Stigg 2.0 Enforces AI Request Costs in Under Five Milliseconds, Deploys in Customer Cloud

Stigg released Stigg 2.0 on June 30, 2026 at the AI Engineer World's Fair in San Francisco, repositioning the company as usage runtime infrastructure for AI products. The rebuilt platform enforces credits, entitlements, and budgets at the individual request level, with a decision latency of under five milliseconds.

What Stigg 2.0 Actually Does

The core function is enforcement, not just metering. Stigg 2.0 sits in the request path of an AI product and resolves, on each call, what that request is permitted to cost — checking credits, entitlements, and budget constraints before the workload proceeds. The sub-five-millisecond figure is the operationally relevant number: at that latency, the enforcement layer does not materially add to end-user response times, which matters for any product where inference speed is a selling point.

The company describes the platform as a "usage runtime," a framing that distinguishes it from billing dashboards or after-the-fact cost attribution tools. The distinction is meaningful to builders: a runtime enforces in-flight, whereas analytics tools report after the fact.

Customer-Cloud Deployment

Stigg 2.0 deploys into a customer's own cloud environment. For enterprise AI product teams, that architecture addresses a recurring procurement objection — sending usage data and entitlement checks to a third-party SaaS introduces a dependency and a potential data residency issue. Running the runtime inside the customer's own infrastructure keeps sensitive request metadata off Stigg's servers entirely.

This deployment model also has implications for reliability. An enforcement layer that lives inside the customer's cloud does not create an external single point of failure on the critical request path.

Context: Why Usage Controls Are Becoming Infrastructure

As AI products move from prototype to production, uncontrolled inference spend has become a recurring line-item problem. Credits, entitlements, and per-customer budgets are the mechanisms product teams use to make unit economics predictable — but implementing them in application code is error-prone and non-trivial to audit. Stigg 2.0's pitch is that this logic belongs in dedicated infrastructure, enforced consistently across every request rather than scattered across product codebases.

The AI Engineer World's Fair was the launch venue, putting the announcement directly in front of the engineering and product audience most likely to own this purchasing decision.