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7 Steps to Reducing Your Logging Costs

Better Stack Team
Updated on January 15, 2024

In modern application environments, the sheer volume of log data being generated can make indexing all of it a costly endeavor.

To avoid exorbitant logging costs, you need to balance the risks of data overload with the necessity of maintaining comprehensive observability.

The dilemma often lies in choosing between reducing costs by filtering logs and retaining complete data for potential troubleshooting and analysis needs.

In this article, we will discuss several strategies you can employ to filter out non-essential log data to manage costs, while still getting the most value out of your log data.

Let's begin!

Step 1 — Start by measuring your logging costs

Before you can start optimizing your log usage and costs, you must assess your current spending on logging tools and associated engineering resources.

You may begin by noting down the specific volumes of log data you're currently processing and their ingestion, storage (for the retention period), and querying costs.

If you're utilizing logging tools that are not part of your primary cloud logging service, ensure to include their associated costs in the estimate.

This might involve accounting for the expenses related to compute power and storage, charges for using data processing services like Apache Kafka or Redis, network fees incurred when aggregating log data, and the investment in engineering personnel dedicated to managing your organization's logging infrastructure.

Once you know where the majority of your logging spend originates from, you can now begin to apply the most relevant techniques below to reduce the spend

Step 2 — Cut down your log volume

In many cases, the most effective way to reduce logging spend is to eliminate the logs you don't need. This approach can be beneficial even with fixed-price contracts with cloud providers, as it can reduce other costs in your pipeline, such as those associated with processing and transferring the log data.

For example, do you really need to log all incoming requests to your servers? If you don't need request-level auditing, it may be best to only log requests that lead to 500 errors to allow for future investigation.

Screenshot 2024-01-15 at 13-18-59 Live tail Better Stack.png

If you're using logs questions like how many requests per second, error rates, most frequently accessed routes, and more, you probably shouldn't. Metrics are a much more effective and cheaper tool for answering these type of questions.

This also means logging at the correct level to eliminate chatty logs that don't add much value. While most production systems default to INFO, you can go up to WARN or even ERROR if you only care about logs when the system is experiencing issues and dynamically increase log verbisity on demand when needed.

Step 3 — Sample your logs

Incorporating log sampling is a strategy that deserves greater attention in the industry, particularly as a proactive measure rather than a reactive solution to escalating costs.

Since it involves selectively recording a percentage of chatty log data to reduce volume, it can be challenging to implement in environments accustomed to retaining all logs. The difficulty lies in shifting the established culture and practices where logs are used for purposes that may be better served by other means, like metrics or traces.

When implemented at a stage where logging costs are not yet a concern, it allows for a smoother transition and less resistance to change. It also reduces the likelihood of encountering entrenched practices that make the adoption of sampling a prolonged and frustrating process for all parties involved.

An increasing amount of logging frameworks are incorporating simple built-in sampling tools that you can use to get started. If your needs become more complex, designing a custom sampling strategy, like varying rates for different log levels, message types, or based on the presence of a certain field can be highly effective.

Learn more: How to Reduce Logging Costs with Sampling

Step 4 — Configure an appropriate log retention period

In many cloud-based logging systems, logs are often stored for a default period (such as 7 days) without incurring additional storage costs. Retaining logs beyond this default period often leads to increased costs.

To avoid this, assess the actual utility of your logs over time and adjust the retention period to the minimum necessary duration. This adjustment can lead to immediate cost reductions.

You can also adapt your retention strategy based on current needs. For instance, extending the retention period might be beneficial around major feature releases . Conversely, during periods of low activity consider shortening the retention period.

Expiring your logs doesn't necessarily mean you have to throw them all away permanently though. It's usually important to maintain access to certain logs for future needs, such as for investigating past incidents or for compliance/auditing reasons.

Therefore, consider archiving expired logs in cost-effective storage solutions like S3 buckets (or similar). These solutions are much cheaper than keeping them on the primary logging platform.

Usually, you only need to create an IAM role and an S3 bucket in your cloud provider. Then, use the archival tools available in your logging platform to specify the bucket and provide necessary access credentials. This setup ensures that you can access and re-ingest logs if needed later, without incurring high costs.

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Step 5 — Rate limit your error logs

During outages, database or API downtime, or other erroneous states, a surge in error messages may occur in your system leading to repetitive error logs that offer limited additional value often with expensive stack traces.

One way to guard against this is to rate-limit how often a given logger will log the same error. This not only reduces the volume of unnecessary logs during an outage but also improves application performance and ensures that costs remain predictable even during system failures.

You can do this by implementing a mechanism that triggers when a specific error message exceeds a predefined threshold. Once this limit is reached, the system ceases to log further instances of that error. This way, you'll get the essential error information without overwhelming the system (and increasing your costs) with redundant data during high-error periods!

The ideal way to rate-limit error logs is to use a framework that supports such features (either built-in or as a plugin) since it means the log will not be generated in the first place, but if this is not possible a log collector can also help you filter out redundant log data effectively.

Step 6 — Set up alerting to detect logging spikes

To prevent spikes in log-related costs, you should monitor the volume of logs and set up alerting to help you detect issues before they result in significant charges.

Here's a general approach to setting up these alerts in cloud logging environments:

  1. First, you must ensure that current volume of logs being processed is within your budgetary constraints.

  2. Next, access the section of your logging platform where logs-based metrics are displayed.

  3. Create or locate a system-defined metric related to your log ingestion volume.

  4. Use the available options to create an alarm for this metric and configure the notification channels.

Step 7 — Switch vendors

Reducing logging costs is sometimes most effective when you reassess your choice of logging vendors. This involves considering whether your current vendor's offerings align with your actual usage and needs, and if not, exploring alternatives that could offer better value.

When choosing a vendor, be aware of the potential for vendor lock-in where you might feel dependent on a single vendor's services. This can limit your flexibility and potentially lead to higher costs in the future.

Better Stack is a cloud-based log management tool that comes with advanced features for parsing, analyzing, filtering, and visualizing logs, handling large data volumes for faster insights. It supports with many log collectors and application environments with detailed integration guides available.

Key features include live tailing, customizable dashboards, real-time monitoring, and incident management. Better Stack also offers custom alert setups, allowing you to receive notifications through your preferred channels when anomalies are detected. Despite the advanced functionality, it remains user-friendly and cost-effective, with plans starting at only $25/month.

Final thoughts

In this article, we covered 7 strategies to optimize and reduce your logging costs. We hope these tips will assist you in achieving more efficient and cost-effective logging practices.

Thanks for reading, and happy logging!

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