Webhooks vs Event Streaming
Webhooks push HTTP events to a single endpoint. Event streaming platforms (Kafka, Kinesis, Pub/Sub) write events to a durable log that many consumers can read independently. The right choice depends on who your consumers are, whether you need replay, and how much complexity you can afford.
Use Webhooks when…
- ✓You are integrating with an external SaaS product
- ✓There is a single consumer (your server)
- ✓Event volume is low to medium
- ✓You do not need to replay past events
- ✓You cannot run infrastructure (no Kafka cluster)
- ✓You want the sender to manage delivery and retries
Use Event Streaming when…
- ✓Multiple independent services consume the same events
- ✓You need to replay events from any point in history
- ✓Event volume is high (millions per second)
- ✓Strict ordering within a partition is required
- ✓You are building event sourcing or CQRS architectures
- ✓You can afford to operate a streaming cluster
Side-by-Side Comparison
| Feature | Webhooks | Event Streaming (Kafka / Kinesis / Pub/Sub) |
|---|---|---|
| Delivery model | Push — sender POSTs to consumer's HTTP endpoint | Pull — consumers read from a shared topic/stream |
| Persistence | None — fire and forget; no storage on sender side | Yes — events stored for configurable retention (days to indefinite) |
| Replay | No (most senders offer limited manual replay) | Yes — consumers can seek to any offset and re-read from any point |
| Fan-out | One endpoint at a time; sender calls each separately | Many independent consumer groups, each with their own offset |
| Ordering | Not guaranteed across retries or concurrent deliveries | Guaranteed within a partition; global ordering requires single partition |
| Throughput | Low–medium (limited by HTTP connection overhead) | Very high — millions of events/sec with horizontal scaling |
| Failure handling | Sender retries with backoff; receiver must be idempotent | Consumer controls offset; can re-read failed events without re-delivery |
| Cross-org delivery | Yes — designed for external SaaS-to-SaaS delivery | No — typically internal systems only |
| Infrastructure | None on receiver side (just an HTTPS endpoint) | Cluster management required (Kafka brokers, Zookeeper/KRaft, schema registry) |
| Complexity | Low — HTTP endpoint, verify signature, respond 200 | High — topics, partitions, consumer groups, offset management |
| Cost model | Free for receiver; sender pays for delivery attempts | Pay for storage + throughput + cluster compute |
What Are Webhooks?
A webhook is an HTTP POST that a source system sends to your server when an event occurs. The sender (Stripe, GitHub, Shopify) manages the delivery — they handle the HTTP connection, retry on failure, and rotate secrets. Your job is to expose an HTTPS endpoint, verify the signature, and return a 200 response.
Webhooks are pull-from-push: the sender pushes data to you, and you react. They are the standard integration pattern for SaaS-to-SaaS communication because they require no shared infrastructure. You give the sender a URL. They deliver events to it.
Typical webhook flow:
Stripe → POST /webhooks/stripe → verify X-Stripe-Signature → parse body → return 200 → enqueue job → process payment
What Is Event Streaming?
Event streaming platforms (Apache Kafka, Amazon Kinesis, Google Pub/Sub, Confluent, Redis Streams) act as a durable, ordered log. Producers write events to a topic. Consumers read from that topic independently, at their own pace, from any offset. The log persists for a configurable retention period — minutes to indefinitely.
This changes the failure model fundamentally. In webhooks, the sender controls delivery. In streaming, the consumer controls consumption. If a consumer crashes, it simply reads from where it left off when it restarts — no retry logic required in the sender.
Apache Kafka
Open source. High throughput, low latency, persistent log. Used for internal event buses, change data capture, and event sourcing.
Amazon Kinesis
Managed AWS service. Simpler than Kafka, integrates with Lambda/Firehose. Good for log ingestion and real-time analytics pipelines.
Google Pub/Sub
Managed GCP service. Serverless, auto-scaling, push and pull delivery modes. Supports filtering and ordering by key.
When Webhooks Are the Right Choice
External SaaS integration
When Stripe, GitHub, Shopify, or any external service needs to notify you of events, webhooks are the only practical option. The sender controls the delivery infrastructure — you can't ask them to write to your Kafka cluster. Webhooks are the standard interface for cross-organization event delivery.
Simple, single-consumer notifications
If exactly one system needs to react to an event, webhooks are sufficient. A payment confirmation email, a Slack notification on a new GitHub issue, a database update when a CRM deal closes — these are single-consumer, low-volume use cases where a streaming cluster would be over-engineered.
No infrastructure budget
Running Kafka requires brokers, ZooKeeper or KRaft, schema registry, and monitoring. Managed services (Confluent Cloud, MSK) start at $200–$500/month for production use. Webhooks require only an HTTPS endpoint — which you likely already have.
Low-to-medium event volume
If you process hundreds to thousands of webhook events per day, the HTTP overhead of webhooks is negligible. At millions of events per minute, that overhead matters and streaming becomes appropriate.
When Event Streaming Is the Right Choice
Multiple independent consumers
If an order.completed event needs to trigger fulfillment, analytics ingestion, fraud scoring, and a notification service independently, webhooks require the sender to call four endpoints separately. Streaming lets each service consume the same topic with its own offset — no coordination between consumers, no blast radius if one is slow.
Replay and backfill requirements
Webhooks cannot be replayed beyond the sender's retry window (usually 1–3 days). Streaming platforms retain events for days, weeks, or indefinitely. When you deploy a new service that needs to process historical events, it can replay from offset zero — impossible with webhooks.
Event sourcing and CQRS
In event-sourced systems, the event log is the source of truth. Projections (read models) are built by replaying the log. Streaming platforms are designed for this. Webhooks are not — they have no durable log, no offset management, and no backpressure mechanism.
High-throughput internal pipelines
Log ingestion, clickstream processing, IoT telemetry, and change data capture from databases (CDC via Debezium) generate millions of events per second. Streaming platforms handle this throughput horizontally. HTTP webhooks at this scale would require thousands of simultaneous connections.
Hybrid Architectures
Most production systems use both. The typical pattern is to receive webhooks from external services, validate and enqueue them, then publish to an internal event stream for downstream consumers.
Common hybrid pattern
The webhook handler validates the signature, deduplicates by delivery ID, publishes a normalized event to an internal Kafka topic, and returns 200. Each downstream service consumes the topic independently with its own consumer group. If Service A is slow, Service B is unaffected.
This hybrid approach gives you the best of both worlds: the simplicity of webhooks for external integration and the fan-out, replay, and ordering guarantees of streaming for internal processing.