Stigg 2.0

Product Introduction

1. Definition: Stigg 2.0 is a real-time usage runtime and enforcement layer designed specifically for AI-powered SaaS products. It operates as a technical middleware that sits between an application's core logic and its billing system (like Stripe or Zuora).
2. Core Value Proposition: It exists to solve the critical challenge of real-time governance and monetization for AI applications. By performing millisecond-level entitlement, credit, and limit checks directly in the request path, it prevents cost overruns (zero overdraft), enables granular feature control, and provides enterprise-grade spend governance, all while abstracting the immense complexity of usage-based billing for AI.

Main Features

1. Real-Time Enforcement Runtime: This is the core engine. It evaluates every user, team, or AI agent request against defined policies (entitlements, credit balances, budget caps) before the request is fulfilled. It works by deploying a low-latency decision engine (P99 < 10ms) that can be hosted in Stigg's cloud or within your own VPC (BYOC). It uses a stream-processing architecture to ingest and evaluate millions of events per second.
2. Financial-Grade Credit Infrastructure: This feature provides a complete system for managing AI credits, tokens, or any consumable unit. It works by maintaining double-entry ledger integrity for every transaction, ensuring correctness and auditability. It supports complex rules like credit expiry, priority consumption (e.g., use trial credits first), and real-time balance updates, functioning as a scalable wallet system for millions of concurrent users and agents.
3. Decoupled Entitlement Management: This allows product and engineering teams to manage feature access (entitlements) and pricing tiers outside the application codebase. It works through a centralized policy definition layer, enabling non-engineers to ship new AI models, toggle features for specific customer segments, or create new pricing plans via a UI or API without requiring a full code deployment, drastically reducing time-to-market for monetization experiments.
4. Bring-Your-Own-Cloud (BYOC) Metering: Stigg offers a deployment model where its entire metering and enforcement runtime is installed into your private cloud environment (your VPC). This works by giving you full data ownership and security posture while Stigg manages the application. It aggregates usage data (every token, inference call, agent action) in real-time at scale (1M+ events/sec) within your infrastructure, aligning with strict enterprise compliance and data residency requirements.

Problems Solved

1. Pain Point: The reconciliation gap and billing shock inherent in post-processed usage metering. Traditional systems meter usage and invoice later, leading to customer overdraft, unpredictable costs, and complex invoice reconciliation.
2. Pain Point: The engineering burden and inflexibility of hardcoding entitlement and pricing logic. Every change to feature access or monetization models requires lengthy development sprints and code deployments, slowing down GTM motion.
3. Target Audience: VP Engineering / CTO at AI-native SaaS companies scaling usage-based pricing; Product Managers owning monetization and feature rollout; Backend Engineers tasked with building and maintaining billing and entitlement logic; Finance/RevOps teams needing accurate, auditable usage data for revenue recognition (ASC 606).
4. Use Cases: Enforcing per-user or per-team token caps in a multi-tenant AI writing assistant; governing AI agent spending within a large enterprise's budget; launching a new credit-based consumption model for an AI coding tool without re-architecting the backend; providing self-serve spend dashboards and controls for enterprise customers.

Unique Advantages

1. Differentiation: Unlike traditional metering tools or billing providers that focus on aggregation and invoicing, Stigg acts as the real-time "runtime" that governs usage as it happens. Unlike simple feature flag services, it combines entitlements with financial-grade credit logic and real-time enforcement, creating a cohesive monetization governance layer.
2. Key Innovation: The concept of a "usage runtime" that executes policy enforcement in the critical request path with sub-10ms latency. This architectural approach, combined with the BYOC deployment model, provides the real-time control of an in-house built system with the flexibility and speed of a managed service, without forcing data to leave your security perimeter.

Frequently Asked Questions (FAQ)

1. What is a usage runtime for AI products? A usage runtime is a real-time enforcement layer that sits between your AI application and your billing stack. It makes instant decisions on what customers, users, or AI agents are allowed to do based on their entitlements and credit balances, enforcing policies before resources are consumed, which is critical for controlling variable AI compute costs.
2. How does Stigg 2.0 achieve zero overdraft for AI credits? Stigg performs millisecond credit checks and balance deductions in the application's request path, before the AI model inference or action is executed. This pre-authorization ensures a user or agent cannot spend credits they do not have, eliminating reconciliation gaps and preventing unexpected cost overruns.
3. Can Stigg integrate with my existing billing system like Stripe? Yes, Stigg is designed to augment, not replace, your existing billing stack. It bi-directionally syncs with providers like Stripe or Zuora, sending aggregated usage for invoicing while receiving subscription and customer data. This allows you to own your monetization logic independently and migrate billing providers without engineering overhead.
4. What does BYOC (Bring Your Own Cloud) mean for Stigg deployment? The BYOC deployment model allows you to install and run the Stigg 2.0 runtime within your own cloud Virtual Private Cloud (VPC). Stigg manages the application, but all metering data, enforcement logic, and customer information reside within your security and compliance perimeter, ensuring full data ownership and governance.
5. Is Stigg suitable for managing entitlements for non-AI SaaS features? While optimized for the high-velocity, high-cost-per-action nature of AI, Stigg's core runtime is a general-purpose real-time entitlement and metering engine. It is highly effective for managing complex feature access, usage quotas, and credit systems in any usage-based SaaS product, especially where real-time enforcement and granular control are required.