Stigg

Stigg positions itself as critical infrastructure for operational management in the AI economy. This platform acts as a real-time execution engine that determines which actions are authorized for each customer, user, or autonomous agent. Its architecture is designed to solve the technical challenge of controlling AI spend and feature access, allowing every request to be evaluated in milliseconds before consuming expensive computational resources.

Stigg’s operation centers on an enforcement layer that integrates credits, entitlements, usage metering, and governance into a single control point. It utilizes a low-latency (p99 < 10ms) approach to ensure the user experience remains unaffected. The tool can be deployed via a cloud API or within the client’s own data center using a BYOC (Bring Your Own Cloud) model, guaranteeing data sovereignty and regulatory compliance in Enterprise SaaS environments.

The key features of this infrastructure tool include:

Credit Ledger: A financial-grade system that manages wallets, expiration rules, and automatic deductions with double-entry integrity to prevent data loss.

Dynamic Entitlement Engine: Allows defining feature configurations and model limits (such as GPT-4 or Claude) outside the source code, facilitating plan updates without additional software deployments.

Usage Metering at Scale: Capable of ingesting over one million events per second to track tokens, inference calls, and agent actions in real time.

Spend Governance: User-configurable budget controls, allowing for strict limits or alerts for specific teams and departments.

For engineering teams and product managers, Stigg eliminates the need to build complex internal systems for quota management and usage-based billing. The tool enables bidirectional synchronization with traditional billing providers, acting as an intelligence layer that adds granular visibility into where AI resources are being consumed. This is essential for companies that need to iterate quickly on their monetization models without bogging down their technical development cycles.

Thanks to its ability to flexibly handle managed entities, such as customer accounts or individual agents, the platform adapts to the complexity of new consumption-based business models. Its technical approach ensures there are no lags in balance reconciliation and that every access decision is backed by instantly updated usage data, optimizing AI governance and operational cost control.