SageMaker Feature Store Pricing Guide

SageMaker Feature Store is a managed repository for storing, sharing, and reusing ML features. It has two storage layers — Online Store for low-latency serving and Offline Store for historical training data — each with different pricing models. The Online Store is where costs can surprise you: at $2.726/GB-month for storage and $7.45 per million write units, a feature group with frequent updates and large record sizes can cost hundreds of dollars per month.

The core cost question is whether a managed feature store justifies its price over a DIY solution using DynamoDB and S3. For teams with 5 or more models sharing features, the answer is almost always yes — the consistency, reuse, and governance benefits outweigh the cost premium.

TL;DR: Feature Store Online pricing: $1.75/million read units, $7.45/million write units, $2.726/GB-month storage. Offline Store uses standard S3 pricing ($0.023/GB-month). A typical setup with 10 feature groups and moderate traffic costs $50-200/month. Optimize by keeping Online Store records small and using Offline Store for batch training.

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Pricing Components

Online Store

The Online Store provides single-digit millisecond reads for real-time inference. It is backed by a managed key-value store.

How read/write units are calculated:

• 1 read unit = 1 strongly consistent read for a record up to 4 KB
• Records larger than 4 KB consume additional read units (rounded up to the nearest 4 KB)
• 1 write unit = 1 write for a record up to 1 KB
• Records larger than 1 KB consume additional write units (rounded up to the nearest 1 KB)

Example: A 6 KB feature record costs 2 read units per read (rounded up from 1.5) and 6 write units per write.

Offline Store

The Offline Store persists feature data in S3 in Parquet format for historical queries and training data generation.

Offline Store costs are dominated by S3 storage. Since feature data is stored in Parquet (columnar, compressed), the actual storage is typically 3-5x smaller than raw row-oriented data.

Feature Processing (Spark Jobs)

Feature transformation pipelines run as SageMaker Processing jobs on Spark clusters:

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Cost Estimation by Scale

Small Feature Store (Startup, 2-3 Models)

Medium Feature Store (Growth Stage, 10 Models)

Large Feature Store (Enterprise, 50+ Models)

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Managed Feature Store vs DIY

Building your own feature store with DynamoDB (online) and S3 (offline) is a common alternative. Here is the cost and effort comparison:

Cost Comparison (Medium Scale)

Feature Comparison

Verdict: DIY saves roughly 30-45% on infrastructure costs but requires significant engineering effort to build and maintain. For teams with fewer than 3 ML engineers, managed Feature Store is typically more cost-effective when you factor in engineering time. For large platform teams with existing DynamoDB expertise, DIY may make sense.

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Cost Optimization Tips

1. Keep Online Store records small. Write units are charged per 1 KB. A 10 KB record costs 10 write units per write vs 1 unit for a 1 KB record. Store only features needed for real-time inference in the Online Store. Put everything else in Offline only.

2. Use Offline Store for training data generation. Never query the Online Store for batch training. Use Athena queries against the Offline Store (Parquet on S3) at $5/TB scanned instead of paying per-read-unit charges.

3. Partition feature groups by access pattern. Separate high-frequency features (updated every minute) from low-frequency features (updated daily). This prevents high write costs on features that rarely change.

4. Enable Offline Store for feature groups that need historical data. If you only need real-time serving, disable Offline Store to avoid S3 storage and Glue catalog overhead.

5. Compact Offline Store data periodically. The Offline Store appends new Parquet files with every update. Over time, this creates many small files. Run periodic compaction jobs to reduce Athena query costs and S3 request charges.

6. Use batch ingestion instead of record-by-record writes. Batch ingestion via PutRecord API is more efficient than individual writes and reduces the total number of write units consumed.

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Related Guides

• AWS SageMaker Pricing: Training, Inference, Studio
• AWS DynamoDB Pricing Guide
• AWS S3 Pricing Guide
• AWS SageMaker Cost Optimization: Cut ML Costs

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FAQ

How much does SageMaker Feature Store cost per month?

For a small setup (5 feature groups, 5M reads/month, 1M writes/month), expect $20-30/month. A medium setup (20 feature groups, 50M reads/month, 10M writes/month) costs $200-250/month. Large enterprise deployments with 100+ feature groups and hundreds of millions of reads/writes can exceed $2,000/month.

Is SageMaker Feature Store cheaper than DynamoDB?

For the online layer, DynamoDB on-demand pricing is typically 30-45% cheaper for raw read/write operations. However, Feature Store includes automatic offline sync, time-travel queries, schema management, and feature lineage that you would need to build yourself with DynamoDB. Factor in engineering time when comparing costs.

Can I use Feature Store without the Online Store?

Yes. You can create feature groups with Offline Store only, skipping the Online Store entirely. This is useful for feature groups used exclusively in batch training where real-time low-latency access is not needed. Offline-only feature groups cost only S3 storage plus Athena query charges.

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