Webhooks vs Message Queues
Webhooks and message queues both solve asynchronous event delivery — but they operate at different layers and solve different problems. Webhooks connect systems across the internet over HTTP. Queues buffer and broker events internally within your own infrastructure.
Use Webhooks when…
- •You receive events from a third-party SaaS (Stripe, GitHub, Shopify)
- •You need to notify an external consumer endpoint
- •The sender controls the delivery mechanism
- •Real-time push to a registered HTTPS endpoint is required
Use a Message Queue when…
- •You need guaranteed delivery with durable storage
- •Multiple consumers need to process the same event
- •Processing is slow and must not block the producer
- •You need strict ordering or exactly-once delivery semantics
Comparison Table
| Property | Webhooks | Message Queues |
|---|---|---|
| Delivery model | HTTP POST to a registered URL (push) | Messages stored in broker; consumers pull or are notified |
| Real-time delivery | Yes — delivered immediately when the event occurs | Near real-time for pull consumers; depends on polling interval |
| Retry behavior | Sender retries on failure (usually 3–10 times with backoff) | Broker retries until acknowledged; configurable retry count and dead-letter queue |
| Durability | Not durable — if your endpoint is down, events can be lost | Durable — messages persist on disk until acknowledged |
| Message ordering | Not guaranteed — retries can cause out-of-order delivery | FIFO ordering available (SQS FIFO, Kafka partition ordering) |
| Fan-out (multiple consumers) | Not built-in — one endpoint per webhook registration | Built-in with pub/sub (SNS, Kafka topics, RabbitMQ exchanges) |
| Complexity | Low — just an HTTP endpoint you register with the sender | Higher — requires broker infrastructure, consumer processes, connection management |
| Failure recovery | Limited — rely on sender retry window, then manual replay | Full — dead-letter queue, message inspection, and replay |
| External system integration | Ideal — designed for cross-system, cross-company event delivery | Not designed for external systems — internal infrastructure only |
| Backpressure handling | None — sender fires regardless of consumer load | Built-in — consumers process at their own pace; producer is decoupled |
What Webhooks Are
A webhook is an HTTP callback delivered over the public internet. When an event occurs in a source system — a payment is completed, a code push is made, an order is fulfilled — the source system sends an HTTP POST request to a URL you registered. Your server receives the request, processes the payload, and returns a 2xx response.
Webhooks are the standard integration primitive in SaaS. Stripe, GitHub, Shopify, Twilio, and thousands of other services deliver events via webhook. The simplicity is intentional: any web server can receive a POST request.
Advantages
- ✓No infrastructure to manage — just an HTTPS endpoint
- ✓Real-time: events arrive immediately
- ✓Universal: any HTTP server can receive them
- ✓Standard pattern for third-party SaaS integration
Limitations
- ✗Not durable — events are lost if your endpoint is down
- ✗No built-in fan-out to multiple consumers
- ✗Delivery ordering is not guaranteed
- ✗Your endpoint must be publicly reachable (HTTPS)
What Message Queues Are
A message queue is a durable broker that stores messages produced by one component until another component (the consumer) retrieves and processes them. The producer and consumer are fully decoupled — they don't need to be running simultaneously. If the consumer is down, messages accumulate in the queue and are processed when it comes back.
Common queue technologies: Amazon SQS (managed, simple), RabbitMQ (flexible routing, AMQP), Apache Kafka (high-throughput, log retention, replay), BullMQ / Sidekiq (Redis-backed job queues for application code), Google Pub/Sub, Azure Service Bus.
Advantages
- ✓Durable — messages survive consumer downtime
- ✓Fan-out — multiple consumer groups can receive each event
- ✓Backpressure — consumers process at their own rate
- ✓Dead-letter queues for failed message inspection and replay
Limitations
- ✗Requires broker infrastructure and operational overhead
- ✗Not designed for external (cross-company) event delivery
- ✗Higher complexity — producers, consumers, and broker to manage
- ✗Learning curve for at-least-once vs exactly-once semantics
The Hybrid Pattern: Webhooks into Queues
The most robust pattern for SaaS integrations combines both technologies. Your webhook endpoint receives the inbound event, returns a 200 immediately, and enqueues the payload for asynchronous processing. This pattern solves the main weakness of each approach.
Stripe, GitHub, etc.
Verify, enqueue, return 200
SQS, BullMQ, RabbitMQ
Database, email, API calls
Fast acknowledgement
The webhook handler returns 200 in under 100ms by only verifying the signature and enqueuing. No database writes or downstream API calls in the hot path. Eliminates sender timeouts and spurious retries.
Idempotent processing
The worker deduplicates by delivery ID before processing. If the webhook is retried (due to a transient network failure), the second delivery is discarded after the queue check. Prevents duplicate orders, emails, or charges.
Failure isolation
A failure in the worker (database down, downstream timeout) does not affect the webhook endpoint. The event stays in the queue and is retried. A permanent failure routes to a dead-letter queue for inspection.
Common Mistakes
Processing synchronously in the webhook handler
Doing database writes, sending emails, or calling downstream APIs in the webhook handler causes timeouts. The sender marks the delivery as failed and retries. Your handler may process the event multiple times. Return 200 immediately and enqueue.
Using a queue for third-party webhook reception
You cannot put a message queue between a third-party sender (Stripe, GitHub) and your endpoint. The sender sends to a URL. Your endpoint must be an HTTP server. The queue goes after your handler — inside your infrastructure.
Not making webhook handlers idempotent
Webhook senders retry on failure. Your handler will receive the same event multiple times. If you create a database record on each delivery without deduplication, you'll end up with duplicate data. Use the delivery ID as an idempotency key.
Treating queue messages as at-most-once
Most queue systems guarantee at-least-once delivery — a message may be delivered more than once (especially after a consumer crash). Design your consumers to be idempotent. Do not assume a message is delivered exactly once unless you've explicitly configured the queue for it (SQS FIFO with deduplication, Kafka idempotent producer).
No dead-letter queue
Without a dead-letter queue, events that fail processing after maximum retries are silently dropped. A dead-letter queue captures these events for inspection and manual replay. Set one up from the start — adding it later requires re-provisioning.