AWS SQS pricing (what to include)

SQS costs are usually predictable: estimate monthly messages, convert to monthly requests, then multiply by $ per 1M requests. Most surprises come from retries and extra receive/delete activity.

What to model (SQS is request-driven)

  • Messages/month: your workload volume (baseline and peak)
  • Requests per message: Send, Receive, Delete, plus any "extra receives" caused by timeouts and retries
  • Queue type and region: use an effective $ per 1M requests that matches Standard vs FIFO and your region
  • Failure rate: retries and poison messages can turn a good estimate into an under-budget in a single day

1) Start with messages per month

If you know throughput (messages/sec), convert to monthly volume first. Use RPS to monthly requests as a rough helper.

  • Use a baseline month and a peak month. Messaging systems are often steady until incidents and backfills arrive.

2) Convert messages into requests

A common baseline is 3 requests per message: Send + Receive + Delete. Add more if you have retries, visibility extensions, or receive polling behavior that increases API calls.

  • Retries: add extra receives and deletes for failures, plus any retry loops in your consumer.
  • Visibility timeouts: if processing exceeds visibility, the same message may be received again (more receives).
  • Batching: batch receive/delete can reduce requests per message if your consumer uses batching effectively.

3) Price it with your effective rate

Use AWS SQS Cost Calculator and plug in your region/queue type effective $ per 1M requests.

  • Save a baseline scenario and a retry-storm scenario so you can compare without losing inputs.

Worked estimate template (copy/paste)

  • Messages/month = producers/month (or RPS converted)
  • Requests/message = Send (1) + Receive (1+) + Delete (1) + retries/visibility effects
  • Requests/month = messages/month * requests/message
  • Queue cost/month = requests/month * ($ per 1M requests / 1,000,000)

4) Common pitfalls (the patterns that inflate spend)

  • Retry storms: timeouts, throttling, and poison messages multiplying requests.
  • Too many receives: aggressive polling, short visibility timeouts, and long processing times.
  • Hidden backfills: migrations and replays that run for weeks (not a "one-time" event in practice).
  • Fan-out misunderstandings: one message can cause many downstream actions (and therefore many SQS operations).
  • Ignoring downstream: Lambda, compute, logs, and databases often dominate total architecture cost.

Comparison: SQS vs SNS cost.

How to validate the estimate

  • In Cost Explorer / CUR, reconcile request usage against your modeled requests/month.
  • In CloudWatch metrics, spot-check message age, receives, and errors around peak windows.
  • Confirm your consumer behavior: visibility timeout, batching, and retry settings match what you modeled.

Related reading

SQS cost calculator Cost estimation checklist Network transfer costs Request-based pricing

Sources

Related guides

AWS SQS cost optimization (high-leverage fixes)
A practical playbook to reduce SQS costs: reduce requests per successful message with batching and long polling, prevent retry storms and poison loops, and validate savings with sent/received/deleted metrics.
Estimate SQS requests (from messages and retries)
A practical workflow to estimate billable SQS request volume: start from messages/month, model requests per successful message (Send/Receive/Delete), and add the multipliers (retries, empty receives, poison loops) that cause spikes.
SQS vs SNS cost: how to compare messaging unit economics
Compare SQS vs SNS cost with a practical checklist: request types, retries, fan-out, payload transfer, and the usage patterns that decide the bill.
SNS pricing: what to model (publishes, deliveries, fan-out)
A practical SNS pricing checklist: publish requests, delivery requests by protocol mix, fan-out after filter policies, and the retry/alert-storm patterns that create surprise delivery volume.
AWS Lambda pricing (what to include)
A practical checklist for estimating AWS Lambda-style costs: requests, duration × memory (GB-seconds), provisioned concurrency when used, logs, and common hidden line items.
CloudWatch dashboards pricing: what to include (dashboard-month + API)
A practical guide to CloudWatch dashboard costs: dashboard-month charges plus the hidden drivers (metrics API requests, alarms, and high-cardinality metrics).

Related calculators

RPS to Monthly Requests Calculator
Estimate monthly request volume from RPS, hours/day, and utilization.
API Request Cost Calculator
Estimate request-based charges from monthly requests and $ per million.
CDN Request Cost Calculator
Estimate CDN request fees from monthly requests and $ per 10k/1M pricing.

FAQ

What is the core SQS cost driver?
Request count. The dominant driver is how many Send/Receive/Delete (and retries) you execute per month.
Why do retries matter for cost?
Retries multiply billable requests. A small failure rate can create large request growth at scale.
How many requests per message should I assume?
A simple baseline is Send + Receive + Delete, then add extra receives for long-running processing, visibility timeouts, and retries. Model baseline and peak scenarios separately.
Do I need to model downstream services too?
Yes. SQS request charges may be small compared to Lambda, compute, logging, and data transfer. Budget the queue and the consumers together.
Last updated: 2026-01-27