4 Proven Ways to Minimize Your AWS MSK Cost

The very tools designed to streamline cloud operations can sometimes stretch budgets thin.

One good example is managing the costs associated with Amazon Managed Streaming for Apache Kafka (MSK). While AWS MSK simplifies deploying and scaling Kafka clusters, the costs can stack up if not optimized.

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Here’s how you can rethink your AWS MSK deployment (whether provisioned or serverless) to keep costs lean and maintain the performance and reliability you need.

Common Cost Pitfalls of AWS MSK

The convenience of AWS MSK can rack up uncomfortable costs if you’re not monitoring:

• Underutilized brokers
• Inefficient storage management, like storing excessive data or neglecting compression practices
• Overprovisioning
• Default or poorly configured retention policies that might store data longer than required

How to Optimize Your AWS MSK Costs

1. Reassess storage policies

If you’re not paying attention, you may store data indefinitely in Kafka. Setting and enforcing appropriate data retention policies can reduce storage overhead without compromising functionality or compliance.

You can:

• Set retention periods that reflect your unique operational requirements and avoid storing redundant data
• Implement log compression to optimize space while preserving critical information
• Periodically review storage configurations, ensuring they remain aligned with current business needs and compliance requirements

2. Optimize data flow

Streamlining Kafka data flow across regions, availability zones, and the open internet can reduce resource consumption, too. A couple of things you can try:

• Adjust the number of partitions and replicas to balance durability with cost efficiency
• Minimize inter-region data transfers by placing producers and consumers in the same region

3. Monitor and act

Proactive monitoring is a great practice for keeping AWS MSK costs in check. Third-party tools that track your resource usage and spending in real time make it possible.

You can set up budget alerts to quickly identify anomalies or spikes, so you’re right on time to block excessive spending. Or conduct quarterly cost reviews to uncover trends and refine your cost optimization strategy. This will highlight small inefficiencies before they turn into major budget issues.

4. Right-size your cluster

As we mentioned in the cost pitfalls above, overprovisioned MSK clusters dig deep into your budget. You definitely shouldn’t be paying for resources you don’t need.

Regularly audit your broker usage, tracking CPU and network utilization. Ensure you’re using the smallest instance that gets the job done and scale up as needed.

Keep in mind: Your clusters should always match actual demand at around 80% CPU utilization. Much less than that and you should scale down.

Also, if you could auto-scale policies to dynamically adjust broker counts based on workload, it would significantly reduce expenses during off-peak hours. But AWS MSK doesn’t have this capability, so you spend more than you need to.

An autoscaler for AWS MSK provides you the capability to implement this dynamic (or auto-adapting) approach.

Summary

While AWS MSK simplifies Kafka management, staying on top of your settings is key to controlling costs. By adjusting storage policies, fine-tuning data flows, monitoring usage with alerts, and right-sizing your clusters to match demand, you can keep your operations efficient and your budget in check. These practical steps help ensure that your cloud setup remains both reliable and cost-effective.