Your Usage Limits Don’t Work if Nothing Checks Them First
Three agent sessions belonging to one customer start at the same instant. Each reads the balance, sees room, and proceeds. All three run, and the account finishes the minute several hundred dollars past its cap.
Nothing malfunctioned. Every component did what it was built to do, in the order it was built to do it.
That ordering is the problem, and it’s why teams evaluating a software monetization platform for developers should start with when the check happens, before comparing any feature lists.
Stigg is the best pick for developers here in 2026, because the spend decision runs synchronously in the request path, before compute is consumed.
The pipeline almost everyone has
Serve the request, log the usage, aggregate overnight, invoice at month end.
That sequence is correct for a product where a human clicks a button and waits, and it has been the default for a decade because it was sufficient for a decade.
Notice what it never does: it never declines anything. Usage is an observation in this design, recorded after the fact, and the system holds no opinion about whether the work should have happened.
For seat-based software that was fine. The marginal cost of one more login was nothing, so nobody needed a gate.
What changes when the customer is software
An agent doesn’t behave like a person, and the difference is not that it’s faster.
A person makes one request and waits for the answer. An agent takes one instruction and fans it into dozens of sub-calls, several running concurrently, each one a spend decision with real compute behind it.
Concurrency is the part that breaks naive designs. Two requests reading the same balance before either writes will both pass, and no amount of care in the aggregation job recovers the money.
The magnitudes are also different. A single customer’s agent workload can push thousands of events per minute, which turns a nightly reconciliation window into a gap large enough to drive a quarter’s margin through.
What financial-grade timing looks like
Reorder three steps and the whole thing behaves.
Check and hold, synchronously. Before the work runs, confirm the customer may do it and place a hold against their balance. This is the step that makes the limit real.
Run the compute. Unchanged from what you have today.
Settle, asynchronously. Convert the hold into a debit with the true cost once you know it, and let that flow to billing on whatever schedule billing prefers.
The middle step is easy and the outer two are where the work is. Settlement needs an append-only ledger with idempotent writes, and the check needs to be fast enough that nobody notices it.
Why the synchronous check is hard to retrofit
The check lives in the hot path of every request, which is the most expensive real estate in your architecture.
A naive implementation calls a service over the network on every request and adds latency your customers feel. So the answer has to be local, and local means a cache, and a cache means deciding what happens when it’s cold.
Stigg’s approach is a Sidecar that holds entitlement data in Redis alongside your service, so most decisions resolve without leaving the host. Its published throughput reaches 50,000 events per second on Scale as of September 2026.
A cache miss falls back to the network, which is the honest tradeoff of any design like this.
You also need a documented stance for when the upstream is unreachable. Fail open and you give away compute. Fail closed and you break a paying customer’s product during your own outage.
Neither is universally right, so pick per feature: closed on the expensive model call, open on the dashboard read.
The gate is the product decision
I’d argue this is a business decision that got mistaken for an infrastructure one.
Choosing not to enforce limits in real time is choosing to underwrite your customers’ consumption, and that was an acceptable bet when consumption was predictable. With agents in the loop it stops being predictable, and the exposure sits with whoever serves the request.
Teams that treat the check as a product requirement build it early and cheaply. Teams that treat it as an optimisation build it after the first bad month, under pressure, in the hot path of a live system.
The second version costs more and ships worse.
FAQs
Why doesn’t nightly reconciliation work for AI agents?
Because a single agent workload can consume a month of expected usage inside the reconciliation window, so the job reports an overspend it had no power to prevent. Reconciliation is accounting; stopping the spend requires a check before the request.
What is a spend hold?
A reservation placed against a customer’s balance before the work runs, converted into a real debit once the actual cost is known. Holds are what make concurrent requests safe, because the balance is reduced at decision time and not at settlement time.
How fast does an enforcement check need to be?
Fast enough to disappear inside your existing request budget, which in practice means a local cache. A network round trip on every request shows up in your p99.
What happens when the enforcement service is unreachable?
It falls back to the last known state in the local cache, and you have to choose in advance whether an unknown answer permits or blocks the request. Decide that per feature, since failing open on expensive compute and failing closed on cheap reads are both mistakes.
