How Stripe Uses engineering

Stripe’s next generation of reporting APIs are focused on enterprise scalability, and this post shows you how to use them in your financial reporting infrastructure.

Harbor is the internal tool Stripe teams use to prototype with an AI agent. This post covers how it compiles prototypes in the browser, how the agent inspects its own work, and how comments stay anchored when the page is rewritten.

AI was involved at every stage of building Stripe's extensibility SDK, but the story isn't that it wrote code faster. Here's how prototypes, agent-drafted specs, and persistent guidance got ideas into reviewable shape sooner.

While a smart fridge can detect when a household is running out of milk, the more interesting engineering problem begins after that.

Stripe's Knowledge AI Platform is our versatile AI agent platform built to handle diverse non-coding knowledge work, from quick queries to complex, multi-day projects. By connecting employees to over 1,000 internal tools and skills, it enables secure, enterprise-scale productivity across the organization.

Discover how we modeled our MongoDB infrastructure as a traversable graph and then use pathfinding algorithms to dynamically compute and execute recovery plans. This automated approach reduced pager volume by 30% (~200 pages/year), eliminated 12 days of unhealthy shard states annually, and supports new shard layouts with zero manual effort.

Learn how to implement fund segregation in Stripe Connect using allocated balances to ring-fence seller funds from your platform's operational balance - covering payment creation, transfers, refunds, disputes, and preparing for upcoming PSD3 regulatory requirements.

Some microservices are difficult to test because their behavior depends on a long tail of inputs that are hard to model by hand. This post focuses on how to build a repeatable Apache Spark replay harness for testing without creating a second implementation of the service.

Some microservices are difficult to test because their behavior depends on a long tail of inputs that are hard to model by hand. For that class of problem, Apache Spark's massive parallelism and linear scalability can be leveraged to build a regression test harness. That makes it possible to compare old and new implementations, model upcoming rule changes, and quantify impact before a change reaches production.

For product-led businesses, conversion is paramount, so an important consideration is deciding whether to implement synchronous or asynchronous tax ID validation in a checkout flow. One is friendlier for conversion, but the other can keep your business compliant.

This post is a follow-up to last week’s “From init to deploy: building with agents and Stripe Projects” and explores the learnings gained along the way.

In this post we’ll dive into how we built a virtual cashless payment method that works for prepaid and Stripe-issued credits, and cleanly integrates with our accounting, compliance, and other frameworks.

Stripe's Selective Test Execution system employs some clever tricks to allow us to continue scaling our team and our codebase while only running around 5% of our tests on average. Find out how it works!