Actualyze AI raises $7M to govern enterprise model calls

Why it matters

AI agents can turn one task into many model calls, multiplying cost and data exposure. Actualyze is betting that enterprises will require a neutral control point before those calls reach providers.

Rafi Khardalian and Sean Lynch, the founders who sold private-cloud operator Metacloud to Cisco, brought Actualyze AI out of stealth on August 3 with $7 million in seed funding and an early-access platform for controlling enterprise AI traffic.

Actualyze named Storm Ventures, Canaan Partners, Morado Ventures and Jerry Yang's AME Cloud Ventures as investors in an announcement distributed by PR Newswire. Actualyze is opening its hosted service through a Design Partner Program.

The founders are returning to a familiar infrastructure problem: software adoption moving faster than the systems built to control it. Khardalian was Metacloud's founding chief technology officer, while Lynch was its co-founder and chief executive. Cisco announced its intent to acquire Metacloud in September 2014 to add managed OpenStack private clouds to its cloud portfolio.

Their earlier work also overlapped at Ticketmaster. Lynch ran global technical operations, according to Actualyze, while Khardalian built distributed infrastructure including Spine, a configuration-management system that a 2008 USENIX presentation said managed over 4,000 systems worldwide.

That operating history explains Actualyze's product choice. Khardalian and Lynch are putting their software directly in the path between an enterprise application and the models it calls, a demanding position where security, reliability and response time all matter.

One governed route to every model

Actualyze uses an OpenAI-compatible endpoint intended to replace the model-provider URL inside an application. Requests then pass through Actualyze before reaching services from OpenAI, Anthropic, Google, Azure, AWS Bedrock, Mistral or self-hosted models. Actualyze says the same connection can also support local inference software such as Ollama and vLLM.

For each request, Actualyze aims to identify the person, team and application responsible; apply access and budget rules; inspect prompts for sensitive information; record an audit trail; and route the request to an eligible model. Actualyze's "virtual models" are designed to choose providers based on capability, cost and quality, with failover when a provider is unavailable.

The model abstraction gives Actualyze a practical entry point. Enterprises increasingly have applications wired to several providers, each with separate credentials, billing records and data policies. Actualyze wants to become the place where platform, security and finance teams set those rules once.

Khardalian sharpened the cost argument in his launch essay. A retry loop, verbose prompt or agent repeatedly loading its context can increase token usage without showing which internal feature caused the bill. Provider dashboards usually organize usage around accounts, projects and API keys. Actualyze proposes attaching that usage to internal teams and budgets before the request is processed, including refusing calls after a budget is exhausted.

Agents make that accounting problem harder because one user action can trigger a series of model and tool calls. "Agents raise the stakes, fanning a single task into dozens of autonomous calls," Khardalian said in the launch announcement.

Metacloud's playbook, applied to inference

Actualyze says Khardalian and Lynch spent the past year speaking with enterprise platform, security and finance leaders. Their thesis closely tracks the lesson behind Metacloud: enterprises want access to a fast-changing infrastructure layer while retaining central control over data, operations and spending.

The technical burden is substantial. Actualyze must handle sensitive prompts and provider credentials while remaining fast and available enough to sit in the critical path of production applications. Provider APIs, model names, prices and behavior also change frequently. A control plane that promises provider independence has to absorb those changes without introducing another source of outages or inconsistent responses.

Actualyze's first release covers access governance, prompt and response security, model operations, and cost-based routing. The integration catalog also lists identity connections using SAML and OIDC, along with exports to security monitoring, data warehouse and finance systems.

The hosted service is in early access. Actualyze's site says an on-premises version for data-residency and air-gapped environments is planned for 2027. The initial design-partner model gives the founders a way to develop the product around a small number of enterprise deployments before attempting a broader rollout.

A control point attracting large platforms

Actualyze is entering a market with specialist gateways, open-source proxies and established infrastructure vendors. Portkey built an AI gateway around observability, routing and governance. Palo Alto Networks described the gateway as a control point for monitoring and governing autonomous agents.

Kong is pushing its existing API infrastructure deeper into the same request path. On April 14th, Kong added agent-to-agent traffic support alongside its controls for model and Model Context Protocol traffic. Developer-oriented projects including Helicone and LiteLLM address adjacent needs such as observability, provider abstraction and spend tracking.

Actualyze's bet is that enterprise buyers will prefer one enforced route that combines identity, security policy, chargeback and model selection. The founders' background gives them credibility with buyers responsible for infrastructure that cannot fail. It also raises the standard for the product: Actualyze is asking enterprises to trust an early-access service with every model request, the credentials behind those requests and the data inside them.

The $7 million seed round buys Khardalian and Lynch time to prove that this control point can remain invisible to developers while giving security and finance teams the authority they lack today. If they can do that, Actualyze will sit in one of the most valuable positions in the enterprise AI stack: between growing inference demand and the providers billing for it.