As businesses increasingly adopt cloud-native and distributed systems, the demand for observability tools has surged. Observability is essential for monitoring system health, diagnosing issues, and ensuring optimal performance, but it comes at a cost—often a significant one.

With the ever-growing volumes of telemetry data, these costs can quickly spiral out of control, straining budgets and operational efficiency. The scale of data being generated is staggering. By 2025, an estimated 100 zettabytes of data will be stored in the cloud, and data volumes are growing at an average rate of 63% per month in many companies.

This rapid expansion presents a double-edged sword: while observability tools are crucial for navigating this complexity, managing their costs has become a pressing challenge for organizations.

Are you someone who is searching for answers to these questions? Then you are at the right place.

In this blog, we will explore actionable strategies to optimize observability expenses without compromising system reliability or performance. By implementing smarter data management practices, selecting the right tools, and leveraging automation, you can achieve a balance between comprehensive observability and cost efficiency.

Also Read :List Of Top 8 Observability Pipeline tool

Without a strategic approach, expenses tied to metrics, logs, and traces can quickly spiral out of control, straining operational budgets.

In this section, we’ll explore practical strategies to optimize observability expenses without compromising on the quality of insights or system reliability. These approaches focus on smarter data management, efficient tool configurations, and cost-effective storage solutions.

Let’s get into these actionable strategies to achieve a balance between comprehensive observability and budget optimization.

1. Implementing an Observability Pipeline

One of the most effective strategies to reduce observability costs is to implement an observability pipeline. This intermediary layer enables businesses to manage and optimize the flow of telemetry data before it reaches storage or monitoring tools.

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By controlling the data at this stage, organizations can significantly reduce unnecessary expenses.

Example: Datadog Observability Pipeline

Key Benefits of an Observability Pipeline:

• Filter and Control Data:

An observability pipeline allows you to decide which data to keep and which to discard. By filtering out irrelevant or low-value data, you can reduce the volume of telemetry data processed and stored, cutting down associated costs.

• No Code Changes Required:

With an observability pipeline, you can make adjustments to data collection and routing without modifying application code. This flexibility ensures minimal disruption to your workflows while allowing for quick optimization.

• Reroute Data as Needed:

Observability pipelines provide the ability to reroute telemetry data to different tools based on specific use cases. For example, critical metrics can be sent to high-priority monitoring tools, while less important logs can be stored in more cost-effective solutions.

How It Helps Reduce Costs:

• Eliminates the storage and processing of redundant or low-priority data.
• Enables dynamic data routing, ensuring you only pay for necessary processing.
• Prevents overloading expensive observability tools with excessive data.

Tools to Consider:

• Vector: A high-performance observability pipeline that enables filtering, transformation, and routing of logs, metrics, and traces.

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• Fluentd or Logstash: Popular open-source tools for building observability pipelines with customizable filters and data routing.

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By implementing an observability pipeline, organizations can take control of their data flow, streamline observability costs, and ensure their tools remain efficient and scalable.

This approach strikes a balance between maintaining comprehensive observability and optimizing budget allocation.

To learn more about observability pipelines, read “ Understanding Observability Pipelines - A Practical Guide”.

2. Reducing Metric Cardinality

Metric cardinality refers to the number of unique combinations of labels or dimensions associated with a metric.

While tracking high-cardinality metrics can provide detailed insights into system behavior, it can also significantly increase observability costs due to the sheer volume of data generated and stored.

For instance, if a metric descriptor includes labels for zone and color, and there are two distinct values for zone and three distinct values for color, the metric would have a cardinality of six. This is because there are six unique combinations of the label values, as illustrated in the diagram.

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Why High Cardinality Increases Costs:

• Exponential Growth in Data Points: Each additional label or dimension multiplies the number of metrics to be processed, stored, and queried.
• Resource Overheads: High-cardinality metrics require more storage, compute power, and query time, driving up costs.
• Tool Overloading: Observability tools can become inefficient when handling large volumes of high-cardinality data, leading to degraded performance.

Strategies for Reducing Metric Cardinality:

1. Evaluate and Prioritize Metrics:
   • Identify critical metrics that are essential for your monitoring goals.
   • Deprioritize or eliminate metrics with low operational value to reduce data volume.
2. Aggregate Data Where Possible:
   • Instead of tracking individual metrics for every instance or service, aggregate them at higher levels (e.g., cluster-level metrics instead of per-node metrics).
   • Use summary metrics to capture essential trends without tracking granular data points.
3. Limit Label Combinations:
   • Review and reduce the number of labels or dimensions applied to each metric.
   • Avoid overly specific labels, such as unique user IDs or timestamps, which can create excessive cardinality.
4. Use Sampling Techniques:
   • Apply sampling to metrics that don’t require real-time or high-resolution monitoring.
   • This approach retains valuable insights while lowering data ingestion and storage costs.

How It Helps Reduce Costs:

• Decreases the volume of data ingested and stored in observability tools.
• Improves the performance and efficiency of metric queries.
• Reduces unnecessary spending on computing and storage resources.

Tools for Metric Management:

• Prometheus: Offers mechanisms like aggregation and label-based filtering to manage metric cardinality effectively.

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• VictoriaMetrics: Optimized for handling high-cardinality metrics with efficient storage and querying.

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By actively reducing metric cardinality, organizations can achieve a balance between detailed observability and cost efficiency, ensuring that only the most valuable metrics are tracked and stored.

To learn more about what cardinality is, read this article: What is Cardinality? Cardinality Metrics for Monitoring and Observability

3. Improving Log Levels

Logs are a critical part of observability, providing detailed insights into system behavior and events.

However, excessive logging or inappropriate log levels can lead to ballooning costs and storage inefficiencies. Optimizing log levels and managing log storage tiers can help reduce observability costs without compromising visibility.

Strategies for Improving Log Levels:

1. Restricting Info Logs in GitHub Workflows:
   • Info logs, while useful for understanding general system behavior, can quickly become overwhelming and unnecessary in production environments.
   • Use GitHub workflows or CI/CD pipelines to enforce restrictions on info-level logs during the development and deployment processes.
     • For example, set up automated checks to ensure that only critical or debug logs are enabled in specific environments.
     • Allow developers to configure logs dynamically based on the needs of staging or production environments.
2. Implementing Log Storage Tiers:
3. Splitting logs into different storage tiers (hot, warm, cold) based on their relevance and frequency of access can significantly reduce costs:
4. • Hot Storage: For logs that need real-time access and quick query times (e.g., active incidents or recent events).
   • Warm Storage: For less frequently accessed logs that still need moderate query performance (e.g., logs from the past week).
   • Cold Storage: For historical logs that are rarely accessed but must be retained for compliance or audit purposes. This could include archival storage solutions like Amazon S3 Glacier.
5. By categorizing logs into these tiers, organizations can optimize costs while maintaining access to critical information when needed.

Benefits of Improving Log Levels:

• Reduces storage costs by minimizing unnecessary or redundant logs.
• Optimizes query performance by limiting excessive log volumes in active storage.
• Ensures compliance and long-term retention requirements are met without overspending.

Tools and Techniques for Log Management:

• Loki or ElasticSearch: Enable fine-grained control over log levels and storage configurations.

Image SourceWant to see this in action? Check out this video for a quick walkthrough!"

• GitHub Actions: Automate log-level restrictions during development pipelines.
• Cloud Storage Solutions: Use tiered storage options like AWS S3 or Google Cloud Storage for cost-efficient log archiving.

By improving log levels and strategically managing storage tiers, organizations can strike a balance between effective logging and cost control, ensuring observability remains both comprehensive and budget-friendly.

Wondering what’s Log Monitoring? Read this article!

4. Tracing Configuration

Tracing is a critical component of observability, providing visibility into the flow of requests across distributed systems. However, without proper configuration, tracing can become a significant cost driver. Optimizing your tracing setup through tagging and provider negotiations can help reduce these costs while maintaining effective observability.

You can;

• Set Appropriate Sampling Rates: Adjust sampling configurations to collect traces for only critical transactions or systems instead of capturing all data.
• Optimize Tagging: Limit the number of tags in your traces to essential fields that are meaningful for debugging or analysis.
• Use Adaptive Sampling: Implement dynamic sampling to increase trace collection during outages or high-impact events while reducing it during normal operations.
• Filter Unnecessary Data: Exclude low-value spans or metadata from your trace collection to streamline storage and reduce costs.

Benefits:

• Align provider costs with your organization’s usage patterns.
• Offers flexibility to scale tracing configurations as needed.
• Ensures fair pricing, particularly for large-scale deployments.

Cost Impact: By minimizing the volume of collected and stored trace data, businesses save on both data storage and compute costs associated with querying and analyzing traces.

Recommended Tools for Tracing Configuration:

• Jaeger or Zipkin: Provide advanced tagging capabilities for trace filtering.

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Read more here: Documentation

• Signoz: Allows custom trace configurations and integrates tagging options seamlessly.

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You can read more here: Documentation

• Uptrace: Supports team-based tagging and trace optimization features.

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Read more here: Documentation

5. Tagging by Team

Tagging metrics, logs, and traces by team ownership is an effective strategy for optimizing costs and enhancing accountability. This approach allows teams to better manage their observability budgets and identify areas for improvement.

How can you do this? Here’s how;

• Create Team-Specific Tags: Assign tags to observability data, such as team=backend or team=frontend, to track which teams are generating the most logs or metrics.
• Enable Cost Monitoring: Use tagging to correlate data volume and associated costs with specific teams. This transparency helps teams understand their observability usage and identify cost-saving opportunities.
• Promote Ownership: Encourage teams to regularly review and optimize their observability data, including reducing redundant logs or metrics.
• Foster Collaboration: Use tagged data to facilitate discussions between teams and ensure observability costs are aligned with overall business goals.

Cost Impact: Tagging enables better tracking and management of observability expenses, encouraging teams to reduce unnecessary data generation and optimize resource utilization.

Suitable Tools:

• Datadog for tagging metrics, traces, and logs by team.

Image Source: Leverage unified service tagging to easily correlate infrastructure metrics with trace metrics and logs in a single dashboard.

For more Read: Documentation

GitHub: https://github.com/datadog

• Prometheus with custom labels for team-specific data.

Also Read, “**How to Add Target-Specific Labels in Prometheus - A Guide”**

GitHub: https://github.com/prometheus/prometheus

• Grafana Loki for team-tagged log monitoring.

Image Source: An Example of Metrics Query

Also Read, “Open source log monitoring: The concise guide to Grafana Loki” to know more about it.

6. Negotiations with Your Providers

Partnering with your observability providers strategically can lead to significant cost reductions. Engaging in proactive discussions about pricing, usage limits, and custom agreements can help you align observability tools with your budget.

You can do this by;

• Review Your Current Contract: Regularly assess your usage patterns and compare them against your existing contract to identify unused or underutilized features.
• Negotiate Volume Discounts: If your organization handles a high volume of observability data, ask for discounts based on usage tiers or commit to a longer-term contract for better pricing.
• Explore Bundled Services: Some providers offer cost-effective bundles for metrics, logs, and traces instead of separate services.
• Request Custom Plans: Work with providers to create tailored pricing models that reflect your specific needs and ensure you only pay for what you use.
• Leverage Competition: Use quotes from other providers as leverage to negotiate better rates or additional benefits.

Cost Impact: Strategic negotiations lead to lower rates, customized pricing, and discounts, significantly reducing overall observability expenses without compromising on capabilities.

These strategies not only help reduce observability costs but also ensure your monitoring setup remains efficient, scalable, and aligned with business goals.