June 2026 Summaries

Stigg, a company addressing the challenges of pricing and entitlements in software, has launched Stigg 2.0, a major update designed to meet the evolving needs of AI companies. The update focuses on real-time enforcement, governance, and credit management to support AI products that require instant decision-making capabilities, especially in scenarios where traditional billing methods fall short. As AI products evolve rapidly, Stigg 2.0 enables businesses to implement programmable and externalized entitlements, allowing for quick adaptation to pricing changes without extensive engineering efforts. This approach is crucial for managing enterprise-level governance and compliance requirements, providing auditability, and ensuring accurate revenue recognition. Stigg 2.0 introduces a modular architecture that can be deployed within a company's own infrastructure, offering real-time decision-making and usage control, which are vital for preventing fraud, managing budgets, and ensuring customer satisfaction. The platform aims to make complex billing and entitlement systems accessible to all AI companies, regardless of their scale, by providing tools that are API-first and suitable for integration into existing workflows.

AI billing infrastructure is a complex system designed to connect AI product usage with revenue generation, incorporating both financial and enforcement layers to handle dynamic and unpredictable usage patterns. The financial layer comprises event ingestion, metering, rating, invoicing, and payment collection, focusing on tracking and billing settled data. However, AI products require an additional enforcement layer to provide real-time control over entitlements, budgets, and usage governance, ensuring requests are allowed to run only within predefined limits. This separation is crucial due to the unique challenges AI presents, such as the disparate costs associated with seemingly similar requests and the need for detailed attribution to understand usage patterns. Enterprises demand governance features like team-level budgets and audit trails to manage AI adoption effectively, which necessitates a well-architected billing infrastructure capable of both recording and controlling consumption. The runtime layer, exemplified by tools like Stigg, bridges the gap between application and billing systems, offering real-time entitlement checks and spend governance to prevent costly overruns and support enterprise requirements.

AI token cost management is a multifaceted challenge that involves tracking consumption, predicting future costs, and enforcing limits to control expenditure effectively. While tracking token usage for billing purposes is relatively straightforward, enforcement in real-time is complex, requiring a robust infrastructure that can intervene in the request path before model calls incur costs. AI token costs arise from per-unit charges for processed or generated text, with output tokens typically costing significantly more due to the computational demands of sequential text generation. As AI products scale, cost control becomes critical, especially in scenarios with diverse models and workflows, where predicting and containing expenses becomes increasingly difficult. Real-time enforcement is necessary to prevent unexpected costs, especially in agent workflows that can independently trigger multiple model calls and exceed predefined budgets. Enterprise customers often require governance structures that allocate budgets across various teams and departments, necessitating sophisticated controls and reporting capabilities. Effective cost management at production scale demands infrastructure capable of immediate entitlement checks, context-aware usage attribution, and concurrent credit management, ensuring that AI deployments remain within budgeted limits while supporting organizational objectives.

Webflow, a prominent visual web design platform, faced significant challenges due to outdated infrastructure that hindered its pricing initiatives and growth potential. Utkarsh Sengar, Webflow's VP of Engineering, spearheaded efforts to overhaul the company's pricing structure, a task complicated by tech debt and system limitations. Recognizing the inefficiencies and resource demands of building a solution in-house, Webflow partnered with Stigg, whose headless pricing and packaging solution aligned perfectly with the company's needs. This collaboration has enabled Webflow to save substantial development time, enhance engineering and product velocity, and support agile pricing and packaging strategies, thereby empowering the organization to focus on strategic growth and market expansion.

Overage fees in AI products present unique challenges due to unpredictable usage patterns and high consumption rates, necessitating real-time enforcement to manage costs effectively. These fees, imposed when usage exceeds a predefined allowance, consist of an included allowance, a threshold where overages begin, and an overage rate for additional consumption. Systems that handle overages must track usage in real time, detect when limits are crossed, and apply fees promptly to avoid billing discrepancies. Different overage pricing models, such as per-unit charges or tiered rates, impact how systems manage excess usage, requiring robust infrastructure to maintain consistency across billing cycles. Real-time visibility, usage alerts, and enforceable caps are critical to preventing unexpected charges and maintaining customer trust. As usage scales, ensuring enforcement aligns with entitlement systems becomes essential to avoid inconsistencies and interruptions, particularly in AI applications where a single request can significantly impact resource consumption. Stigg's approach to real-time usage tracking and enforcement aims to keep the system's state consistent, thereby preventing overage issues before they escalate.

Value-based pricing, especially for AI-native products, hinges on aligning the cost with the value delivered to the customer, measured through units like tokens processed or actions completed, rather than traditional production costs. While traditional SaaS platforms utilize tiered subscriptions with occasional access checks, AI products require real-time infrastructure to manage and enforce usage limits continuously, ensuring credit balances are accurate and usage doesn't exceed set limits, which are crucial to avoid unexpected costs. This pricing model necessitates sophisticated infrastructure capable of running entitlement checks in the request path, maintaining credit wallets with multiple balance types, and supporting multi-tenant governance for enterprise clients. Failures in this system often arise from infrastructure inadequacies, rather than flaws in the pricing strategy itself, as teams might build accurate metering systems but lack real-time enforcement capabilities, leading to potential margin loss. Solutions like Stigg offer specialized enforcement layers that integrate seamlessly with existing billing systems, ensuring real-time entitlement checks and supporting complex governance structures essential for AI products at enterprise scale.

Dynamic pricing for AI-native SaaS platforms involves adjusting subscription tiers and pricing models to account for usage variability, such as consumption of LLM tokens or compute resources. The enforcement of pricing decisions must occur synchronously within the request path to prevent overages and ensure real-time access control, distinguishing it from traditional metering systems that track usage for billing purposes. Effective dynamic pricing models require separation of pricing logic from billing systems, focus on real cost units, and design for organizational complexity with multi-currency credit ledgers and self-serve governance interfaces. Companies must avoid common pitfalls like conflating metering with enforcement, relying on employee-count thresholds for governance, and bolting governance onto billing systems post-implementation. As AI products scale, engineering teams must anticipate challenges related to concurrency, cache coherence, and throughput under burst conditions, often necessitating a robust enforcement layer like Stigg to manage entitlements and credits effectively without disrupting existing billing infrastructures.

AI product pricing models often encounter failures when transitioning from test environments to production, primarily due to issues such as concurrent usage and shared state complexities. To address this, a structured approach to pricing simulation is essential, involving the separation of pricing logic from application code, defining pricing models in configuration, replaying actual usage data, and testing multiple models in parallel to evaluate their behavior under real conditions. This process helps identify potential failure points, such as inconsistent entitlement resolutions, credit depletion order, tier boundary behavior, and provisioning latency. Additionally, it emphasizes the importance of maintaining a clear boundary between configuration and code to ensure consistency and reliability in pricing updates. The guide highlights the necessity of validating pricing models under realistic concurrency levels to expose race conditions and state inconsistencies that might otherwise go unnoticed. Moreover, it stresses the importance of a robust feedback loop that ties usage events to entitlement decisions, capturing relevant signals to explain enforcement behavior. While pricing simulation significantly reduces risk, it cannot entirely replace real-world testing, as certain edge cases only emerge under production conditions.

The text discusses the critical billing system requirements for AI SaaS products that go beyond traditional billing platforms, which are typically designed for invoicing, payment processing, and compliance. These requirements include real-time enforcement in the request path, credit and wallet management, event-level usage metering, and multi-tenant organizational hierarchy support. Such requirements are essential for handling the rapid cost generation of AI workloads and meeting enterprise demands for spend controls, audit trails, and organizational visibility. The text emphasizes the importance of a dedicated layer that operates above standard billing systems to manage entitlements, credits, and usage limits in real-time, preventing overages and supporting enterprise-level governance. The document also highlights Stigg as a solution that provides this functionality, allowing teams to focus on product development rather than billing logic.

Credit-based pricing has become essential in modern software infrastructure, particularly within the AI and API sectors, necessitating precise management akin to financial transactions. Stigg has revamped its credit engine to enhance this management by introducing features such as granular event dimensions, isolated credit pools, and advanced time-series filtering. These updates allow users to break down credit usage by specific dimensions like user ID, LLM model type, and geographical region, providing detailed insights into consumption patterns. Additionally, the introduction of Resource-Specific Credit Pools enables companies to allocate credits according to their product structures, ensuring that different resources operate independently without affecting each other. The implementation of flexible Credit Usage Date Filters allows for custom analysis of credit consumption over time, supporting more accurate planning and troubleshooting. These improvements aim to provide real-time financial control and visibility, accommodating complex monetization models and safeguarding against unexpected overdrafts in an era dominated by AI and automated systems.

Standard billing software architecture, designed for predictable, post-usage settlement, struggles to manage the dynamic and variable compute costs introduced by AI products, necessitating an additional enforcement layer. This layer acts in real-time, making access decisions before compute is consumed, managing credits, and enforcing budget governance across organizational hierarchies, which standard billing systems like Stripe and Zuora cannot manage alone. The guide highlights the inadequacies of traditional billing systems in handling AI-specific challenges such as variable marginal costs per request, emphasizing the need for a runtime enforcement layer that operates upstream of billing to prevent technical debt and ensure efficient access control. This layer includes components like entitlement caches and credit ledgers that operate with low latency to enforce real-time consumption limits and governance, integrating seamlessly with existing financial layers to ensure both layers handle distinct responsibilities without data model overlap. Mistakes like wiring usage limits into billing engines or coupling billing with entitlements lead to technical challenges, underscoring the importance of correctly layering architecture to support AI products.

Software licensing models, which define how customers access and consume products, pose significant engineering challenges as they require systems to evaluate entitlements, enforce limits, and track usage in real-time. These models include hybrid, credit-based, usage-based, feature-based, tiered, and per-seat licensing, each with specific technical requirements that impact the metering, entitlement, and enforcement layers of a system. Hybrid models, common in AI products, combine subscription access with usage or credit consumption, necessitating aligned entitlements and metering. Credit-based models involve prepaid balances with real-time consumption tracking, while usage-based models require metering of unit consumption like API calls or compute time. Feature-based licensing offers access to specific features based on subscription plans, demanding a centralized feature-gating system. Tiered licensing involves predefined plans with bundled features and limits, and per-seat models tie access to user identities, challenging systems to manage identity mapping and seat assignments. Engineering problems arise when licensing is treated as a mere pricing decision rather than an infrastructure issue, often leading to inconsistent enforcement, delayed usage data, and complex maintenance requirements. Stigg, a runtime enforcement layer, provides solutions with real-time entitlement checks and usage control, crucial for AI-powered systems where each request directly impacts costs, transforming licensing into a continuous cost control system.

The launch of the MCP server aimed to alleviate the common developer issue of context switching by integrating Stigg into the native developer environment via the CLI, reducing the need to switch between browsers and IDEs. However, while the server equips coding agents with tools to interact with Stigg, the AI models lack the domain expertise needed for complex product configurations. To address this, the introduction of Agent Skills for Claude Code enhances AI coding agents with the necessary structural domain knowledge, allowing seamless integration of Stigg into applications. These skills operate at the prompt layer, providing Stigg-specific insights, pricing best practices, and safety measures, ensuring agents follow correct implementation patterns and terminology. As a result, developers receive more accurate, near-production-ready output from initial prompts without the need for extensive trial and error. The combination of MCP, Stigg CLI, and Agent Skills creates a streamlined, intuitive development experience for building usage-aware products.

The text discusses the challenges of usage-based billing systems, emphasizing the need for a separate usage runtime to handle real-time access decisions and entitlements, which most billing tools do not address. It examines nine Amberflo alternatives, each with different strengths and limitations, such as Togai's ability to transform raw data into billable metrics, Metronome's precise metering for engineering-led teams, and Stripe's strong API for payments. The document highlights the problem of enforcing usage limits, which often results in scattered logic across services, and suggests using a tool like Stigg to fill this gap by providing real-time credit governance and access control. The evaluation concludes that while billing infrastructure can reliably track usage and generate invoices, the missing piece is often the synchronous enforcement of limits, which is crucial for systems handling AI-scale workloads.

AI pricing in 2026 is shaped by six models—hybrid tiers, usage-based, credit pools, outcome-based, seat-based with add-ons, and freemium—each with distinct infrastructure needs for real-time metering and enforcement to manage consumption effectively and avoid unexpected costs. As AI workloads can rapidly inflate costs, systems like Stigg are crucial for monitoring usage in real time, ensuring entitlements are enforced, and providing visibility into who is consuming resources. Many companies combine two or three pricing models to balance predictable revenue with variable consumption, but without proper governance, costs can escalate unexpectedly, often due to the delay between usage and billing. Each model demands specific infrastructure requirements, such as real-time event metering for usage-based pricing or credit tracking for credit pools, to maintain control over AI consumption and prevent financial discrepancies. Building or buying infrastructure like Stigg depends on how quickly a company needs to address real-world challenges, with the decision often influenced by the complexity and scale of the AI system's usage.

Multi-tenant and single-tenant architectures represent two distinct approaches to managing customer data and resources, each with its own advantages and tradeoffs. Single-tenant architecture dedicates a unique environment for each customer, offering strong isolation and customization but at a higher cost and operational overhead due to the need to manage individual environments. In contrast, multi-tenant systems use shared infrastructure to serve multiple customers, which enhances resource efficiency and scalability, making it ideal for handling variable or bursty workloads, such as those seen in AI-native products. However, multi-tenant systems require robust real-time enforcement to ensure consistent resource allocation and prevent issues like resource imbalance and usage drift. The decision between these architectures depends on factors like the need for isolation versus cost efficiency and how well each system performs under real-world conditions. Many production systems adopt a hybrid model, combining both approaches to balance the advantages of efficient scaling with the isolation needed for certain enterprise requirements. Regardless of the architecture chosen, a consistent enforcement layer is crucial to maintain control over usage and pricing, ensuring that limits are upheld and that tenant-specific entitlements are accurately reflected across services.

Token-based pricing systems are designed to meter and enforce usage limits in real-time by allocating tokens for each request, which are then decremented from a balance. However, challenges arise when these systems scale, particularly with issues such as concurrency, cache drift, and incorrect credit depletion, leading to billing disputes and negative balances. The architecture of an effective token-based pricing system requires a metering layer to track usage, an entitlement layer to define access rules, a credit system to manage balances, and an enforcement layer to regulate requests before execution. These components must operate synchronously to ensure that usage is controlled accurately and efficiently, preventing over-consumption and ensuring auditability. Implementation complexities include the need for atomic credit enforcement, centralized pricing logic separate from application code, and real-time entitlement checks, which become increasingly difficult as concurrency and service interactions grow. Solutions to these challenges often involve moving enforcement into the request path and ensuring that pricing logic is centralized and independent of application deployment cycles, as seen in systems like Stigg, which integrate a control layer to maintain consistency and reliability at scale.

Entitlement management solutions are designed to manage and enforce what customers can access and how much they can utilize within a product, separating this logic from the application and placing it between the product and billing systems. Unlike billing systems that calculate charges after usage, entitlement solutions regulate access in real-time, ensuring that access decisions are made before usage occurs. These systems differ from Role-Based Access Control (RBAC) by focusing on plan-based access rather than user roles, handling complexities such as multiple plans, feature access, credits, trials, and promotions, which must all be resolved into a single decision per request. As products grow, the complexity of managing entitlements increases, making it challenging to maintain consistency and efficiency if handled in-house, leading teams to consider third-party solutions like Stigg for their ability to manage entitlements with low-latency checks, configuration-driven pricing, and an append-only ledger for credits. These solutions allow for more flexible and efficient management of pricing and access logic, reducing the engineering burden and enabling seamless updates without code changes, ultimately enhancing scalability and reliability of the system.

Amberflo offers a scalable pricing model based on event ingestion volume and invoiced amounts, tailored to AI product teams and enterprises with different needs. The Essential plan is designed for teams starting with AI products, covering real-time usage metering, flexible pricing models, and customer-level cost tracking, while the Custom plan caters to enterprises with expanded scale and support requirements, including enterprise-grade SLAs and advanced security options. Amberflo's system relies on events as the core unit for billing and cost tracking, supporting a wide range of AI-native pricing models without requiring changes to application code. While Amberflo excels in tying usage, cost, and billing together, it does not handle runtime entitlements, which is where Stigg, an overlay control plane, fits in by managing runtime access and usage control. Amberflo is ideal for businesses where cost and usage visibility are crucial, particularly for AI workloads, but may not be suitable for those relying on flat-rate subscriptions with straightforward billing needs.

Miro faced significant challenges with its monetization logic as the company grew, with disparate plan checks scattered across the codebase and entitlements embedded in application code, leading to difficulties in making changes and integrating new features like their AI-powered Innovation Workspace. The company was faced with a choice between continuing to build and maintain their entitlements infrastructure internally or adopting a specialized solution, ultimately choosing Stigg for its comprehensive capabilities, which not only met existing requirements but also anticipated future needs. Implementing Stigg's solution streamlined Miro's operations, consolidating all monetization logic into a single layer that integrated seamlessly with existing systems, enabling rapid deployment of new plans and features, and reducing engineering hours significantly. The transition allowed Miro to shift from complex, high-friction changes to routine configuration adjustments, enhancing their ability to adapt and scale efficiently. The story illustrates the benefits of leveraging specialized solutions to overcome the hidden costs of organic growth and the complexities introduced by advanced features like AI, ultimately allowing Miro to focus on innovation and scalability.