The Foundation
for Enterprise AI.

Actualyze is an enterprise AI governance platform and AI gateway, one control plane between your applications and AI providers. Each call routed through it is checked for access, held to its budget, scanned for PII, routed, and recorded in an append-only audit trail. Team access and budget authority are separate: who may use a model and which budget pays are decided by different roles.

An AI gateway is a proxy between your applications and model providers: one endpoint for many models, with routing, failover, and usage logging. Many gateways add budgets, guardrails, and central policy. Actualyze does that job and adds the structure an enterprise runs on: Team Owners approve what their own team deploys, Budget Managers own the budgets that pay for it, and neither overrides the other. Each model's approval requirement sets who signs off before a team can deploy it. Our AI gateway comparison sets Actualyze beside the other AI gateways, row by row.

To connect an AI client or application to Actualyze, point it at Actualyze instead of your model provider. Most AI clients, SDKs, and apps let you set a custom base URL, so you swap in the Actualyze URL, add your Actualyze API key, and choose a model. Your code stays the same, and every call now runs through your access rules, budgets, and guardrails. Our Labs section has step-by-step setup guides for Claude Code, Codex, Cursor, GitHub Copilot, and other popular clients.

Actualyze works with any model served behind an OpenAI- or Anthropic-compatible API. Providers include OpenAI, Anthropic, Google, Amazon Bedrock, Azure OpenAI, and OpenRouter. vLLM or Ollama servers you already operate connect through a relay you run, which only makes outbound connections. Model Groups let the same model serve through any provider a caller may already use.

Actualyze controls AI spend through budgets. Every API key is bound to one team and one budget when it is created, and every call charges exactly that budget. When a budget reaches its monthly limit, the next call stops with a 402 before any model is contacted, and one alert threshold warns you before that point. A key's holder can also set a monthly limit on the key itself, and an optional workspace COGS ceiling stops spending at a limit on confirmed provider cost, with estimated spend shown beside it as exposure. The Control AI Cost use case shows how budgets, key limits, and chargeback fit together.

Actualyze guardrails mask PII by default on prompts and responses, using a fixed catalog of built-in shapes (personal names and dates can be turned on), and each guardrail runs in monitor mode (log only) or enforce mode (mask). For agents, tool-call controls block risky tool calls with allowlists, URL filtering, and parameter and result scanning. Guardrails mask PII, not clinical health data. Data you forward to a SIEM carries metadata only, never prompts or responses. More detail is in the Secure AI Usage use case.