AI MIDDLEWARE · ENTERPRISE AI STACK
The middleware layer between your apps and every AI.
Applications call one OpenAI-compatible endpoint. Kosmoy speaks LLM, MCP and A2A outbound. Policy, logging and cost attribution live in between — owned by you, not by a cloud or model vendor.
AI middleware is the software layer that sits between enterprise applications and the AI services they consume — models, tools and agents — and standardises access, policy and telemetry across providers. Instead of every team wiring its own SDKs, keys and logging, applications call one endpoint and the middleware handles authentication, guardrails, routing, budgets and audit.
Kosmoy is AI middleware built for the regulated enterprise. It is hyperscaler-independent — the one layer in the AI stack no cloud or SaaS vendor owns — and runs single-tenant in your own Kubernetes on Azure, AWS, GCP or on-prem, air-gapped if needed. Swapping a model is configuration, not a rewrite.
What it does.
One API, every provider
OpenAI, Anthropic, Google, Mistral, Meta, on-prem vLLM and fine-tuned SLMs behind one OpenAI-compatible endpoint.
Policy in the path
Guardrails — PII, toxicity, prompt injection — plus RBAC and budgets, enforced on every call. Configured once, not rebuilt per app.
Three protocols outbound
OpenAI-compatible to LLMs, MCP to tool servers, A2A to agents. One middleware path for all three.
Registries underneath
Every model, MCP server and agent the middleware can reach is registered, owned and risk-classified in the AI Inventory.
Observability built in
Cost, usage, latency and feedback attributed by team, app, model and use case — FinOps for AI, as it happens.
No lock-in
Model-agnostic and cloud-portable. The middleware belongs to you; the providers stay swappable.
Module questions, answered straight.
What is AI middleware?
AI middleware is the software layer that sits between enterprise applications and the AI services they consume — models, tools and agents — and standardises access, policy and telemetry across providers. Instead of every team wiring its own SDKs, keys and logging, applications call one endpoint and the middleware handles authentication, guardrails, routing, budgets and audit.
How is AI middleware different from an AI gateway?
The gateway is the enforcement point in the data path — the component that authenticates, guardrails, routes and logs a call. AI middleware is the full intermediary layer around it: the registries that say what exists, the monitoring that says what it costs, the policy that says what is allowed, and the evidence trail that proves it. The Kosmoy AI Gateway is the data plane of the Kosmoy middleware layer.
Does AI middleware add latency?
Latency is proportional to the guardrails enabled. Built-in fast-path checks (regex, list-based PII) are sub-10ms; fine-tuned SLM guardrails are sub-200ms; frontier-model guardrails — used only where policy explicitly demands them — are slower. Routing overhead is negligible against model inference time.
Why shouldn't AI middleware come from a hyperscaler or model vendor?
Middleware is where policy, keys, routing and audit live. If one cloud or model vendor owns that layer, switching providers means rebuilding your controls — that is the lock-in. An independent middleware layer lets you use every provider and move between them while the policy and the audit trail stay yours. Kosmoy is the one layer in the AI stack no cloud or SaaS vendor owns.
What changes in our application code?
Usually the base URL. The middleware speaks an OpenAI-compatible API, so Python services, LangChain, LlamaIndex and any OpenAI-pointing tool swap one line. Off-the-shelf software that supports Bring-Your-Own-Model points its endpoint at Kosmoy and keeps its own UX.
See Kosmoy as your AI middleware.
One endpoint in front of every provider — auth, guardrails, routing and cost attribution, live.