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Use case

AI customer support

Support bots that don't go down when a provider does — with caching and per-conversation cost visibility.

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Stay up when a provider doesn't

Automatic fallback reroutes requests to a healthy model the moment your primary provider rate-limits or errors. Support doesn't go dark.

Cache the questions everyone asks

Prompt caching cuts the cost and latency of the repeated context and FAQ-style answers that dominate support traffic.

Cost per conversation

Every message is logged with model, tokens and dollar cost, so you can see what each conversation — and your whole support volume — actually costs.

Pick the right model for the tier

Route simple FAQs to a fast, cheap model and escalate complex tickets to a frontier model, all through one endpoint.

Support traffic is high-volume, repetitive, and unforgiving about downtime

An AI support agent answers the same questions thousands of times a day, and the one time it returns a 503 is the time a customer was about to churn. Three things matter: it has to stay up, it has to be cheap at volume, and you have to know what it costs.

LLM Gateway gives you all three on top of any model — one OpenAI-compatible API, automatic fallback, caching, and per-request cost logging.

Don't let a provider outage become a support outage

Single-provider support bots inherit that provider's bad days. With the gateway you define a fallback chain, and a rate-limit or error on the primary model silently reroutes to the next one:

1import OpenAI from "openai";2
3const client = new OpenAI({4  baseURL: "https://api.llmgateway.io/v1",5  apiKey: process.env.LLM_GATEWAY_API_KEY,6});7
8// The gateway routes to a fallback model if the primary is unavailable.9const reply = await client.chat.completions.create({10  model: "openai/gpt-5.1",11  messages: conversation,12});

Your customers see an answer; they never see the failover.

Pay less for the questions you answer constantly

Most support volume is variations on a handful of questions, wrapped in the same system prompt and knowledge-base context. Prompt caching means you stop paying full price for those repeated tokens — cutting both cost and latency exactly where your volume concentrates.

Route by complexity

Not every ticket needs a frontier model. Send straightforward FAQs to a fast, inexpensive model and reserve the expensive reasoning models for genuinely hard tickets — all through the same endpoint, decided per request.

Know the cost of every conversation

The gateway logs each message with model, tokens, latency and dollar cost. Attribute spend by channel or environment and you can finally answer "what does support cost us per conversation?" — and watch that number move as you tune routing and caching.

Frequently asked questions

How does LLM Gateway improve support reliability?

It adds automatic fallback across providers. If your primary model is rate-limited or returns an error, the gateway routes the request to another model so your support bot keeps responding. You define the chain; the gateway handles the failover transparently.

Can I reduce cost on repetitive support questions?

Yes. Support traffic is full of repeated context and similar questions. Prompt caching lets you avoid paying full price for the same system prompt and knowledge-base context on every message, which meaningfully lowers cost at support volumes.

Can I see the cost of each support conversation?

Every request is logged with its model, token counts, latency and dollar cost. By assigning keys or metadata per channel or environment, you can attribute spend to conversations, teams or customer segments.

Do I need to change my chatbot code to use it?

No. The gateway is OpenAI-compatible. If your bot already calls the OpenAI API, you change the base URL and key — the rest of your integration stays the same.

One API for every model

Route across 200+ models with fallback, caching and per-request cost analytics. Start free in minutes.

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