Estimate DNS queries per month (Route 53 query volume)

DNS query charges are volume-based, so the key input is queries per month. The best estimates come from measured query counts, not guesswork.

Quick DNS query estimate

• Baseline QPS: average resolver queries per second.
• Cache hit rate: effective queries = total x (1 - hit rate).
• Health checks: add query volume from health checks and routing.

Method 1: From Route 53 hosted zone metrics (fastest)

• Use query count metrics per hosted zone and roll up to a month.
• Choose a window that represents typical load (for example, last 30 days).
• If you have multiple zones (prod/staging/dev), estimate each separately so you can see which zone drives the bill.

Method 2: From resolver logs (best for attribution)

• Count DNS requests from resolver logs, then roll up by day.
• Segment by domain/service to identify top query drivers.
• Use this method when you suspect one noisy service, namespace, or environment is dominating volume.

• Separate successful answers vs NXDOMAIN. NXDOMAIN bursts are a common incident pattern and cost driver.
• Rank by FQDN so you can fix the top 5–10 names instead of guessing.

Method 3: From request rate (when you don’t have DNS telemetry)

If you know average request rate, you can approximate DNS queries as:
queries/month ≈ requests/month × DNS lookups per request

This method is crude but useful early. Tighten it later using metrics/logs because caching and TTL can change lookups dramatically.

Worked example (convert telemetry to a budget input)

• If you measure 10 million queries/day, then queries/month ≈ 10M × 30 = 300M.
• Feed 300M into a “$/1M queries” calculator and keep a peak scenario if incidents spike queries.

Sanity checks (avoid common estimation errors)

• Low TTL increases query volume and reduces cache effectiveness.
• Retry loops and service discovery churn can explode query rates.
• Kubernetes cluster size can multiply lookups if workloads resolve frequently.
• Blue/green rollouts can temporarily double query volume (two stacks resolving simultaneously).

If your estimate is unstable, it usually means caching behavior changed (TTL, resolvers) or there is an incident pattern (timeouts/retries) that needs fixing anyway.

Turn volume into cost

Use AWS Route 53 Cost Calculator with your query volume and effective $ per 1M queries pricing.

Next steps

Sources

• Amazon Route 53 pricing
• Route 53 query metrics

Related guides

Related calculators

FAQ