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Pricing looks simple until you try to change it. What starts as a few plans on a pricing page quickly turns into a mess of feature flags, usage tracking, seat management, and billing logic. Every change touches multiple systems, and small updates can take weeks to ship.
This guide breaks down seven embedded software monetization models from an engineering perspective: what systems need to support, where complexity grows, and what breaks when requirements change.
What is embedded software monetization?
Embedded software monetization means the enforcement logic lives inside your product, not just in your billing system. Billing handles invoices, taxes, and payment processing after the purchase decision. Embedded monetization governs what customers can access and how much they can consume, in real time.
An entitlements system handles this layer. Entitlements are the rules that define who gets what, for example:
- A Free user gets 5 exports per month
- A Pro user gets unlimited
- An Enterprise user gets a custom cap set by the sales team
The entitlements system enforces these rules inside your app on every request. A product catalog is the source of truth for all these rules, mapping plans to features and storing the limits for each. When your catalog changes, your entitlements update without a code deployment if your architecture supports it.
The 7 embedded software monetization models
These 7 models shape how SaaS and AI companies charge for software: tiered subscription, usage-based pricing, credit-based pricing, feature gating, hybrid pricing, per-seat pricing, and modular and add-on pricing.
Each one creates a different set of technical requirements for your entitlements layer, metering system, and product catalog.
1. Tiered subscription
Tiered subscription is where most SaaS products start. Each plan maps to a fixed set of features and limits, and while Free, Pro, and Enterprise are the classic tiers, most products end up with more as the business grows.
What the system needs to do:
- Store a plan-to-feature mapping in a product catalog
- Check entitlements on every feature access request
- Provision new feature access instantly when a customer upgrades
- Handle grandfathered plans when you change pricing
Where complexity grows
Tiered subscription starts simple, but complexity accumulates fast. After 2 years, you are managing 8 tiers, 30 features, 3 legacy plans you cannot retire, and 2 acquired products on different pricing structures. The system you built in a sprint now needs a dedicated engineer to maintain.
Provisioning is another common gap. Many teams build the entitlement check but leave provisioning manual, leaving Enterprise customers waiting 24 hours for access after an upgrade.
What breaks when requirements change
Adding a new plan forces you to update every entitlement check that hard-codes plan names. When access logic is scattered across services, a single pricing change touches dozens of files. A centralized catalog with a single enforcement layer fixes this.
This is the difference a centralized catalog makes in practice. For instance, Webflow (since partnering with Stigg) has been able to update pricing, implement localization, and explore credits without touching billing infrastructure or requiring code deployments for each change. The entitlements layer handles what the product enforces separately from how billing is configured.
2. Usage-based pricing
Usage-based pricing is common for AI companies. Customers pay based on consumption, with metrics like API calls, tokens, model runs, GB processed, or seats. Price scales with the value the customer gets.
OpenAI and Anthropic both use this model, charging per token consumed across different models at different rates.
What the system needs to do:
- Capture usage events in real time and attribute them to a customer
- Aggregate consumption against a plan allowance
- Enforce hard limits (block at cap) or soft limits (allow overage at a different rate)
- Pass aggregated usage to billing for invoice generation
Where complexity grows
Metering at scale is harder than it looks. At low volume, a simple counter works, but at high volume, you need an event stream that handles bursts without dropping events. A missed event is a billing error, and so is a duplicate one.
Latency compounds the problem. Every feature access request may need to check the current usage balance, and a 200ms round-trip to a remote database on every API call is something users will notice. The enforcement layer needs a local cache with low-latency reads.
What breaks when requirements change
Usage-based systems usually start counting 1 metric. Then the product grows. You add a second metric, then a third. Each new metric demands its own metering pipeline, aggregation logic, and enforcement rules. Building these as one-off systems compounds the operational burden fast.
3. Hybrid: Subscription + usage
Hybrid pricing is now the dominant model in AI and modern SaaS. A base subscription covers a usage allowance, and customers who exceed it pay an overage rate.
Cursor uses this model with a base plan that includes a monthly allowance of fast model requests, and pay-as-you-go pricing once that allowance is exceeded. The base provides predictable revenue, while overages capture additional usage from heavier users.
A common structure looks like: $99/month base fee, includes 10,000 API calls, $0.002 per call after that. The base gives you predictable revenue, and the overage captures upside from heavy users.
What the system needs to do:
- Track both subscription entitlements and real-time consumption in one system
- Switch enforcement behavior at the allowance boundary: included usage vs. overage usage
- Apply different rates per customer segment, often negotiated at the enterprise level
- Surface current usage to customers in-app so they can self-manage
Where complexity grows
Hybrid pricing is where most homegrown entitlement systems fail. Teams built them for subscriptions and bolted on usage tracking later as a separate system.
Because the two systems don't share a model of the customer's entitlement state, edge cases multiply: mid-month upgrades, prorated allowances, tiered overage rates. Each exception pulls engineers into billing support instead of product work.
Latency becomes a problem, too. Enforcement needs the customer’s current usage on every request to decide whether they are within the allowance or in overage.
Some teams solve this by evaluating entitlements locally through a sidecar (like Stigg’s) or cache layer instead of calling a remote database each time. The enforcement logic runs close to the application and syncs state in the background, keeping latency low without sacrificing accuracy.
What breaks when requirements change
Changing overage rates or allowance amounts means touching multiple systems that were never designed to change together. The fix is a unified product catalog where subscription entitlements and usage allowances live in the same model.
When they do, updating an overage rate or allowance amount becomes a catalog change with no application code to touch and no cross-team deployment to coordinate. Stigg's infrastructure is built around this model specifically because hybrid pricing is where split-system architectures fail most visibly.
4. Feature gating
Plans and add-ons gate features, giving different customer segments access to different capabilities. Advanced analytics, SSO, higher rate limits, and premium AI models are common examples.
The key engineering distinction here is that gating is not hiding. Hiding a feature removes the upsell opportunity. Gating shows the feature, blocks access, and presents an upgrade prompt. That moment is when conversion is most likely.
What the system needs to do:
- Evaluate entitlements on every gate check and return a structured response: allowed, blocked, or soft-blocked with upgrade context
- Serve upgrade prompts with the right plan information at the right moment
- Update gates in real time when a customer upgrades, with no session restart required
- Support pricing experiments on gate behavior without code deployments
Where complexity grows
Gate logic tends to spread across the codebase over time. One team adds a check in the API, another in the frontend, and a third in a background job.
When pricing changes, every check needs updating. Inconsistent logic gives users contradictory behavior: blocked in the UI but allowed via the API.
What breaks when requirements change
Hard-coded plan checks are the main problem: if (user.plan === 'pro') scattered across 50 files makes any pricing experiment expensive. A centralized entitlements layer lets you change the catalog without touching application code.
5. Seat-based pricing
Seat-based pricing is common in SaaS tools, where companies pay per user with minimum seat commitments and tier-based features layered on top. For AI tools, seat pricing sometimes blends with usage pricing when individual consumption varies widely across users.
What the system needs to do:
- Track the seat count against the purchased quantity
- Provision new users instantly when an admin adds them
- Revoke access immediately when an admin removes a user, protecting both security and billing accuracy
- Sync seat counts with CRM and CPQ systems for enterprise deals
- Handle seat overages and enforce hard limits or notify admins
Where complexity grows
Manual provisioning breaks quickly at enterprise scale. When adding a user requires a support ticket or a nightly batch job, customers notice. What worked for early customers becomes a bottleneck once larger organizations start onboarding teams.
Seat changes also become harder to manage in real time. Large teams are frequently adding and removing users across departments. Without automation, seat counts drift from what’s actually provisioned, leading to billing errors, access issues, and manual reconciliation across billing, CRM, and provisioning systems.
What breaks when requirements change
Adding a new role type or seat category with different pricing touches every system that models user access. If seat logic lives across multiple services, these changes require coordinated deployments across teams.
6. Add-ons and modular pricing
Not every customer needs a full plan upgrade. Add-ons let them buy specific features on top of their base plan, whether that is extra usage, premium model access, or a compliance package. The result is more expansion revenue without restructuring anything.
What the system needs to do:
- Support a product catalog structure of: Products → Plans → Add-ons → Features
- Evaluate entitlements as the union of a customer's base plan plus all active add-ons
- Handle add-on activation and deactivation in real time
- Prevent invalid combinations and enforce dependencies between add-ons
Where complexity grows
Five add-ons create 32 possible combinations, and ten create over 1,000. Each combination needs to resolve to a coherent set of entitlements, and building add-on logic as if-else chains makes it unmaintainable fast.
What breaks when requirements change
Adding a new add-on should require only a catalog update, but if it also requires changes to entitlement check logic, your architecture is coupling the catalog to the enforcement code.
Those should be separate concerns, and keeping them separate is what makes the system flexible enough to change without coordinated deployments.
The catalog structure that handles this cleanly is: Products → Plans → Add-ons → Features. With that hierarchy in place, entitlements resolve as the union of a customer's base plan and active add-ons, and adding a new add-on requires only a catalog update with no changes to enforcement logic. Stigg's catalog is built around this hierarchy for exactly that reason.
7. Credit-based systems
Credit-based pricing is popular in AI products because it abstracts complex compute costs into a single unit that customers can reason about. Customers buy credits and spend them on actions: an image generation costs 5 credits, and an LLM query costs 50 credits.
It also lets you price multiple models under one system without exposing the underlying cost structure.
What the system needs to do:
- Maintain a credit balance per customer with ledger-based accounting
- Deduct credits atomically on each action to prevent race conditions
- Support preloaded balances, auto-recharge, and manual top-ups
- Provide an audit trail for revenue recognition and customer visibility
- Allocate budgets across teams in enterprise accounts
Where complexity grows
Credits look simple at first: just a balance column in a database table. But once you're in production, you need atomic debits for concurrency, a ledger for finance reconciliation, self-serve top-ups, and team-level budgets. Teams that ship a basic counter in a sprint usually spend the next quarter filling in everything they missed.
What breaks when requirements change
If credits live in a single balance column, adding multi-currency support or team-level allocation means a schema migration and a lot of refactoring. A ledger model, where every transaction is its own row, makes those changes much cheaper down the road.
A ledger model also gives finance a clean audit trail without a separate reporting system. This is why purpose-built credit systems (like Stigg’s) default to a ledger structure rather than a balance column. Stigg’s credit system uses a ledger structure with built-in support for preloaded balances, auto-recharge, and team-level budget allocation.
How monetization models interact with your stack
Two distinct systems power your monetization model, and confusing them is where most teams run into trouble.
- Billing handles invoices, payment processing, tax, and financial compliance.
- Your entitlements layer handles feature access, usage tracking, credit balances, provisioning, and in-app monetization experiences.
A billing platform is not designed to enforce feature gates at millisecond latency, and an entitlements system is not designed to generate invoices.
The teams that run into the most trouble are the ones that try to use billing configuration to drive product behavior. Stripe plans are not a product catalog. When you change pricing, you should update a catalog, not redeploy application code or reconfigure a billing provider.
When your entitlements system becomes the bottleneck
Picking the right monetization model is only half the problem. The other half is building infrastructure that can enforce it today and adapt when requirements change, which is harder than most teams expect.
Most start with an in-house entitlements system, and it handles the first few models well. But as products grow and pricing gets more complex, the system that took a week to build starts requiring a dedicated engineer to maintain, and every pricing change ends up going through a sprint. A dedicated entitlements layer can help separate concerns in the stack:
- Sits between the product and billing system
- Manages the product catalog, feature access, metering, and credits
- Keeps billing focused on invoices, subscriptions, and payments
- Allows pricing and packaging updates through configuration instead of code
If your team is at that inflection point, you should explore how platforms like Stigg approach the entitlements layer.
FAQs
1. What is the difference between feature gating and feature hiding?
The main difference between feature gating and feature hiding is that gating shows the feature, blocks access, and presents an upgrade prompt, while hiding removes the feature entirely. Gating preserves the upsell opportunity; hiding eliminates it.
2. What is the most common embedded software monetization model for AI companies?
Hybrid pricing is the most common monetization model for AI companies. It combines a base subscription that covers a usage allowance with an overage rate for customers who exceed it, giving the business predictable revenue while capturing upside from heavy users.
3. Why do homegrown entitlement systems fail at scale?
Homegrown entitlement systems fail at scale because they are typically built for one monetization model and patched to support others over time. As pricing complexity grows, subscription entitlements and usage tracking end up in separate systems that don't share a model of the customer's state, and every pricing change requires coordinated engineering effort across multiple services.
4. What is the difference between a billing system and an entitlements layer?
The main difference between a billing system and an entitlements layer is what each one controls. Billing handles invoices, payment processing, tax, and financial compliance after the purchase decision. An entitlements layer handles feature access, usage enforcement, credit balances, and provisioning inside the product in real time. Using billing configuration to drive product behavior is where most teams run into trouble.
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