Database costs explained: compute, storage growth, backups, and network

Database estimates fail when you model only instance-hours. Real bills are usually: compute baseline + storage GB-month + backups/snapshots retention + replication and network patterns. This hub links the most useful checklists and tools.

1) Compute baseline

  • Model instance-hours or provisioned capacity for steady state (not peak-only).
  • Validate CPU/memory headroom and expected peaks (autoscaling is not always available).
  • Watch for always-on non-prod environments (dev/test) that silently multiply the baseline.

2) Storage GB-month and growth

  • Start with current stored GB and a monthly growth rate.
  • Translate growth into GB-month (average stored GB over the month).
  • Tool: Database storage growth

3) Backups, snapshots, and retention

  • Estimate backup retention days and snapshot policy (daily + weekly + monthly).
  • Check what is free vs billed (varies by provider and engine).
  • Include cross-region backups if you enable them.

4) Network patterns (where surprises come from)

  • Replication: cross-AZ/region replication moves bytes continuously.
  • Cross-region reads: multi-region apps can pay for data transfer and higher latency.
  • Private connectivity: endpoints and data processing can add recurring cost.
  • Tools: egress, cross-region transfer

Related tools

Storage growth Egress cost Cross-region transfer

More database guides

Aurora pricing (what to include): compute, storage, I/O, and backups
A practical checklist for estimating Aurora costs: instance hours (or ACUs), storage growth, I/O-heavy workloads, backups/retention, and the line items that commonly surprise budgets.
Aurora Serverless v2 pricing: how to estimate ACUs and avoid surprise bills
A practical way to estimate Aurora Serverless v2 costs: ACU-hours, storage GB-month, backups/retention, and how to model peaks so your estimate survives real traffic.
AWS CloudWatch Metrics Pricing & Cost Guide
CloudWatch metrics cost model: custom metrics, API requests, dashboards, and retention.
AWS cross-AZ data transfer cost: causes and estimate steps
A practical guide to AWS cross-AZ data transfer costs: common causes (load balancers, databases, Kubernetes), how to estimate GB crossing zones, and how to reduce it safely.
AWS RDS cost optimization (high-leverage fixes)
A short playbook to reduce RDS cost: right-size instances, control storage growth, tune backups, and avoid expensive I/O patterns.
AWS RDS pricing (what to include)
A practical checklist for estimating AWS RDS costs: instances, storage, backups, I/O, and the line items that commonly surprise budgets.
Azure SQL Database pricing: a practical estimate (compute, storage, backups, transfer)
Model Azure SQL Database cost without memorizing price tables: compute baseline (vCore/DTU), storage GB-month + growth, backup retention, and network transfer. Includes a validation checklist and common sizing traps.
Bigtable cost estimation: nodes, storage growth, and transfer (practical model)
A driver-based Bigtable estimate: provisioned capacity (node-hours), stored GB-month + growth, and network transfer. Includes validation steps for hotspots, compactions, and peak throughput that force over-provisioning.
Cloud Spanner cost estimation: capacity, storage, backups, and multi-region traffic
Estimate Spanner cost using measurable drivers: provisioned capacity (baseline + peak), stored GB-month (data + indexes), backups/retention, and multi-region/network patterns. Includes a worked template, common pitfalls, and validation steps.
Cloud SQL pricing: instance-hours, storage, backups, and network (practical estimate)
A driver-based Cloud SQL estimate: instance-hours (HA + replicas), storage GB-month, backups/retention, and data transfer. Includes a worked template, common pitfalls, and validation steps for peak sizing and growth.
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).
CloudWatch Logs Insights cost optimization (reduce GB scanned)
A practical playbook to reduce CloudWatch Logs Insights costs: measure GB scanned, fix query patterns, time-bound dashboards, and avoid repeated incident scans.
Estimate CloudWatch metrics API requests (dashboards and polling)
How to estimate CloudWatch metrics API request volume for cost models: derive requests from dashboards and tooling polling, include refresh rates, and validate with measured usage.
Estimate RDS backup storage (GB-month) from retention and churn
A practical method to estimate RDS backup storage (GB-month): start from daily changed data, retention days, and sanity-check with snapshot sizes. Includes common mistakes that inflate backup cost.
Inter-zone transfer costs on GCP: identify flows, estimate GB/month, and reduce churn
A practical checklist to estimate cross-zone data transfer: load balancers, multi-zone clusters, east-west chatter, and storage/database access patterns. Includes a worked template, validation steps, and control levers.
Metrics and monitoring costs explained: series, cardinality, and retention
A practical framework to estimate metrics bills: number of unique time series (cardinality), retention, dashboards/alerts, and the fastest levers to reduce cost safely.
RDS backups and snapshots (how to estimate cost)
A practical approach to estimating RDS backup and snapshot storage: retention, growth, and the biggest planning mistakes.
RDS snapshot retention policy: cost model and safe defaults
How to choose an RDS snapshot retention policy without surprise costs: model retention vs churn, avoid redundant snapshots, and set long-term retention intentionally.

Related guides

Azure SQL Database pricing: a practical estimate (compute, storage, backups, transfer)
Model Azure SQL Database cost without memorizing price tables: compute baseline (vCore/DTU), storage GB-month + growth, backup retention, and network transfer. Includes a validation checklist and common sizing traps.
Aurora pricing (what to include): compute, storage, I/O, and backups
A practical checklist for estimating Aurora costs: instance hours (or ACUs), storage growth, I/O-heavy workloads, backups/retention, and the line items that commonly surprise budgets.
Cloud SQL pricing: instance-hours, storage, backups, and network (practical estimate)
A driver-based Cloud SQL estimate: instance-hours (HA + replicas), storage GB-month, backups/retention, and data transfer. Includes a worked template, common pitfalls, and validation steps for peak sizing and growth.
RDS vs Aurora cost: what to compare (compute, storage, I/O, and retention)
A practical RDS vs Aurora cost comparison checklist. Compare unit economics, scaling model, storage growth, backups/retention, and the workload patterns that change the answer.
AWS RDS cost optimization (high-leverage fixes)
A short playbook to reduce RDS cost: right-size instances, control storage growth, tune backups, and avoid expensive I/O patterns.
AWS RDS pricing (what to include)
A practical checklist for estimating AWS RDS costs: instances, storage, backups, I/O, and the line items that commonly surprise budgets.

FAQ

What usually drives database cost?
For most managed databases, compute (instance-hours or provisioned capacity) is the anchor. Storage growth and backup retention add steady cost, and network patterns (replication, cross-region reads, egress) create surprises.
How do I estimate quickly?
Start with a baseline size (GB), a monthly growth rate, and a target instance size. Then add backups/retention and sanity-check network (replication, cross-region reads, private connectivity).
What breaks estimates most often?
Underestimating growth, forgetting backups/snapshots retention, and ignoring replication or cross-zone/region data transfer patterns.
Last updated: 2026-01-22