In modern distributed systems, logging is a cornerstone of observability, enabling engineers and SREs to monitor system behavior, debug issues, and ensure reliability.

However, the exponential growth of data from logs—especially in microservices and cloud-native architectures—has made logging an increasingly expensive endeavor. Inefficient log management often leads to ballooning costs, overwhelmed storage, and difficulty extracting actionable insights.

Logs are critical, but not all logs hold equal value. Capturing every debug message or verbose output without discrimination results in cluttered dashboards and unnecessary expenses. The challenge lies in striking a balance: retaining essential logs for observability while reducing unnecessary overhead.

This blog unfolds some of th most crucial actionable strategies to optimize logging costs. From implementing smarter filtering and indexing practices to leveraging cost-efficient storage solutions, these techniques will help you manage your logging setup effectively without compromising on performance or system visibility.

Whether you’re managing Kubernetes logs, scaling distributed systems, or refining your DevOps practices, these strategies are designed to address the unique challenges faced by SREs and engineers. Let’s get started with factors influencing logging costs.

Understanding what drives logging costs is the first step toward optimizing them. Here are the key factors that contribute to logging expenses and why they matter:

1. Log Volume

Log volume means the total amount of data generated by logs, including application events, infrastructure metrics, and debug messages.

• Impact on Costs:

Larger log volumes increase ingestion, processing, and storage expenses. Excessive verbosity, such as debug-level logs in production, can quickly inflate costs.

2. Log Retention Period

It means the duration logs are stored for querying and analysis.

• Impact on Costs: Longer retention periods significantly increase storage costs, especially for high-frequency logs.

3. Log Indexing

It means the process of structuring and organizing logs for quick search and retrieval.

• Impact on Costs: Indexing all logs, including low-value ones, can lead to unnecessary processing and storage overhead.

4. High Cardinality Data

It means logs with numerous unique label combinations, such as user IDs, IPs, or timestamps.

• Impact on Costs: High cardinality data increases processing complexity, query time, and storage requirements.

5. Storage Tier

It means that the type of storage used for logs ranges from high-speed, high-cost hot storage to slower, cost-effective cold storage.

• Impact on Costs: Keeping all logs in expensive hot storage can drain budgets unnecessarily.

Why Addressing These Factors Matters

By identifying and optimizing these cost drivers, you can significantly reduce logging expenses while maintaining an effective observability framework. Addressing log volume, retention, indexing, and storage tiers ensures you’re not paying for redundant or unnecessary data, helping your team focus on meaningful insights.

In the next section, we’ll explore actionable strategies to optimize these factors and manage logging costs more effectively.

Reducing logging costs requires a combination of smarter log management, efficient routing, and leveraging tools to minimize unnecessary expenses.

Below are actionable strategies to optimize your logging setup without compromising on visibility:

1. Use Observability Pipelines

Observability pipelines act as an intermediary layer to preprocess and filter logs before sending them to storage or analysis tools.

• Key Benefits:
  • Filter out redundant or low-value logs.
  • Route high-priority logs to expensive storage (e.g., hot storage) while archiving less important logs in cheaper tiers.
  • Convert raw logs into metrics or summaries to reduce ingestion volumes.
• Tools to Consider:
  • Vector.dev
  • Fluentd

Example: Datadog Observability Pipelines

Read this article, “Understanding Observability Pipelines - A Practical Guide.” To know more about observability pipelines.

2. Optimizing Log Volume by Splitting Logs by Level and Use Case

Image Source

Managing log volume effectively is a critical strategy for reducing logging costs. Not all logs are equally important, and splitting them based on their level (e.g., debug, info, warn, error) and use case allows you to focus on high-priority logs while minimizing costs for less critical ones.

1. Split Logs by Level

Logs are typically categorized into levels, each serving a distinct purpose:

• Debug Logs: Detailed information for developers, primarily used during development and testing.
• Info Logs: General operational logs for routine activities.
• Warn Logs: Indicators of potential issues that require attention but aren’t critical.
• Error Logs: Critical issues that need immediate action.

By categorizing logs at their source, you can apply different handling and storage strategies to optimize costs.

2. Route Logs to Different Destinations

Not all logs need the same level of storage or search priority. Use log routing to send logs to destinations based on their importance and frequency of use:

• High-Priority Logs (e.g., Errors and Warnings):
  • Store these in hot storage for real-time access and quick retrieval.
  • Use for active monitoring, alerting, and immediate debugging.
• Low-Priority Logs (e.g., Debug and Info):
  • Send these to cold storage or archive solutions for occasional use.
  • These logs can be compressed or stored in less expensive storage tiers.
• Example Use Case:
  • Route warning and error log to Elasticsearch for fast querying.
  • Store debug logs in AWS S3 or a similar cost-effective solution for archival purposes.

3. Match Search Priority to Log Use

Logs accessed frequently should be stored in high-performance solutions, while infrequently accessed logs can be archived:

• Critical Logs: Enable fast search capabilities for immediate troubleshooting.
• Rarely Accessed Logs: Archive with extended retrieval times to save on costs.

Benefits of Log Splitting and Routing

• Cost Efficiency: Avoids paying premium storage costs for low-value logs.
• Improved Performance: Reduces clutter in high-performance storage, making searches faster.
• Scalability: Frees up resources to handle high-priority logs efficiently as systems scale.

By splitting logs by level and directing them to appropriate destinations, you can optimize your logging infrastructure while maintaining essential system visibility and functionality.

Read more about log optimization with this article, “Log Cost Optimization”.

4. Convert Logs into Custom Metrics

Image Source: Example of creating metrics from an analytics search by selecting the “Generate new metric” option from the Export menu in the Datadog.

Transforming large volumes of repetitive logs into custom metrics is an effective way to reduce storage costs and simplify observability.

Logs that lack unique identifiers or add little value individually can often be aggregated into meaningful metrics, reducing the need to store every single log entry.

How It Works

• Instead of storing millions of identical or repetitive log lines, aggregate them into a single metric with low-cardinality labels.
• These metrics represent summarized data points, such as counts, averages, or occurrences over time, which provide actionable insights without retaining unnecessary details.

Example Scenario

• Original Logs:
• Imagine a system generates 1 million log lines tracking a specific event (e.g., HTTP 200 responses) with no unique identifiers like user IDs or timestamps.
• After Conversion:
  • These logs are converted into a single custom metric, such as:

• This metric now tracks the total count of these events without retaining every redundant log entry.

Benefits of Custom Metrics

• Reduced Log Volume: Aggregating repetitive logs dramatically lowers storage costs.
• Improved Query Performance: Metrics are easier and faster to query than processing raw logs.
• Cost Efficiency: Storing a single metric with low-cardinality labels is significantly cheaper than retaining millions of redundant logs.

Best Practices for Implementing Custom Metrics

1. Identify Aggregatable Logs:
   • Focus on logs with repetitive patterns or low-value details.
2. Define Low-Cardinality Labels:
   • Use labels like status, endpoint, or method to summarize data without creating unnecessary uniqueness.
3. Leverage Observability Tools:
   • Use tools like Prometheus or OpenTelemetry to define and store metrics derived from logs.

By focusing on aggregated data instead of storing redundant log lines, you can significantly optimize your logging setup for both performance and cost efficiency.Also, read more about Prometheus here!Want to know more about cardinality? Read this article, “How to manage high cardinality in metrics”.

‍5. Implement GitHub Actions for Log Review

Image Source

One of the most proactive strategies to reduce logging costs is addressing excessive log generation at the source.

By integrating GitHub Actions into your CI/CD pipeline, you can automatically review if the code is triggering any high-volume logs before deploying it to production.

How It Works

GitHub Actions can be configured to:

1. Analyze code changes during pull requests to identify logging patterns.
2. Flag excessive or unnecessary logging, such as:
   • Debug logs left in production-ready code.
   • Verbose logging within tight loops or frequently accessed endpoints.
3. Provide actionable feedback to developers to adjust logging levels or remove redundant log statements.

Steps to Implement This Strategy

1. Define Logging Thresholds
   • Set thresholds for acceptable log volume or log levels (e.g., limit debug logs in production).
   • Define rules for logging practices in your project, such as avoiding unnecessary logging in loops or sensitive operations.
2. Configure GitHub Action
   • Use GitHub Actions to create a workflow that runs log analysis tools on new code commits.
   • Example tools:
     • Custom scripts for analyzing log statements.
     • Linting tools or static analysis tools tailored for your programming language.
3. Automate Feedback
   • The workflow should flag problematic code and provide suggestions for improvement.
   • Example: A log within a loop generating thousands of entries can be flagged, prompting the developer to review and optimize.
4. Approval Gates
   • Add a mandatory review step for pull requests with flagged logging issues to ensure they are addressed before merging.

Benefits of GitHub Actions for Log Review

• Proactive Cost Management: Prevents unnecessary log volume from reaching production, reducing ingestion and storage costs.
• Improved Code Quality: Encourages developers to follow best practices for logging.
• Time Efficiency: Automates the review process, reducing manual intervention.
• Smarter Deployments: Ensures only optimized logging patterns are deployed to production environments.

By implementing GitHub Actions to review high-volume logs, you can enforce better logging practices and significantly reduce unnecessary logging costs before they escalate in production.

Want to know more about GitHub actions for log review? Click here!