Solution

High-volume inference

Sustained, large-scale inference workloads run into problems a demo never hits: provider rate limits, connection exhaustion, and retry storms during upstream slowdowns. Aniron absorbs that operational load so your team does not have to build it in-house.

The problem

Your product grows from thousands to millions of requests a day, and the thin HTTP client you wrote against a single provider's SDK starts throwing rate-limit errors during peak hours. You did not plan for retry logic, backoff, or connection pooling because the demo never needed it.

Or: a batch job that reprocesses historical data competes with real-time user traffic for the same provider rate limit, and users start seeing slow responses because a background job is consuming the shared capacity.

Or: you scale to a second region and now need to duplicate connection pooling, retry, and rate-limit handling logic in a second codebase, and the two implementations drift out of sync.

Volume exposes infrastructure problems that low traffic hides. Building that infrastructure yourself is a full-time engineering commitment.

How Aniron solves it

Managed connection pooling

Aniron keeps warm connections to every upstream provider. Your requests do not pay per-request connection setup cost even at sustained high volume.

Backoff and retry, built in

Rate-limited requests are retried with exponential backoff automatically. Your application code does not need its own retry loop for provider throttling.

Per-key isolation at scale

Separate batch jobs from real-time traffic with different keys and budgets, so a large background job cannot starve user-facing requests of throughput.

Same call, any volume

// Same request shape at 10 req/day or 10M req/day
POST /v1/chat/completions
{
  "model": "gpt-4o",
  "messages": [{ "role": "user", "content": "..." }]
}

// Aniron handles connection pooling, retries with backoff,
// and provider-side rate limit queuing behind this one call.

24/7 Retry and backoff handling

Rate-limit and transient errors are retried automatically, without application-level retry logic.

1 Gateway to scale

No duplicated connection pooling or retry logic across regions or services.

Who this is for

Teams scaling past a single provider's limits

Growth outpaces one provider's rate limits before it outpaces your budget. Route excess volume to a secondary model instead of queuing indefinitely.

Batch and real-time workloads sharing infrastructure

Reprocessing jobs, embeddings backfills, and evaluation runs can consume real capacity. Per-key budgets keep them from degrading production traffic.

Multi-region products

One gateway configuration serves every region instead of reimplementing connection handling and retry logic per deployment.

Lean engineering teams without dedicated infra staff

Get production-grade retry, pooling, and failover behavior without assigning an engineer to build and maintain it in-house.

Frequently asked questions

What happens when I exceed a provider's rate limit?

Aniron queues and retries requests with exponential backoff against the same provider, and can fail over to a secondary model if the queue backs up past a configurable threshold. Your application sees a slower response, not an error.

Does Aniron add meaningful latency at high volume?

The proxy layer adds low single-digit milliseconds of overhead per request. Connection pooling to upstream providers is kept warm, so there is no per-request connection setup cost under sustained load.

Can I run batch and real-time traffic through the same account?

Yes. Use per-key budgets and separate keys for batch jobs versus real-time serving so a large batch run cannot starve real-time traffic of budget or provider throughput.

How do I know if I am approaching a provider capacity limit?

The usage API and dashboard surface rate-limit rejections and retry counts per provider, so you can see contention building before it becomes a user-facing slowdown.

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