AWS SQS cost optimization (high-leverage fixes)

SQS spend is usually request-driven. The highest-leverage strategy is to reduce requests per successful message and prevent the multipliers: retries, empty receives, and poison loops. This playbook focuses on changes that are measurable and safe.

Step 0: baseline “requests per message”

• Messages sent/received/deleted per day (representative week)
• Retry rate / redrives (how often messages are processed more than once)
• Empty receives (polling tax)
• Visibility timeout extensions (ChangeMessageVisibility calls)

Estimation workflow: estimate SQS requests

1) Batch operations (reduces requests per message immediately)

• Use batch send/receive/delete where your client supports it.
• Choose a batch size that matches your processing latency goals.
• Validate end-to-end: batching reduces requests but can change how quickly you drain bursts.

2) Reduce empty receives (polling tax)

Empty receives are pure waste: they are billable requests without useful work. Common fixes:

• Enable long polling to reduce empty responses when the queue is quiet.
• Don’t over-provision consumers; scale consumers to backlog/lag, not to peak guesswork.
• For scheduled workloads, don’t poll continuously.

3) Fix retries and poison message loops

• Idempotency: make processing safe to retry without side effects.
• DLQ policy: set maxReceiveCount so poison messages don’t loop forever.
• Timeout tuning: set visibility timeout to cover normal processing time; avoid repeated timeouts.
• Backoff: if you retry, use jitter and a clear stop condition.

4) Reduce “extra” API calls

• Minimize ChangeMessageVisibility calls by aligning visibility timeout with real processing time.
• Avoid designs where one logical message triggers multiple queue operations unnecessarily.
• Watch for consumer restarts that re-receive in-flight messages.

Quantify savings before/after

• Requests/message before vs after (sent/received/deleted metrics)
• Empty receives/day before vs after
• Retry rate and DLQ redrives (poison loop reduction)

Tool: AWS SQS cost calculator

Quick triage: what’s driving requests?

• If received ≫ sent: retries/poison loops are likely dominating.
• If received is high while backlog is near zero: empty receives (polling) are likely dominating.
• If visibility changes are frequent: processing time vs visibility timeout mismatch.
• If DLQ is growing: fix the poison message class; it’s creating repeated requests.

Common pitfalls

• Batching without monitoring backlog (latency may change).
• Scaling consumers aggressively and creating huge empty receive volume.
• Not using DLQs, so poison messages loop indefinitely.
• Visibility timeout too short, causing repeated receives and duplicate work.
• Optimizing SQS requests but ignoring downstream retries (which can recreate the problem).

Related guides

Sources

• AWS SQS pricing
• SQS batch API actions
• SQS dead-letter queues

Related guides

Related calculators

FAQ