Run agents in production without burning your budget.
Inference is expensive, latency-sensitive, and easy to ruin. We build the platform layer underneath your agents — the part that keeps them fast, observable, and within budget at scale.
What we offer
- →Multi-provider inference routing (Anthropic / OpenAI / open weights)
- →Caching layers — prompt caching, response caching, semantic cache
- →Cost telemetry per agent, per workflow, per customer
- →Latency budgets and graceful degradation
- →Self-hosted model deployment (vLLM, TGI, Ollama)
- →LLM observability — traces, evals, and replay
How we work
Caching is the cheapest performance win
Prompt caching alone often cuts inference cost 60–80% and latency in half. We instrument it before tuning anything else.
$/conversation is the only metric at scale
Per-token pricing hides the real cost of long-running agents. We track end-to-end spend and tie it to user value, not API calls.
Vendor lock is real — design for migration day one
Provider-specific shortcuts age fast. Abstraction layers we ship are boring on purpose: swap Anthropic for open-weights without rewriting the agent.
Inference bill spiraling, or latency tanking?
Send us your current setup and where it's hurting — we'll come back with a no-fluff next step.