AI is the most expensive infrastructure
most companies don't manage.

Every company building with LLMs ends up at the same place: an invoice from OpenAI, Anthropic, or Google that's two, three, ten times larger than last month — and no clear answer to which team spent what, which calls were necessary, or which models could have been cheaper.

Cloud computing went through this exact cycle a decade ago. It produced an entire industry — FinOps — dedicated to making cloud spend visible, attributable, and optimizable. AI doesn't have that yet. Most teams pick one premium model, call it for everything, and watch the bill compound.

Trimio is the FinOps layer for that gap. One URL change in your stack, and every call gets compressed, routed to the cheapest model that still hits your quality bar, and served from cache when it can be — automatically, transparently, and with a per-request audit trail your CFO can sign off on.

Trimio sits between your application and the LLM providers — OpenAI, Anthropic, Google, AWS Bedrock, Azure, and 1,600+ models in between. Every request passes through four optimization engines, in parallel:

Least Cost Routing. Real-time complexity scoring picks the cheapest capable model on every call. You set the quality floor; we pick the model that hits it.

Token Compression. Structure-preserving compression strips redundancy before any token reaches the provider. Per-request token deltas in the response headers.

Provider Cache Optimization. Sophisticated cache-aware request handling that maximizes hits against Anthropic's, OpenAI's, and Google's native prompt caching — 93% average savings on hits.

Model Upgrade Detection. Continuous analysis of your traffic flags task categories that are systematically over-served by premium models, with quantified savings and a one-click reroute.

Together, customers see 30–60% lower AI bills from day one, with no application code changes and per-request audit logs covering every routing decision and every dollar saved.

Trimio is built by engineers and operators who have spent years inside large-scale AI systems — from the infrastructure layer of major cloud providers to the inference path of frontier-model products that serve millions of requests a day.

We've watched first-hand how AI bills get out of control: how a single prompt template change can 5× a model's token bill overnight; how "just use the best model" becomes the default that costs millions a year; how the visibility tooling teams need to manage the spend simply doesn't exist outside a few of the largest companies.

We're building the platform we wished we had — opinionated where it matters (the proxy, the optimization engines, the audit trail), pragmatic everywhere else (drop-in compatibility with the SDKs your team already uses).