Why We Removed DynamoDB Accelerator (DAX): Improving Reliability, Reducing Costs, and Simplifying Our Architecture

As part of scaling a production system built on Amazon DynamoDB, we introduced Amazon DynamoDB Accelerator (DAX) to reduce read latency and improve performance.

DAX is often positioned as a drop-in solution for high-performance caching. In theory, it delivers microsecond response times and reduces pressure on DynamoDB.

However, in our real-world workload, it introduced scalability constraints, operational overhead, and reliability risks.

After a detailed evaluation, we removed DAX entirely. The outcome:

• ~$600 cost reduction
• Eliminated ~30-minute recovery delays during peak load failures
• Improved system reliability and scalability
• Simplified overall architecture

This post breaks down what happened, why DAX became a bottleneck, and what we learned.

System Context

Our system handled:

• High read throughput
• Spiky traffic patterns
• Latency-sensitive APIs

Initial architecture:

Client → DAX Cluster → DynamoDB

The goal was straightforward:

• Cache frequently accessed data
• Reduce database load
• Improve response times

Where Things Started to Break

1. Scaling Mismatch Between DAX and DynamoDB

A key assumption we made was that DAX would scale similarly to DynamoDB.

That assumption was wrong.

• DynamoDB supports seamless, near-instant auto-scaling
• DAX requires manual node scaling

As traffic increased:

• The DAX cluster hit capacity limits
• Cache performance degraded
• Latency increased instead of decreasing

👉 This created a single point of failure in an otherwise scalable system

2. Critical Recovery Delays (~30 Minutes)

The most impactful issue was recovery time.

When the DAX cluster:

• Reached maximum capacity
• Became unstable

It required approximately 30 minutes to return to a healthy operational state

During this window:

• API latency increased significantly
• Error rates spiked
• System reliability degraded

👉 For a production system, this was unacceptable.

3. Cost vs Performance Trade-off

We analysed:

• Cache hit rates
• Latency improvements
• Infrastructure cost

Findings:

• Performance gains were marginal for our workload
• DynamoDB alone handled traffic efficiently
• DAX added ~$600 cost without proportional value

The Decision Framework

Before removing DAX, we evaluated:

• Can we optimise DynamoDB queries instead?
• Is caching truly required for our access patterns?
• Does DAX provide measurable business value?

Conclusion:

👉 DAX was not solving a critical problem, but introducing new ones.

The New Architecture

We simplified the system to:

Client → DynamoDB

Key improvements:

• Removed scaling bottleneck
• Reduced operational complexity
• Improved reliability

Results

✅ Cost Optimisation

• Reduced infrastructure spend by ~$600

✅ Improved Reliability

• Eliminated long recovery windows
• Removed DAX-related failure scenarios

✅ Better Scalability

• DynamoDB handled load natively
• No manual scaling required

✅ Operational Simplicity

• Fewer moving parts
• Easier debugging and maintenance

Key Engineering Lessons

🔹 1. Not All Managed Services Are Fully Scalable

Even managed services like DAX can introduce:

• Hard limits
• Manual intervention requirements

Always validate scaling behaviour under real load.

🔹 2. Avoid Introducing Bottlenecks in Distributed Systems

Ironically, adding DAX:

• Reduced system resilience
• Created a choke point

👉 Every new component must be evaluated as a potential failure point

🔹 3. Measure Before Optimising

Caching is powerful — but only when:

• Cache hit rates are high
• Latency improvements are meaningful

Otherwise, it becomes unnecessary complexity.

🔹 4. Simplicity is a Competitive Advantage

By removing DAX, we:

• Improved reliability
• Reduced cost
• Simplified architecture

👉 Simpler systems are easier to scale and operate.

When DAX Still Makes Sense

DAX can be valuable if:

• You have extremely read-heavy workloads
• You need sub-millisecond latency
• Your access patterns are highly cacheable

But it may not be suitable when:

• Traffic is highly dynamic
• Auto-scaling is critical
• Simplicity and resilience are priorities

Final Thoughts

This was a classic case where:

Adding a component improved performance in theory, but reduced system reliability in practice.

Removing DAX was not just a cost decision — it was an architectural correction.

As engineers, one of the most impactful decisions we can make is not what to add… but what to remove.