You're here because inference got expensive, slow, or rate-limited — and you need overflow.

Overflow Inference for AI Apps

Route non-critical AI traffic to a fallback when your primary provider or GPUs get expensive, capped, or slow.

Works with OpenAI / Gemini / Anthropic / Azure / AWS / self-hosted.

Provider-agnostic · Best-effort · No SLA · No lock-in · Remove anytime

You'll receive: endpoint + API key + a one-request test command.

Who This Is For

This is for teams who:

  • Run AI in production
  • Pay real inference bills (token-based or GPU-based)
  • Hit spikes, rate limits, timeouts, or cost blowouts
  • Need a fallback lane for traffic that doesn't need perfect latency

This is NOT for:

  • Hobby projects
  • Workloads that require strict SLAs
  • "Replace our main provider" migrations

(This page is for overflow + fallback, not a rip-and-replace.)

What "Overflow" Means

What overflow is

Overflow = a second lane for inference.
You keep your current provider as primary. You route only specific traffic to overflow when it makes sense.

Common overflow traffic:

  • Background jobs (summaries, tagging, enrichment)
  • Batch embeddings
  • Image generation / render queues
  • "Retry after fail" requests (timeouts / 429s)
  • Non-urgent chats ("good enough" responses)

What it's not

  • Not a migration
  • Not a replacement
  • Not a commitment

If it doesn't save money or reduce incidents, you remove it.

Why Apps Add Overflow

1) Spikes are expensive to over-provision

Keeping spare GPU capacity (or higher-tier quotas) "just in case" is wasted spend most days.

2) Providers rate-limit and degrade under load

429s, retry storms, and long tail latency happen — and your users feel it.

3) Fallbacks prevent lost work

When primary fails, overflow saves the request instead of dropping it (especially for batch + async).

4) You need a cost-pressure release valve

When prices spike or usage explodes, overflow lets you keep serving requests without blowing budget.

How It Works

1

Add a fallback endpoint

Add one overflow endpoint to your app.
No migration. No rewrite. You keep your current provider.

If you already speak OpenAI-style APIs, integration is usually a 1-file change.

2

Choose what routes to overflow

You control routing rules like:

  • When primary returns 429 / 5xx / timeout
  • When queue depth > X
  • When cost threshold is exceeded
  • When request type is batch / background

Most teams start with "fallback on failure" + batch jobs.

3

Pay only for what you use

  • Pay-as-you-go
  • Stripe billing
  • No contract

If it works, keep it. If not, turn it off.

Why Teams Use This

Avoid over-provisioning GPUs

Reduce 429 + timeout incident impact

Keep batch jobs moving during spikes

Lower blended inference cost

Add a "second lane" without migration

Works across providers + self-hosted

Setting Expectations

What we guarantee

  • You get an overflow endpoint + key
  • Clear pricing, pay-as-you-go
  • Best-effort execution, designed for overflow traffic

What we don't promise

  • SLAs
  • Perfect latency
  • "Make primary providers irrelevant"

That's the point: overflow is the safety valve, not the main engine.

Add overflow in under 10 minutes.

Get an endpoint + key + a one-request test command. Route only what you choose.

No contract · No commitment · Remove anytime

Provider-agnostic

Best-effort inference · Overflow use only