DynamoDB Data Modeling Strategies To Reduce Monthly Costs
DynamoDB costs are all about your data model - fix it and lower these costs dramatically.
DynamoDB is cheap only if your data model is good.
Poor modeling leads to unnecessary reads, over-provisioned capacity, bloated indexes, and surprise bills.
The good news is that most DynamoDB cost issues can be traced back to a small set of design decisions, and fixed with the right strategies.
Let’s walk through the most effective ways to reduce DynamoDB costs, starting at the data model level and expanding outward into operational techniques.
1. Design for Queries, Not for Entities
The fastest way to burn money in DynamoDB is to model it like a relational database.
If your design forces you to:
Scan tables
Filter results after reading
Query multiple tables to assemble one response
You are paying for data you don’t need.
Instead, DynamoDB data modeling should start with access patterns. Ask yourself:
What data is fetched together?
In what order?
How often?
For example, if your application frequently needs “all posts for a user, ordered by creation date,” then that query should be satisfied with a single Query operation, not multiple reads or filters.
A well-designed partition key and sort key allow DynamoDB to return only the exact items you want. Less data read means fewer RCUs consumed and lower latency: both wins.
2. Embrace Single-Table Design to Reduce Redundant Reads
Multiple tables feel clean, but they often increase costs.
Why?
Because related data ends up being fetched separately.
Single-table design allows you to:
Store related entities together
Retrieve multiple entity types in one query
Avoid extra round trips
For example, instead of querying a Users table and then a Posts table, you can store both under the same partition key:
PK = user#123
SK = profile
SK = post#001
SK = post#002One query, one read operation, predictable costs.
The key is this: DynamoDB charges per operation, not per “entity.” Fewer operations equals lower cost.
3. Use GSIs Sparingly and Make Them Purposeful
Global Secondary Indexes are powerful, but expensive if misused.
Every GSI:
Duplicates data
Consumes write capacity
Increases storage costs
The mistake many teams make is creating GSIs “just in case.” Instead, each GSI should exist to serve a specific access pattern that cannot be satisfied by the primary key.
It’s even better to use sparse GSIs when you can. If only 10% of your items need to be queried by a secondary access pattern, only those items should appear in the index.
Sparse indexes drastically reduce:
Index size
WCU usage
Storage costs
If a GSI doesn’t directly support a production query, it probably doesn’t belong.
Creating a sparse index is simple: for the items you want inside the sarse index, define only their partition and sort keys which the index uses.
For example, if an index uses GSI1PK and GSI1SK as primary keys, only the items you want inside the index should have these keys defined, the rest don’t and are filtered out of the index.
4. Keep Items Small and Intentional
DynamoDB charges based on item size. Every extra attribute increases:
Read cost
Write cost
Storage cost
Large items also reduce the efficiency of your queries.
A common optimization is item splitting:
Store frequently accessed attributes in one item
Store rarely used or large attributes (logs, metadata, blobs) in separate items
This ensures your hot paths stay cheap and fast, while cold data doesn’t inflate every read.
Think in terms of access frequency, not convenience.
5. Avoid Filters: They Cost the Same as Full Reads
FilterExpressions look efficient, but they’re deceptive.
DynamoDB applies filters after reading the data. You are charged for all items read, not just the ones returned.
If you regularly filter out 90% of the data, you’re paying for wasted reads.
Instead:
Encode filtering logic into the sort key
Use key conditions like begins_with or BETWEEN
Split access patterns across indexes if needed
When optimizing for costs, filter expressions should only be used after an already filtered query, never with a scan.
The goal is always the same: never read what you don’t intend to use.
6. Use TTL and Time-Based Partitioning
Old data quietly racks up storage costs.
DynamoDB TTL allows you to automatically expire items without manual cleanup (and won’t charge you the delete operations). This is especially effective for:
Logs
Notifications
Temporary sessions
Audit data with retention limits
TTL keeps your table lean over time and prevents long-term storage creep.
For high-volume time-series data, combining TTL with time-based partitioning keeps partitions evenly distributed and cost predictable.
7. Choose the Right Capacity Mode
Finally, capacity mode matters, but only after modeling is right.
On-demand is great for spiky or unpredictable workloads
Provisioned + auto-scaling is cheaper for steady traffic
Reserved capacity can significantly reduce costs at scale
But no capacity setting can fix a bad data model.
If you’re scanning, filtering, or over-indexing, you’ll still pay more than necessary.
Conclusion
DynamoDB cost optimization is not about turning knobs. It’s about thinking differently about data.
Model for access patterns, index only what you use and store intentionally.
When your data model is aligned with how your application actually works, DynamoDB becomes not just fast and scalable, but remarkably cheap.