Webhooks vs Polling: When to Use Each and How to Monitor Both

Every time you need to get data from an external system, you face a fundamental choice: do you ask for it repeatedly until something changes, or do you wait for the system to tell you when something changes?

That's polling vs webhooks in one sentence. Both are legitimate architectural patterns, both are widely used, and both have failure modes that require monitoring. The right choice depends on your specific constraints — and sometimes the answer is both.

How Polling Works

Polling is the request-response pattern you already know. Your application periodically sends a request to an API endpoint to check for new data:

Your Server                    External API
    |                              |
    |--- GET /orders?since=...  -->|
    |<-- 200 OK (no new data) ----|
    |                              |
    |    (wait 30 seconds)         |
    |                              |
    |--- GET /orders?since=...  -->|
    |<-- 200 OK (2 new orders) ---|
    |                              |
    |    (wait 30 seconds)         |
    |                              |
    |--- GET /orders?since=...  -->|
    |<-- 200 OK (no new data) ----|

Your server drives the interaction. You decide how often to check, what to check for, and how to handle the response.

How Webhooks Work

Webhooks invert the control. You register a URL with the external service, and the service sends an HTTP POST to that URL whenever something happens:

Your Server                    External Service
    |                              |
    |    (register webhook URL)    |
    |                              |
    |          ... time passes ... |
    |                              |
    |<-- POST /webhook (order 1) --|
    |--- 200 OK ------------------>|
    |                              |
    |<-- POST /webhook (order 2) --|
    |--- 200 OK ------------------>|

The external service drives the interaction. You respond to events as they arrive.

The Trade-offs

Latency

Webhooks win. You're notified within seconds (sometimes milliseconds) of an event occurring. With polling, your maximum latency equals your polling interval. If you poll every 30 seconds, you might discover a change 30 seconds after it happened.

You can reduce polling intervals, but there's a floor — most APIs enforce rate limits that prevent you from polling more than a few times per minute.

Resource Efficiency

Webhooks win in most scenarios. Consider an API where changes happen 10 times per day. With 30-second polling, you make 2,880 requests per day to discover 10 events. That's 2,870 wasted requests.

With webhooks, you receive exactly 10 requests — one per event. Your server processes only meaningful data.

However, the picture changes for high-frequency data. If an API produces thousands of events per minute, a webhook flood can overwhelm your server. In that case, periodic batch polling might be more efficient than processing individual events.

Reliability

This is where it gets nuanced.

Polling is inherently resilient. If a poll request fails, you try again next interval. The data is still there on the API server, waiting. You might be delayed, but you won't lose data.

Webhooks have the inverse reliability profile. The data comes to you, which is great — unless you're not ready to receive it. If your server is down, the webhook fails. Most providers retry, but retry policies vary enormously (GitHub retries 3 times in 30 seconds; Stripe retries for 3 days). If all retries fail, the event is lost.

Complexity

Polling is simpler to implement. It's a scheduled HTTP request. You already know how to make HTTP requests. The main complexity is managing state — remembering what you've already seen so you don't process duplicates.

Webhooks require more infrastructure:

• A publicly accessible HTTPS endpoint
• Signature verification (HMAC, etc.)
• Idempotency handling (you will receive duplicates)
• A retry/dead-letter strategy for your own failures
• Monitoring to detect when deliveries stop arriving

Cost

Polling costs scale with your polling frequency, not your event frequency. You pay for every API call, whether or not there's new data.

Webhook costs scale with event frequency. You only pay for compute when something actually happens. But you also pay for the infrastructure to receive webhooks — a server that's always running, TLS certificates, domain management.

The Decision Matrix

When to Use Both

The most robust integrations combine both approaches:

                    ┌─────────────────┐
                    │ External Service │
                    └────────┬────────┘
                             │
              ┌──────────────┼──────────────┐
              │              │              │
    ┌─────────▼──────┐      │    ┌─────────▼──────┐
    │   Webhook       │      │    │  Polling Job    │
    │   (real-time)   │      │    │  (every 5 min)  │
    └─────────┬──────┘      │    └─────────┬──────┘
              │              │              │
              └──────────────┼──────────────┘
                             │
                    ┌────────▼────────┐
                    │  Deduplication   │
                    │  Layer           │
                    └────────┬────────┘
                             │
                    ┌────────▼────────┐
                    │  Your Business   │
                    │  Logic           │
                    └─────────────────┘

Use webhooks as the primary channel for real-time reactivity. Use polling as a backup to catch anything the webhooks missed. The deduplication layer ensures you process each event exactly once.

This pattern is common in payment processing. You receive Stripe webhooks for instant payment confirmation, but you also poll the Charges API every few minutes to catch any webhooks that were lost due to server downtime or retry exhaustion.

// Webhook handler — process immediately
func handleStripeWebhook(event stripe.Event) {
    if isAlreadyProcessed(event.ID) {
        return // Deduplicate
    }
    processPaymentEvent(event)
    markProcessed(event.ID)
}

// Polling job — catch missed webhooks
func pollForMissedPayments() {
    since := getLastPollTimestamp()
    charges := stripe.ListCharges(since)
    for _, charge := range charges {
        if !isAlreadyProcessed(charge.ID) {
            processPaymentEvent(charge)
            markProcessed(charge.ID)
        }
    }
    updateLastPollTimestamp(time.Now())
}

Monitoring Polling-Based Integrations

Polling seems simple, but it has its own failure modes:

What to Monitor

• Poll success rate — is the API returning 200s? A spike in 429 (rate limited) or 500 (server error) responses means your integration is degrading.
• Empty response rate — if 99% of your polls return no new data, you might be polling too frequently. If polls that used to return data consistently start returning empty, the integration might be broken.
• Processing lag — the time between an event occurring and your system processing it. With polling, this is bounded by your interval plus processing time.
• Rate limit headroom — how close are you to the API's rate limit? If you're at 80% of your quota, adding a new feature that polls the same API could push you over.

Common Polling Failures

The cursor drift problem: Your poll uses a timestamp to fetch events since the last check. If your server's clock drifts, or if the API's clock disagrees with yours, you can miss events in the gap or process duplicates from the overlap.

// Fragile: depends on clock sync
events := api.GetEvents(lastPollTime)

// Better: use API-provided cursor/pagination
events := api.GetEvents(lastCursorID)

The rate limit cascade: You poll 5 APIs at the same interval. One API goes slow, causing your polling loop to back up. Now all 5 APIs get polled late. Rate limits compound the delay.

The silent failure: Your cron job that runs the polling loop fails silently (see our article on cron job monitoring). No polls run, no data arrives, and nobody notices until a downstream report is empty.

Monitoring Webhook-Based Integrations

Webhooks require more active monitoring because failures are silent by default.

What to Monitor

• Delivery success rate — percentage of webhooks that your endpoint acknowledged with a 2xx response
• Response latency — how long your endpoint takes to respond. Slow responses trigger provider timeouts, which look like failures.
• Event gap detection — if you normally receive ~100 webhooks per hour and suddenly receive 0, something is wrong. This is the hardest metric to implement because it requires understanding your normal traffic pattern.
• Retry rate — a high retry rate means your primary processing is unreliable
• Endpoint availability — is your webhook URL reachable? A simple uptime check catches infrastructure failures.

The Gap Detection Problem

The trickiest aspect of webhook monitoring is detecting when webhooks stop arriving. With polling, a failed poll is an active event you can alert on. With webhooks, the absence of events is... nothing. Silence.

You need a "dead man's switch" approach:

1. Track the last time you received a webhook from each provider
2. Set an expected interval based on historical patterns
3. Alert if the interval is exceeded

// Simplified gap detection
func checkWebhookGaps() {
    providers := getActiveWebhookProviders()
    for _, provider := range providers {
        lastReceived := getLastWebhookTime(provider)
        expectedInterval := getExpectedInterval(provider)

        if time.Since(lastReceived) > expectedInterval * 3 {
            alert(fmt.Sprintf("No webhooks from %s in %v",
                provider, time.Since(lastReceived)))
        }
    }
}

This is an area where dedicated webhook monitoring tools like [HookWatch](https://hookwatch.dev) provide significant value. A proxy layer between the provider and your application can track delivery patterns, detect gaps, and alert on anomalies across all your webhook integrations from a single dashboard.

Real-World Architecture: E-commerce Order Processing

Here's how a real e-commerce system might combine both approaches:

Shopify Webhooks (real-time)
├── orders/create → Process immediately, send confirmation email
├── orders/paid → Update payment status, trigger fulfilment
└── orders/cancelled → Reverse fulfilment, process refund

Shopify Polling (every 5 minutes, backup)
├── GET /orders.json?updated_at_min=... → Catch missed webhook events
└── Reconcile against webhook-processed orders

Monitoring Layer
├── Webhook success rate dashboard
├── Alert: webhook error rate > 5% for 5 minutes
├── Alert: no webhooks received in 30 minutes (business hours)
├── Polling success rate dashboard
├── Alert: polling returning 429 (rate limited)
└── Reconciliation report: orders found by polling but missed by webhooks

Conclusion

Webhooks and polling aren't competing patterns — they're complementary tools for different situations. Webhooks give you real-time reactivity with the trade-off of requiring infrastructure and monitoring. Polling gives you simplicity and reliability with the trade-off of latency and wasted requests.

The best integrations use webhooks for the fast path and polling for the safety net. And regardless of which approach you choose, monitoring is not optional. Polling needs cron monitoring and rate limit tracking. Webhooks need delivery monitoring and gap detection.

The question isn't "webhooks or polling?" It's "what does my use case need, and how do I make sure it keeps working?"