Best LLM API Aggregators

Check the exact models, providers, regions, modalities, context limits, tools, JSON modes, and parameters used by your workloads.

Score retries, fallback, circuit breaking, load balancing, health signals, model aliases, and the ability to pin versions.

Model provider price, platform fee, credit fee, BYOK fee, caching, failed attempts, rate limits, and support cost separately.

Review virtual keys, budgets, roles, retention, redaction, exports, audit logs, regional routing, and an exit plan.

Include short chat, long context, structured extraction, tools, images, streaming, and safety-sensitive requests in the same proportions as production.

Capture resolved model, provider, region, latency, tokens, cost, cache status, fallback reason, and output-quality score for every request.

Throttle providers, inject 429 and 5xx errors, break streams, and remove preferred models to observe real fallback behavior.

Export logs and config, reroute one workload directly or through another gateway, and measure the work required.

Hundreds of models do not help if the five you need lack stable parameters, regions, throughput, or support.

Automatic fallback without route logs can hide provider changes, cost spikes, and quality regressions.

Separate projects, environments, customers, and teams so budgets, revocation, and incident analysis remain possible.

Inference aggregation and external capability aggregation require different catalogs, permissions, provenance, and audits.

A managed marketplace can minimize setup when the goal is comparing models quickly. Use separate development keys, strict budgets, non-sensitive test data, and a reproducible evaluation set so experimentation does not become an ungoverned production dependency.

A self-hosted proxy or enterprise gateway can give a platform team consistent identity, policy, routing, and usage attribution across products. The organization must fund ongoing ownership rather than treating the gateway as a one-time integration project.

Prioritize stable capacity, transparent provider routing, predictable fees, caching, route-level telemetry, version controls, and tested fallback. Benchmark with the real distribution of short, long, streaming, tool, image, and failed requests—not an average prompt.

Use the aggregator for model selection and inference. Add a capability layer such as QVeris when the agent must find and call market data, filings, fundamentals, news, or actions with explicit provenance and permissions.

Model inference, platform subscription, credit purchase, BYOK volume, support, data retention, egress, evaluation, and enterprise minimums may be priced differently. Build a twelve-month model using your actual traffic and growth assumptions.

Ask whether retries, provider fallbacks, cancelled streams, safety rejections, cache misses, and partial outputs are billed. Confirm whether the aggregator's usage record can be reconciled with underlying provider invoices and application traces.

Published request limits rarely describe burst behavior, per-model capacity, account-wide quotas, provider-specific throttling, or enterprise reservations completely. Load-test representative concurrency and inspect both gateway and provider-side rate-limit signals.

Require configuration and log exports, clear credit and refund terms, provider and subprocessor disclosure, model retirement notice, termination assistance, and a documented path for migrating keys, aliases, budgets, and historical usage.

Require the actual provider, model version, region, route history, latency, usage, and price basis on every response. A successful request is not auditable if the application cannot explain who served it.

Compare aggregator usage with application traces and underlying provider records. Include retries, partial streams, cache reads, safety rejection, fallback, credit markup, and failed attempts in the reconciliation model.

Export aliases, routing rules, virtual keys, budgets, and logs, then rebuild a representative route elsewhere. Portability is demonstrated by a completed rehearsal, not an OpenAI-compatible endpoint claim.

Map prompt, response, metadata, and log copies across aggregator, provider, observability, and support systems. Verify retention, redaction, region, subprocessors, deletion, and incident-notification terms.