Bigtable cost estimation: nodes, storage growth, and transfer (practical model)

Bigtable-like systems are capacity planned. Your bill is driven by provisioned throughput capacity plus stored data, and it becomes expensive when you are forced to over-provision for peaks or hotspots. This guide gives a simple model you can validate.

0) Identify the workload shape (what forces capacity)

• Read/write throughput: baseline vs peak (batch jobs and backfills are peak).
• Hot keys / hotspots: uneven key distribution forces more capacity.
• Latency targets: stricter tail latency often implies more headroom.

1) Provisioned capacity (node-hours)

Model baseline nodes and peak nodes separately. If peak is rare, do not pay for it 24/7; if peak is frequent, you should treat peak as the real baseline.

Tool: Compute cost model (generic monthly capacity pricing).

• Keep baseline and peak node counts as two explicit scenarios.
• If one job drives peak, model that job separately (duration per day/week).

2) Storage (GB-month) and growth

Storage estimation is about average GB across the month, including growth. If you retain multiple versions or keep long TTLs, storage grows faster than most teams expect.

Tool: Storage growth model.

• Model data retention and any versioning explicitly (retention is a multiplier).
• Validate compaction/GC behavior (retention settings and churn affect stored size).

3) Network transfer (billable boundaries)

If clients are outside the region, or you replicate/serve across regions, include outbound transfer as a separate line. Transfer often becomes meaningful when you add multi-region patterns.

Tools: Egress, Cross-region transfer.

Worked estimate template (copy/paste)

• Baseline node-hours = baseline nodes × hours/month
• Peak node-hours = (peak nodes - baseline nodes) × peak hours/month
• Stored GB-month = average stored GB across the month (include growth)
• Transfer GB/month = cross-region/internet bytes (if applicable)

Common pitfalls

• Ignoring hotspots (hot keys force capacity above average throughput).
• Paying for peak capacity 24/7 even if peak is a short batch window.
• Not modeling retention/versioning (stored GB grows quietly).
• Missing cross-region transfer when adding DR or global clients.
• Not validating with real workload traces (good-day averages hide tail risk).

How to validate

• Validate peak read/write throughput and identify peak drivers (batch jobs, backfills).
• Validate hotspot risk (key distribution, top partitions) and fix the schema before scaling blindly.
• Validate storage growth and retention (including churn and compaction behavior).
• Validate where clients are (same-region vs cross-region) and which legs are billable.

Related reading

Sources

• Cloud Bigtable pricing
• Cloud Bigtable documentation

Related guides

Related calculators

FAQ