How to Build an LLM Gateway (Playbook)

Every application that calls a model provider directly reimplements retries, timeouts, logging, and cost tracking, badly and differently. An LLM gateway centralizes those concerns: one API, routing and fallbacks, caching, cost accounting, logging, and policy. It is the first platform component FISTA builds in an AI enablement engagement because it makes every subsequent application cheaper, safer, and observable. This playbook covers the build. Concepts are in what is an ai gateway and what is an llm router.

What does the gateway do?

Capability	Function
Unified API	One interface for chat, completion, embeddings, and tools across providers
Routing	Logical model names mapped to providers and versions by policy
Fallbacks	Automatic failover on errors, timeouts, rate limits
Caching	Prompt and response caching; shared context caching
Cost accounting	Tokens and cost per request, feature, tenant, model
Logging	Structured traces with redaction
Policy	Rate and spend limits, content screening, data-handling rules
Keys and identity	Per-application credentials; provider keys held centrally

Step 1: Define the API and logical models

Design an internal API covering the operations applications need, with request metadata for identity, feature, tenant, and purpose. Define logical model names or task classes (for example, fast-classification, high-quality-drafting) that applications request, so provider and version decisions live in gateway policy rather than in application code.

Step 2: Implement provider adapters and routing

Build adapters per provider that normalize requests and responses, handle streaming, and surface errors uniformly. Implement routing policy: logical name to provider and pinned version, with cost and latency considerations, and per-tenant or per-feature overrides. Pin versions explicitly; provider model updates are adopted only after evaluation. See how to choose an llm provider.

Step 3: Add fallbacks and resilience

Configure fallback chains per logical model: alternate providers or models on errors, timeouts, and rate limits, with circuit breakers and backoff. Define degraded modes for when all options fail. Test failover by injecting failures. Resilience patterns are in what is a fallback model and the LLM production readiness whitepaper.

Step 4: Implement caching

Cache exact-match prompts and responses where semantics allow, with TTLs and invalidation keyed to prompt version; use provider prompt-caching features for shared system prompts and context; and consider semantic caching for high-volume near-duplicate queries with validation. Report cache hit rates. See what is prompt caching.

Step 5: Account for cost

Record tokens and cost per request using provider pricing tables, attribute to feature, tenant, application, and model, and expose dashboards and exports for engineering and finance. Cost accounting is what turns AI spend from a monthly surprise into a managed line. See how to build an ai cost dashboard and llm api cost optimization.

Step 6: Log with redaction

Emit structured traces per request: identity, logical and actual model, prompts and responses with redaction per data classification, token counts, latency, cache status, routing and fallback decisions, and errors. Feed traces to the observability platform and, sampled, to evaluation. Access to raw traces is controlled and logged. See the AI observability whitepaper and ai data leakage prevention.

Step 7: Enforce policy

At the gateway, enforce rate limits per application and tenant, spend budgets with alerts and hard stops, content screening for inputs and outputs where policy requires, data-handling rules such as blocking sensitive classes from specific providers, and context and output size limits. Policy is configuration, versioned and reviewed. Security context is in ai secrets management and ai access control.

Step 8: Roll out incrementally

Migrate applications behind the gateway one at a time, starting with non-critical ones, validating behavior and latency, and retiring direct provider keys as each moves. Provide client libraries and documentation. The gateway should be invisible to applications that use logical models correctly. Track adoption and remaining direct integrations.

Worked example: consolidating five applications

An organization has five LLM applications built by different teams, each with its own provider keys, retry logic, and no shared cost view. The gateway is built with logical models for classification, extraction, drafting, and embeddings, adapters for two providers, pinned versions, fallback chains, exact-match and shared-context caching, per-feature cost accounting, redacted tracing, and spend budgets per team. Applications migrate one per fortnight, starting with an internal summarizer. Within a quarter, all five run behind the gateway; a provider outage triggers automatic failover without incident; cost dashboards reveal one application spending most of the budget on an oversized context, which is fixed; and a provider model update is caught by the evaluation run in staging before it changes production behavior.

What does it cost to run?

The gateway adds modest latency and infrastructure cost and typically pays for itself through caching, routing, and the spend visibility that drives optimization. Drivers are in cost of running llms in production.

What are the common mistakes?

• Applications bypassing the gateway with their own keys.
• Routing by provider name rather than logical models, so migrations require code changes.
• No version pinning, so provider updates change behavior silently.
• Logging without redaction, creating a sensitive-data store.
• Cost tracked in total but not per feature.
• Fallbacks configured but never tested.

What team owns the gateway?

A platform team owns the gateway as shared infrastructure: its API, routing policy, provider adapters, cost reporting, and on-call. Application teams consume it through logical models and client libraries and request new capabilities through a lightweight intake. Treating the gateway as one team's side project is how it decays into a bottleneck.

How do you roll out a gateway to existing applications?

Start by proxying traffic transparently with no behavior change, gather attribution and latency data, then enable caching, routing, and fallbacks feature by feature with evaluation on each. Applications should never notice the gateway except through lower cost and fewer outages.

How FISTA Solutions builds LLM gateways

FISTA Solutions builds LLM gateways to this playbook as the foundation of its AI enablement platform work: logical-model API, provider adapters with pinned versions, tested fallbacks, caching, per-feature cost accounting, redacted tracing, policy enforcement, and incremental migration. Every AI agent FISTA delivers runs behind one, and forward deployed engineers integrate it with your identity, secrets, and observability systems. The record behind the work is 150+ projects with 99.9% uptime.

To scope an LLM gateway, or to consolidate existing applications behind one, message FISTA on WhatsApp, or read how to build a prompt management system for the companion component.