The TechBeat: 4 DynamoDB Configuration Changes for Significant Cost Savings (5/30/2026)

Why DynamoDB costs creep up

Many teams adopt DynamoDB for its serverless promise, but the pay‑per‑request model can hide inefficiencies. A typical pattern is to provision generous read/write capacity or enable features that add hidden overhead. When traffic spikes, the bill can jump 3‑5× overnight, prompting frantic scaling discussions.

The good news is that most of those spikes are avoidable with a few well‑understood configuration tweaks. Below we walk through four changes that have repeatedly delivered 20‑40 % savings for mid‑scale workloads (10‑50 M reads/writes per month) while keeping latency in the low‑single‑digit milliseconds range.

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1. Switch from Provisioned to On‑Demand mode for bursty workloads

The problem

Provisioned capacity charges you for the maximum capacity you reserve, regardless of whether you actually use it. If your traffic is uneven—say, a nightly batch job followed by a quiet daytime window—you’re paying for idle capacity.

The change

Enable On‑Demand mode on tables that see irregular traffic patterns. DynamoDB automatically scales reads and writes up to 40,000 RCU and 40,000 WCU per table without any manual intervention.

Impact

A case study from a fintech startup showed a 28 % reduction in monthly spend after moving a high‑variance table (average 150 RCU, peaks of 2,500 RCU) to On‑Demand. Latency stayed under 5 ms because DynamoDB’s internal throttling only kicks in at sustained >10 k RCU, far beyond the observed peaks.

Tip: Use the AWS Cost Explorer filter DynamoDB – On‑Demand to verify that the switch actually reduces the Provisioned line item.

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2. Enable Auto Scaling on provisioned tables and set tighter bounds

The problem

When you keep tables in provisioned mode, many teams set a generous safety margin (e.g., 150 % of observed traffic) to avoid throttling. That cushion translates directly into higher hourly charges.

The change

Activate Auto Scaling with a target utilization of 70 % and a minimum/maximum range that reflects realistic load. For a table that averages 500 RCU, a range of 300‑800 RCU is usually sufficient.

Impact

An e‑commerce platform reduced its provisioned spend by 22 % after tightening the auto‑scale target from 80 % to 70 % and cutting the maximum capacity by 30 %. The number of throttled requests dropped from 0.12 % to 0.03 % because the scaling policies reacted faster to traffic spikes.

Implementation note: The AWS CLI command aws application-autoscaling put-scaling-policy lets you script the policy creation, making it easy to apply the same bounds across dozens of tables.

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3. Turn off DynamoDB Streams for tables that don’t need them

The problem

Streams add a per‑hour charge (currently $0.02 per 100,000 stream read requests) and increase the write‑capacity consumption because each write generates a stream record.

The change

Audit your tables. If you’re not using Lambda triggers, Kinesis pipelines, or CDC tools that rely on streams, disable the feature via the console or the UpdateTable API.

Impact

A SaaS analytics provider disabled streams on 12 low‑traffic tables, shaving $1,200 off its annual bill (about 4 % of its total DynamoDB spend). The change also reduced write latency by ~0.5 ms because DynamoDB no longer needed to write the extra stream record.

Caution: Before turning streams off, verify that no downstream process depends on them. A quick check of CloudWatch logs for StreamArn references can prevent accidental breakage.

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4. Optimize TTL (Time‑to‑Live) settings to purge stale items automatically

The problem

Many applications keep historical records indefinitely, inflating storage costs. DynamoDB charges $0.25 per GB‑month of data stored, so a table that grows to 500 GB can add a sizable line item.

The change

Define a TTL attribute (e.g., expireAt) for data that has a natural lifecycle—session tokens, temporary caches, or logs older than 30 days. DynamoDB automatically deletes expired items within 48 hours of the TTL timestamp.

Impact

A gaming backend that retained session data for 90 days switched to a 30‑day TTL. Within a month, the table’s size dropped from 420 GB to 140 GB, cutting storage costs by roughly $67 per month. Because TTL deletions happen in the background, there was no impact on read/write latency.

Best practice: Combine TTL with DynamoDB Streams (if you need audit trails) so that deletions can be captured for compliance before the stream is turned off.

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Quick implementation checklist

1. Audit traffic patterns – Identify tables with bursty usage.
2. Switch to On‑Demand where variance > 30 %.
3. Enable Auto Scaling on remaining provisioned tables; set target utilization to 70 %.
4. Review Streams – Disable on tables without downstream consumers.
5. Add TTL to any data with a known expiration.
6. Monitor – Use Cost Explorer and CloudWatch metrics for the first two billing cycles to confirm savings.

Bottom line

The four tweaks described above are low‑risk, require only a handful of console clicks or a short script, and have a track record of delivering double‑digit cost reductions. For teams already wrestling with a growing DynamoDB bill, the ROI on these changes is immediate and measurable.

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For deeper dives into each setting, see the official AWS documentation: On‑Demand Capacity Mode, Auto Scaling, DynamoDB Streams, and Time‑to‑Live.