AI Usage, Under Control

Product Introduction

1. Definition: Stigg is a real-time AI usage runtime and governance layer. It is a technical infrastructure component that sits between an application's core logic and its billing system (like Stripe or Zuora) to enforce usage policies, manage credits, and meter consumption at the point of request.
2. Core Value Proposition: It exists to give AI product teams precise, millisecond-level control over user consumption, preventing revenue leakage from overdrafts and enabling complex, self-serve monetization models without engineering bottlenecks. Its primary value is real-time enforcement of AI usage and entitlements, replacing slow, error-prone post-hoc invoice reconciliation.

Main Features

1. Real-Time Enforcement Runtime: This is the core engine. It performs entitlement checks and credit deductions in the application's request path, with a p99 latency of under 10ms. How it works: When a user makes an AI request (e.g., an API call for GPT-4, a Claude inference, an image generation), the application calls Stigg's SDK or API before processing the request. Stigg evaluates the user's plan, remaining credits, and feature access in real-time, returning an allow/deny decision. This happens locally in BYOC (Bring Your Own Cloud) deployments for maximum speed and security.
2. Financial-Grade Credit Infrastructure: Manages the entire lifecycle of AI credits, tokens, or compute units. It provides virtual wallets, double-entry immutable ledgers for auditability (ASC 606 compliant), configurable expiry rules, and priority consumption logic (e.g., use trial credits before paid credits). This solves edge cases like concurrent deductions and ensures zero overdraft by authorizing transactions against a real-time balance.
3. Entitlements as a Service: Allows product and growth teams to define and manage feature access rules (entitlements) outside the application codebase. Unlike simple feature flags, Stigg's entitlements are tied to customer tiers, usage meters, and credit balances. Teams can ship new AI models, adjust rate limits, or create new pricing tiers via a UI or API without requiring a code deployment from engineering.
4. High-Volume Metering Engine: Ingests and aggregates usage events at a rate of over 1 million events per second. It captures granular data (tokens, inference calls, agent actions) in real-time, providing the source of truth for billing and analytics. In BYOC mode, this metering runs in the customer's own VPC, ensuring full data ownership and privacy.
5. Enterprise Governance & Self-Serve Controls: Enables end-customers (especially in B2B/enterprise contexts) to set their own internal governance rules. Administrators can configure per-user, per-team, or per-agent budget caps and spending limits, which are enforced by Stigg's runtime at call time. This shifts configuration from support tickets to customer self-service.

Problems Solved

1. Pain Point: Revenue Leakage from Usage Overdraft. Traditional metering and billing systems operate on a reconciliation model: usage is collected, aggregated, and invoiced later. This creates a lag where users can consume services (expensive AI inference) beyond their credits or plan limits, leading to unrecoverable costs and complex customer conversations.
2. Pain Point: Engineering Bottlenecks in Monetization. Every change to pricing, feature packaging, or credit rules requires complex, error-prone code changes, slowing down GTM experiments and new product launches. Hardcoded entitlement logic is brittle and difficult to scale across different customer segments (users, teams, agents).
3. Target Audience: VP Engineering & CTOs at scaling AI startups and SaaS companies who need robust, scalable infrastructure to monetize AI features reliably. Product Managers & Growth Leads who own pricing and packaging and need to iterate quickly without constant engineering support. Enterprise DevOps & Security Teams who require deployment in their own cloud (BYOC) for compliance and data sovereignty.
4. Use Cases: Launching a new AI credit or token-based pricing model with real-time spend enforcement. Migrating billing providers (e.g., from Stripe to Zuora) without disrupting the entitlement layer. Providing enterprise customers with self-serve budget controls for their teams and AI agents. Enforcing strict, per-agent usage limits in a multi-agent AI application.

Unique Advantages

1. Differentiation: Unlike standalone metering tools (like Metronome) or billing systems, Stigg is an enforcement runtime first. It acts in the critical path of the AI request to prevent overages, whereas others primarily meter for invoicing. Unlike building in-house, it provides a complete, pre-built financial ledger and entitlement system designed for AI's scale and complexity.
2. Key Innovation: The "BYOC (Bring Your Own Cloud) Runtime." Stigg can be deployed as a fully managed service within the customer's own Virtual Private Cloud (VPC). This unique deployment model combines the manageability of a SaaS product with the security, latency, and data ownership of an on-premise solution, which is critical for enterprise AI applications handling sensitive data.

Frequently Asked Questions (FAQ)

1. How does Stigg prevent AI usage overdraft and control costs? Stigg performs real-time credit checks and entitlement enforcement in the application's request path, before the AI model inference is executed. It deducts credits from a customer's balance at the moment of the request, ensuring zero overdraft and preventing unauthorized or over-budget usage instantly.
2. Can Stigg integrate with my existing billing system like Stripe? Yes, Stigg is designed to augment your existing billing stack. It bi-directionally syncs with providers like Stripe, Zuora, or Chargebee. Stigg becomes the real-time usage and entitlement layer, while your billing provider handles subscriptions, invoicing, and payments. This allows you to change billing providers without re-engineering your monetization logic.
3. What is the difference between Stigg's entitlements and feature flags? While feature flags are binary toggles controlled by development, Stigg's entitlements are dynamic rules tied to customer identity, subscription tier, and real-time credit balances. They govern what features, models, or quotas a user can access based on their commercial relationship, enabling complex monetization logic without code changes.
4. Is my usage data secure with Stigg, especially for enterprise AI applications? Stigg offers a BYOC (Bring Your Own Cloud) deployment model where the entire runtime, including the metering engine, is installed in your own AWS VPC. Your usage data never leaves your infrastructure, meeting strict enterprise security, compliance, and data sovereignty requirements while still using a fully managed service.
5. How does Stigg handle scale for high-volume AI applications? The Stigg runtime is built for AI-era throughput, capable of ingesting over 1 million events per second and making entitlement decisions with p99 latency under 10ms. Its architecture is designed to scale horizontally across millions of entities (customers, users, agents) without performance degradation.