Call Multiple LLMs
Through One Production API

TL;DR

What the unified call should standardize

Standardize application concerns that are genuinely common: service identity, tenant, trace context, workload alias, messages, timeout, budget, required capabilities and a stable response envelope. Resolve the alias from a versioned catalog before provider translation.

Do not force every feature into the common denominator. Use typed extensions for provider-specific tools, reasoning, schema, media or caching controls. Reject missing required capabilities before the call rather than silently degrading them.

One-API integration layers

Build the common contract around workloads

Prove each model path

• Create golden requests for each production workload and required capability combination.
• Run contract tests against every eligible GPT, Claude and Gemini endpoint.
• Diff outputs, tools, streams, errors, usage and latency under real payloads.
• Canary route changes and block on workload-specific invariant regressions.
• Keep provider-native direct paths for diagnosis and tested rollback.

Use aliases above typed provider adapters

The application sends a canonical request with workload alias and required capabilities. A catalog expands eligible endpoints; a policy router selects one; a typed adapter maps the request. The normalized response returns with provider-native identity and evidence. Unsupported requirements fail before execution, and fallback uses only verified equivalents.

Design the canonical request contract first

Start with the behavior your application needs, not with one provider's JSON. A useful request includes a workload alias, ordered messages, required capabilities, optional preferences, timeout, maximum spend, tenant, trace context, and an idempotency key where retries can create side effects. Keep hard requirements separate from preferences: JSON schema compliance may be required, while lower latency may only be a preference.

The response should be equally deliberate. Return normalized content and tool calls, then attach the selected provider, exact model version, gateway request ID, upstream request ID, routing policy, attempt count, usage units, finish reason, and any lossy transformation. That envelope gives application code stability without erasing the evidence needed for debugging.

Filter by capability before optimizing the route

Build a versioned capability catalog for every allowed model endpoint. Record supported input modalities, tool calling, parallel tools, structured output, streaming, context limits, regions, data handling, lifecycle status, and known workload quality. Refresh the catalog from official documentation and verified probes; do not let a marketing model name stand in for an executable capability claim.

Routing is a two-stage decision. First remove candidates that fail a hard requirement. Only then rank the eligible set by quality, latency, availability, price, residency, or tenant policy. Record both the candidate set and exclusion reasons so “smart routing” remains explainable when a result or bill is challenged.

Keep provider translation inside typed adapters

Each adapter owns translation to and from one native API: message roles, system instructions, tools, schemas, media, sampling controls, stop reasons, error types, rate-limit headers, and usage. The adapter must reject an impossible mapping. Silently dropping a schema, tool choice, image, or safety option creates a successful HTTP response with the wrong behavior.

Avoid a lowest-common-denominator API. Keep a compact portable core and expose namespaced, typed extensions for features that are intentionally provider-specific. The caller then knows whether it requested portable behavior or accepted a lock-in boundary.

Treat streams, tools, and structured output as protocols

A stream is more than text chunks. Providers can emit content deltas, reasoning events, tool arguments, citations, usage, and terminal metadata in different orders. Define a gateway event model, test chunk assembly and cancellation, and preserve the native terminal reason. Once bytes have reached the caller, automatic fallback can duplicate text or produce a mixed answer.

Tool calling also needs a lifecycle: validate arguments, authorize the tool, attach a stable call ID, record the result, and decide who resumes the model. For structured output, test the exact schema constructs each route supports and validate the final payload again at the gateway boundary.

Use one client key without flattening upstream secrets

Applications should authenticate to the gateway with a scoped credential tied to service, tenant, environment, and allowed workloads. Provider keys stay encrypted behind the gateway. Give each secret an owner, environment, provider account, rotation schedule, allowed routes, and emergency revocation path. A single leaked client token should not reveal or unlock every upstream account.

Enforce per-tenant quotas, concurrency, model allowlists, and spend limits at the gateway. Redact prompts and credentials from logs by default, and make any payload retention an explicit policy rather than an observability side effect.

Make fallback narrow, bounded, and observable

Retry transient failures with capped exponential backoff and jitter, while respecting provider retry guidance. Fallback may select only a prequalified equivalent for the same workload. Carry the original deadline and budget across attempts; do not give every retry a fresh allowance. Stop on authentication, invalid requests, policy rejections, and unsupported features.

Never blindly replay a request after a partial stream or a side-effecting tool call. Use idempotency keys, tool execution ledgers, and explicit resume rules where recovery is necessary. Return the attempt history so operators can distinguish a slow first route from a successful but expensive fallback chain.

Measure accepted work, not just token price

Normalize usage into a cost record without pretending provider units are identical. Store native input, output, cached, reasoning, image, audio, and tool-related units alongside the provider's billed amount and currency. Join that record to workload, tenant, route, latency, retries, quality score, and acceptance outcome.

The useful comparison is cost per accepted task, not the advertised price per token. Track success rate, p50 and p95 latency, schema-valid rate, tool success, fallback rate, budget overruns, and quality by alias. Alerts should include the gateway request ID, chosen path, policy version, and upstream evidence.

Production checklist for one multi-LLM API

• Version the request, response, aliases, capability catalog, and routing policy independently.
• Fail before execution when no route satisfies every hard capability and policy requirement.
• Contract-test every adapter for text, streams, tools, schemas, errors, cancellation, and usage.
• Keep upstream secrets isolated; scope client credentials and budgets by tenant and workload.
• Canary alias changes, compare golden workloads, and retain a tested rollback path.
• Expose selected provider, exact model, transformations, attempts, native IDs, usage, and cost.

Seven mistakes that make a unified API unsafe

One model API plus one capability interface

The unified model API gives an agent consistent inference access. QVeris gives that agent Discover → Inspect → Call access to external APIs, tools, services and live data. Stack them as model interface plus capability interface, sharing workload identity and trace context.

FAQ

Official sources and further reading