Estimate Cloud Bigtable costs for nodes, SSD/HDD storage, autoscaling, and multi-cluster replication.
Last verified: May 2026
Production instances require at least 3 nodes.
1 = single cluster, 2+ = multi-cluster replication.
Output will appear here...The estimator computes Bigtable monthly cost as: nodes × per-node-hour rate by storage type (SSD: $0.65/hr per node, HDD: $0.17/hr per node) × hours, plus storage GB × per-GB-month rate ($0.17 SSD, $0.026 HDD), plus replication node costs for multi-cluster instances, plus network egress. Output presents per-cluster breakdown plus the implied throughput capacity (rows/sec by storage type) at the configured node count.
Cloud Bigtable is a petabyte-scale, low-latency NoSQL database designed for large analytical and operational workloads like time-series data, IoT telemetry, and ad-tech pipelines. Bigtable pricing is based on the number and type of nodes (SSD or HDD), storage consumed, and network egress. Node count directly determines throughput capacity, with each SSD node handling roughly 10,000 rows/second for reads. This estimator calculates monthly costs based on your cluster topology, storage class, replication configuration, and expected data volumes.
Your team is building a clickstream analytics database expected to handle 50K rows/sec writes + 10K rows/sec reads, storing 100 TB. The estimator compares: 6-node SSD cluster = $2,800/month + storage $17K/month = $19,800/month (handles 60K rows/s reads). 6-node HDD cluster = $730/month + storage $2,600/month = $3,330/month (handles 3K rows/s reads — insufficient). For this workload, SSD is required despite the cost. The $20K/month decision is justified by the latency requirement; HDD would have failed the read SLA.
HDD storage is ~80% cheaper per GB but caps reads at ~500 rows/s/node — only suitable for batch analytics where 200ms+ latency is acceptable. For any real-time serving (user-facing apps, ad-tech, fraud detection), SSD is the only viable choice despite the cost.
Bigtable nodes have linear scaling: 10 nodes = 10x the throughput AND storage capacity of 1 node. Adding nodes is the simple scaling lever. Don't try to optimize at the row-key level until you've right-sized your cluster — node count is the dominant cost AND performance factor.
Multi-cluster replication doubles compute cost but storage is single-billed. For multi-region read availability, this is acceptable. For DR-only (asynchronous failover acceptable), consider scheduled exports to GCS instead — dramatically cheaper than always-on replication.
SSD storage provides consistent single-digit millisecond latency for both reads and writes, making it ideal for serving real-time applications. HDD storage costs approximately 80% less per GB but has higher read latency (around 200ms) and is suited for large-scale batch analytics or archival workloads where latency is not critical. Each SSD node supports about 10,000 rows/s reads; HDD nodes support about 500 rows/s reads. You cannot mix storage types within a cluster.
Each replicated cluster has its own set of nodes that are billed independently. A two-cluster instance costs roughly double the compute of a single-cluster instance, plus replication network bandwidth. However, storage is only billed once per cluster. Replication provides automatic failover and the ability to route read traffic to the nearest cluster, reducing latency for globally distributed applications.
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