BlogSet up a managed gateway to route requests across OpenAI, Anthropic and Google models.
Concentrate.ai
You can use Concentrate to access OpenAI, Anthropic and Google models through one managed API. Start with one workload, confirm its model-specific behavior, then add the keys, budgets and fallback paths your team needs.
LLM Gateway
Change models, providers, add fallbacks, and more by running all AI usage through one platform.
If you call this an SI gateway or super intelligence gateway, the integration steps remain concrete: authenticate a request, select a supported model, inspect the result and decide how failures should be handled. The name does not change the model’s capabilities.
Create a Concentrate workspace and an API key for the application you are testing. Keep development traffic separate from production so an experiment has its own usage history and budget.
Store the key in your secret manager or deployment environment. Do not put it in browser code, a public repository or a shared example. Record which team owns the key before distributing access.
For an OpenAI-compatible client, set the base URL to https://api.concentrate.ai/v1 and supply the Concentrate key. Select the exact supported model identifier for the OpenAI, Anthropic or Google model you want to call.
Keep the first test small: a short input, a bounded output and no external tool actions. Confirm authentication and response handling before testing longer conversations or agent loops. An API-compatible request format does not make every provider-specific parameter interchangeable.
Run representative inputs against each candidate model. For extraction, compare required fields and invalid-output behavior. For coding, check whether the result passes your existing tests. For chat, inspect streaming events and cancellation handling.
Test tool calling and structured outputs separately when your application depends on them. Keep the model, provider path and settings in your results so you can reproduce a comparison.
Confirm the supported scope and behavior of your account’s access and spending controls, then add application limits on agent iterations and output length. Review usage by owner instead of sharing one unrestricted key across unrelated jobs.
Choose acceptable fallback destinations explicitly. Moving to another provider serving the same model is different from moving to another model. Test both the response and the total request duration. Our guide to OpenAI RPM, TPM and TPD limits explains why request counts and token volume need separate attention.
Keep a rollback path while comparing output quality, errors, latency and billed usage. Expand only after the application behaves as expected under normal requests, rate limits and failures.
Start with Concentrate and one application key. Add more models when you have a specific workload for them, with an owner and a budget for each.
Concentrate combines managed inference with BAA-covered access for approved workloads. Confirm the covered account, models, provider endpoints, retention and fallback destinations before sending PHI. The general model catalog is not a list of universally eligible healthcare routes.