As a SaaS product matures, customers stop wanting to poll your API for changes and start wanting you to notify them the moment something happens. A payment succeeds, a document is signed, a support ticket is resolved, and their systems need to react immediately. Webhooks, the mechanism by which your platform makes an outbound HTTP request to a customer-supplied URL when an event occurs, are how this real-time integration usually works. They look deceptively simple: something happens, you POST a payload to a URL. In practice, building webhooks that customers can actually depend on is one of the more subtle engineering challenges in a platform, because you are making network calls to systems you do not control and cannot trust to be available.
Why webhooks are harder than they look
The core difficulty is that the receiving end is unreliable by nature. The customer’s endpoint might be down for maintenance, might be slow, might return an error, or might silently accept the request and then crash before processing it. Your platform, meanwhile, may generate thousands of events per second across all customers. A naive implementation that fires a webhook synchronously inside the code path that created the event will eventually take down that code path when a customer’s slow endpoint causes requests to pile up. The first principle of reliable webhooks is therefore to decouple event generation from delivery, pushing events onto a queue and delivering them from a separate worker system that can fail and retry without affecting your core product.
At-least-once delivery and the idempotency contract
Once you introduce queues and retries, you must confront a fundamental trade-off. Guaranteeing that a webhook is delivered exactly once is effectively impossible across an unreliable network, because you can never be certain whether a request that timed out was actually processed. The practical choice almost every mature platform makes is at-least-once delivery: you would rather send a duplicate than drop an event entirely. This shifts a responsibility onto the receiver, and a good platform is explicit about it. Each webhook should carry a unique event identifier, and you should document clearly that consumers must be idempotent, meaning that processing the same event twice has the same effect as processing it once. A customer building on top of your webhooks should key their processing on that event ID so that a duplicate delivery is safely ignored.
Retries, backoff, and giving up gracefully
When a delivery fails, you retry, but how you retry matters. Immediate, aggressive retries against a struggling endpoint make the problem worse by hammering a system that is already down. The standard approach is exponential backoff with jitter: wait a few seconds, then longer, then longer still, adding a small random offset so that a fleet of failed deliveries does not retry in perfect synchronization and create a thundering herd. You also need a policy for giving up. After some number of attempts spread over hours or days, you mark the delivery as failed and stop. What you do then defines the quality of your platform. The best implementations expose failed deliveries to the customer, let them inspect the payload and the response they returned, and offer a manual replay so that a customer who fixes their endpoint can recover the events they missed without needing to contact support.
Security: proving the request came from you
A webhook endpoint is a URL sitting on the public internet, and anyone who discovers it could send it fake events. If your webhooks trigger meaningful actions, such as marking an order as paid, this is dangerous. The standard defense is to sign each payload. You compute a cryptographic signature of the request body using a secret shared with that specific customer, and you include the signature in a header. The customer recomputes the signature on their end and rejects any request where it does not match. This proves both that the request came from you and that the body was not tampered with in transit. Including a timestamp in the signed content and rejecting old requests further protects against replay attacks, where someone captures a valid webhook and sends it again later.
Observability and the developer experience
Webhooks are a developer-facing feature, and developers judge them by how easy they are to debug. When an integration is not working, the customer’s first question is “did you even send the event, and what happened.” A platform that cannot answer this generates endless frustrated support tickets. The solution is a delivery log the customer can see: every event, when it was sent, how many attempts were made, what status code came back, and the response body. A dashboard showing this turns a mysterious integration failure into a self-service investigation. Providing a way to send test events on demand, so a developer can wire up their endpoint and immediately verify it receives something, dramatically shortens the time from signup to working integration.
Ordering, versioning, and other sharp edges
Two subtler issues catch teams by surprise. The first is ordering. Because events are delivered independently and retried, they can arrive out of order; an “updated” event might land before the “created” event it logically follows. Rather than promising strict ordering, which is expensive to guarantee, most platforms include enough information in each payload, such as a timestamp or version number, for the receiver to reason about sequence themselves. The second is payload versioning. Over time you will want to change the structure of your webhook payloads, but customers have written code against the current shape and a breaking change will silently break their integrations. Versioning your event schemas, and giving customers a way to opt into new versions on their own schedule, prevents you from breaking working integrations every time you improve the product.
Reliable webhooks are a quiet mark of platform maturity. Customers rarely praise webhooks that simply work, but they remember vividly the ones that dropped a critical event or could not be debugged. Investing in decoupled delivery, sensible retries, clear security, and a transparent delivery log turns a feature that could generate constant support pain into one that developers trust and build businesses on top of.