AWS Route 53 Pricing & Cost Guide

Route 53-style costs are typically a combination of hosted zone fees plus DNS query charges and (optionally) health checks. The best cost model starts with measured query volume.

What to model (the 3 line items)

  • Hosted zones: how many zones you maintain across environments/accounts
  • Standard queries/month: your actual DNS query volume
  • Health checks: if you use DNS-level health checks or endpoint monitoring

How to get the inputs (without guessing)

  • Zones: list public and private zones separately. In multi-account setups, zones often get duplicated across environments.
  • Queries: prefer a measured window from query logs. If you do not have logs, start from DNS QPS/RPS and convert to monthly.
  • Health checks: count them, and confirm whether you have per-endpoint checks, multi-region checks, or checks per record.

A fast estimate (2 scenarios, not 1)

Use AWS Route 53 Cost Calculator for a simple zones + queries + health checks model. Then refine query volume using metrics/logs from a representative time window.

  • Baseline scenario: a typical week (exclude outage windows and launch days).
  • Peak scenario: incident retry storms, cache misses, or a short-lived traffic spike. This is what prevents a budget from failing in real life.

Query estimation methods

Method A: From query logs (best)

  • Enable Route 53 query logging and sample at least 7 days.
  • Compute: queries/day by hosted zone and top record names.
  • Use the peak day as a sanity check for incident windows.

Method B: From request rate (good first pass)

  • Convert RPS/QPS to monthly queries using RPS to monthly requests.
  • Apply a multiplier for retries and cache misses (use a separate peak scenario rather than one inflated average).

What usually causes surprise bills (and how to prevent them)

  • Very low TTLs: every resolver cache refresh becomes a paid query. Low TTLs have a purpose, but treat them as a cost decision.
  • Service scale-out: more pods/instances often means more DNS lookups (especially with chatty clients or short DNS caches in runtimes).
  • Retry storms: outages can multiply DNS queries as clients retry or fail over. Keep a peak scenario in your model.
  • Health check sprawl: checks per endpoint and per environment add steady monthly cost and can be missed in budgets.
  • CNAME chains: not always a cost problem, but they can increase lookup behavior and worsen incident sensitivity.

Worked estimate template (copy/paste)

  • Hosted zones cost = public zones + private zones (as applicable)
  • Query cost = standard queries/month (baseline) + standard queries/month (peak scenario)
  • Health checks cost = health checks/month (baseline) + any extra checks used only for peak events

How to validate the estimate

  • In Cost Explorer / CUR, confirm you see distinct line items for hosted zones, queries, and health checks.
  • Compare baseline vs peak: if peak is not materially higher, you may be missing incident behavior in your model.
  • Audit the top record names from query logs and look for noisy internal service discovery patterns.

Related tools

Route 53 cost Estimate DNS queries RPS to monthly Cost checklist

Sources

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Related calculators

Data Egress Cost Calculator
Estimate monthly egress spend from GB transferred and $/GB pricing.
API Response Size Transfer Calculator
Estimate monthly transfer from request volume and average response size.
VPC Data Transfer Cost Calculator
Estimate data transfer spend from GB/month and $/GB assumptions.
Cross-region Transfer Cost Calculator
Estimate monthly cross-region transfer cost from GB transferred and $/GB pricing.
Log Cost Calculator
Estimate total log costs: ingestion, storage, and scan/search.
Log Ingestion Cost Calculator
Estimate monthly log ingestion cost from GB/day or from event rate and $/GB pricing.

FAQ

What usually drives Route 53 cost?
DNS query volume is the most common driver, followed by hosted zone count. Health checks can add a steady baseline if you use many of them.
Why do query charges grow over time?
Low TTLs, service growth (more pods/instances), and resolver behavior can increase query volume. Incident-driven retries can also create sudden spikes.
Should I estimate queries using RPS or from logs?
If you have query logs, use them. If not, RPS can be a good first pass. Convert RPS to monthly queries, then add a peak scenario for incidents and traffic spikes.
Do record types matter for pricing?
For first-pass budgets, query count is usually the key. But certain features (like health checks and advanced routing) can change the model, so treat them as separate line items.
Last updated: 2026-02-23