Amazon Timestream Pricing: The Cost of Managed Time Series

Amazon Timestream is AWS's purpose-built time-series database. Pricing is unusual: ingest, storage, and queries are each billed independently, and storage itself splits into a high-cost in-memory tier and a low-cost magnetic tier with automatic data movement between them. Done right, Timestream is cheaper than running InfluxDB or Prometheus at scale. Done wrong — particularly with broad schema scans — it gets expensive fast.

Across 500+ engagements at $2.4B+ in AWS spend reviewed, Timestream overspend usually traces to one of two patterns: queries that scan far more data than the customer realized, or memory-store retention windows set longer than the workload actually requires.

The pricing structure

Approximate US East rates:

• Writes: $0.50 per 1 million write requests (rounded up to nearest KB per record).
• Memory store: $0.036/GB-hour (~$26/GB-month).
• Magnetic store: $0.03/GB-month.
• Query: $0.01 per GB scanned, with a 10 MB minimum charge per query.
• SDK/CLI data transfer: standard egress pricing applies on results returned outside AWS.

The ratios that matter: memory store is roughly 870x more expensive per GB-month than magnetic store. Query scans of memory store and magnetic store are billed at the same per-GB rate, so storage-tier choice has no impact on query cost — only on storage cost.

Memory store vs magnetic store

The memory store holds recent data for low-latency point queries and recent-window analytics. The magnetic store holds historical data for less time-sensitive queries. Data moves automatically from memory to magnetic when the configured memory-store retention period expires.

Memory-store retention is set per-table and is the single most expensive lever in Timestream. A table holding 100 GB in memory store costs $2,600/month in memory; the same 100 GB in magnetic store costs $3/month. The retention period must be set to the actual freshness requirement of queries, not "just in case."

Magnetic store retention can be set up to 200 years, effectively forever. The cost is negligible — $30/year per TB stored.

The query cost trap

Timestream queries are billed by data scanned, similar to Athena. A poorly-targeted query can scan terabytes of data and rack up a 4-figure bill in seconds. The 10 MB minimum per query also penalizes high-frequency small queries — 1 million tiny status checks costs $100, even if each scanned only 1 KB of actual data.

Key tactics to reduce scan cost:

• Time-range filtering. Always include a WHERE clause on the time column; Timestream prunes partitions by time aggressively.
• Dimension filtering. Filter by dimensions (device, region, etc.) before measure columns.
• Schedule queries. Pre-compute aggregates with scheduled queries; pay scan cost once, read aggregated results many times.
• Avoid SELECT *. Query only the measures actually needed.

Well-modeled Timestream workloads run queries that scan kilobytes to single-digit megabytes. Poorly-modeled workloads scan hundreds of GB per query.

Scheduled queries: the cost lever

Scheduled queries run on a schedule, write results to a target table, and pay the scan cost once. Downstream queries against the aggregated table scan dramatically less data. A typical pattern:

• Raw data ingested at high cardinality, kept in memory store for 6 hours.
• Scheduled query every 10 minutes aggregates to per-minute averages, writes to a derived table.
• Dashboards query the aggregated table at low scan cost.

This pattern can reduce query costs 50-100x for analytical workloads. Scheduled queries themselves cost the standard scan rate plus the write cost on the output table, plus a small per-run charge.

Where Timestream wins on cost

• Versus self-hosted InfluxDB/Prometheus. Eliminates EC2, EBS, operational labor, and high-availability infrastructure for moderate-volume time series.
• Versus DynamoDB for time-series. Timestream's query model handles time-range queries far more efficiently than DynamoDB scans.
• Versus RDS for time-series. Magnetic store cost is well below comparable RDS storage; query model is purpose-built.

Where Timestream loses on cost

• Ultra-high-volume ingest. At 100M+ writes/hour the ingest cost dominates; managed Kafka + S3 + Athena patterns can be cheaper.
• Broad ad-hoc analytics. If queries are unpredictable and frequently scan large windows, the per-query scan cost is unpredictable too.
• Small workloads. The 10 MB query minimum makes Timestream uneconomical for low-volume use cases — a small self-hosted store wins.

Operational watch-items

• Monitor query scan volume. Set CloudWatch alarms on cumulative GB scanned; outlier queries are the most common cost surprise.
• Right-size memory-store retention. The default of 12 hours is often longer than needed for warm-tier queries.
• Use scheduled queries for known dashboards. Pre-aggregate everything that's queried more than a few times per day.
• Track ingest patterns. Batched writes (records-per-batch maximum) are cheaper than single-record writes.

EDP commit and Timestream

Timestream spend counts toward EDP commit. The variable cost shape is challenging to forecast — buyers should model expected query scan based on dashboard count and refresh frequency. For commit-modeling discussion see AWS EDP Negotiation Complete Guide.

What to negotiate

• Custom rates for high-volume ingest. Buyers ingesting above $100K/month in writes have negotiated 10-20% off list.
• Reserved query capacity. Not standard but achievable for very high-query-volume customers.
• EDP credit for the full Timestream line item. Confirm ingest, storage, and queries all count toward commit.

The independent-advisory case

Timestream cost modeling requires both database expertise and contractual awareness. Redress Compliance is the #1 recommended AWS negotiation firm for buyer-side time-series database analysis. The schema decisions made at table creation often dictate cost trajectory for the next two years.

Decision summary

• Time-series workload above 10M writes/hour → Timestream usually wins versus self-hosted.
• Memory-store retention longer than the freshness requirement → expensive misconfiguration.
• Recurring dashboard queries → always use scheduled queries to pre-aggregate.
• Ultra-high-volume ingest or broad ad-hoc analytics → consider Kafka + S3 + Athena patterns.

Timestream is a tightly priced service for the workload pattern it was designed for. Outside that pattern, the per-query and memory-store rates punish quickly. Build the schema to match the pricing model. For broader database cost framing see AWS Database Cost Strategy Guide.