One cost dashboard for OpenAI, Anthropic, Gemini, and every other provider you use
Very few teams stay on a single AI provider for long. One feature ships on GPT-4o because that's what the team knew when they started. A second feature moves to Claude because it handles a specific task better. A third routes to a fast, cheap open-weight model through Groq for latency-sensitive work. Each of these is a reasonable, independent decision — and each one adds a provider console with its own invoice, its own dashboard, and no connection to the others.
The result is a familiar one: nobody can answer “what did we spend on AI last month, total, across everything” without manually adding up numbers from three or four different consoles, each with its own login, its own export format, and its own definition of a billing period.
Why provider consoles can't solve this on their own
Every provider console does one job well: show you spend on that provider. None of them have any visibility into what you're spending elsewhere, because they have no reason to — each provider is optimizing its own billing experience, not your cross-provider view. Asking a provider console to show combined spend across competitors is asking it to do something fundamentally outside its purpose.
The manual workaround — exporting each console's invoice and combining them in a spreadsheet — works for a single retrospective look, but it doesn't scale into something you can act on in real time. By the time the exports are collected and combined, the spend has already happened, and the combined view is stale the moment it's finished, same as any manual reconciliation process.
How a single proxy layer solves it structurally
Cognocient proxies seven AI providers — OpenAI, Anthropic, Google Gemini, Mistral, Groq, Together AI, and Azure OpenAI — through a single base_url. Your application code points at Cognocient instead of each provider directly, and Cognocient detects the provider automatically from the model name — gpt-4o routes to OpenAI, claude-sonnet-4-6 routes to Anthropic, gemini-2.0-flash routes to Google — with no separate SDK, no re-authentication, and no new keys to manage per provider.
Every call, regardless of which provider ultimately serves it, is recorded in the same database with the same attribution headers, the same waste detection, and the same budget enforcement applied consistently. Switching a feature from one provider to another for a cost or quality reason becomes a one-line model-name change in application code — the cost tracking, attribution, and budgets around that feature keep working without any reconfiguration. See the full provider support reference for supported models and setup per provider.
What this looks like in practice
Consider a hypothetical team running three features on three different providers: a support chatbot on GPT-4o-mini for cost efficiency, a document-analysis feature on Claude Sonnet for long-context handling, and a real-time classification step on Groq's fast inference for latency. Before consolidating onto a single proxy, answering “what's our total AI spend this month” meant logging into three consoles and adding three numbers by hand, each with a slightly different billing-cycle boundary.
After consolidating, the same three features appear as three rows in one dashboard, broken down by feature and by provider simultaneously — so a provider-level cost comparison (“is the Claude feature actually more expensive per call than the GPT-4o-mini feature”) is a filter, not a research project. This is illustrative of how the combined view behaves, not a specific customer's reported outcome.
A single proxy across providers is also what makes per-feature and per-department attribution and budget enforcement work consistently across your whole AI stack instead of separately per provider — one set of rules, applied uniformly, regardless of which provider ends up serving any given call.