check whether streaming works, whether tool calls and JSON mode behave correctly, whether max context is really honored, whether the response style matches the claimed model, and whether the provider returns consistent errors. Only after that should you try a non-sensitive real repo. Keep environment files, private keys, customer data, and proprietary documents out of the prompt path.

When a relay is acceptable

Good use cases are small, reversible, and observable

A relay can be reasonable for short-term experiments: checking whether a tool supports custom base URLs, comparing model families before opening official accounts, prototyping a personal script, or testing a China-accessible fallback for non-sensitive workloads. In those cases, keep the balance small, use separate API keys per tool, and record the exact model ID, price, latency, error rate, and output quality.

For anything that starts to get real traffic, move the winning workflow to an official API, an official cloud platform, or a gateway you control. If the relay remains in the stack, document it as a vendor dependency, set budget alerts, add fallbacks, and write down what data is allowed to pass through it. A relay without a data policy should be treated like an untrusted third-party processor.

Practical recommendation

If the goal is a quick personal website or a small automation, a domestic relay can be worth testing because setup is fast. If the goal is an enduring product, use official APIs or a reputable cloud platform for primary traffic, then place Cloudflare AI Gateway, Vercel AI Gateway, Portkey, or LiteLLM in front if you need routing, observability, caching, and failover. Treat relay marketplaces as a convenience layer, not as the root of trust.

AI Code Limits does not sell API relay accounts and does not verify the financial or legal status of listed services. Some outbound relay links may use referral codes; the risk labels remain independent checklist labels, not guarantees or paid rankings. Always check the provider's current pricing, terms, data policy, and model availability before connecting production traffic.