6 techniques to reduce cloud observability cost

Cloud observability is really important for most modern organizations in that it dives deep when it comes down to keeping application functionality, problems, and those little bumps in the road along the way, a smooth overall user experience. Meanwhile, the growing toll of telemetry data that keeps piling up, such as logs, metrics and traces, becomes costlier by the minute. But one thing is clear: You do not have to compromise visibility just to reduce costs.  

This post is all about strategies and top practices that can help optimize your cloud observability spend for you to derive value from your monitoring investments without breaking the bank.  

Recognizing the drivers of cost: The observability  

Before going into solutions, let’s understand what could actually make observability costs skyrocket.  

Data ingestion volume. This is one of the biggest causes of costs. The more logs, metrics and traces you ingest, the more expensive it gets. This includes data from applications, infrastructure, networks and third-party services.

Data retention. Keeping huge historical data for long periods makes costs higher.

High cardinality metrics. Metrics with many unique labels or dimensions can translate to an explosion in data points and storage requirements.

Overcollection. Collecting data is never used in real-time for monitoring or alerting, or for analysis purposes.

Tool sprawl. Using different observability tools that do not connect creates duplications in data ingestion and managing those overheads.

Lack of cost awareness. Teams provisioning resources without understanding the financial implications of their observability choices.  

Key techniques to reduce cloud observability cost  

Now, let’s explore actionable techniques to bring your observability costs under control:  

1. Optimize data ingestion at the sources  

This one is perhaps the most impactful area in which we can reduce costs. Make certain that only the data that really counts is collected.  

Filter and whitelist the data:

Logs. Aggressive filtering must be applied at the source to get rid of debug logs, information that is not useful or data coming in from non-critical services. Several observability platforms allow you to filter logs before ingesting them.

Metrics. Focus on those metrics that impact how the application performs, user experience and utilization of resources (such as application response times, CPU/memory usage, error rates). Dismiss low-value or unused metrics.

Traces. Focus on business-critical transactions and distributed traces that help the organization understand service dependencies.  

Strategic sampling. For high-volume data streams (especially traces and logs), consider some intelligent sampling methods that will allow you to capture a statistically significant subset of data, thus reducing volume while still allowing for anomaly detection and trend analysis.

Scrape intervals. Consider the periodicity of metric collection. Do you really need to scrape metrics every 10 seconds, when every 60 seconds would have been enough to get a view of this service? Adjusting these intervals can greatly reduce the number of data points.

Data transformation rules. Transform raw data into a more compact and efficient format before ingesting it. This may involve parsing logs to extract only relevant fields and ignoring the rest.

Compression techniques: Most observability platforms incorporate certain compression techniques that considerably minimize the volume of data for storage.  

2. Intelligent data retention policies  

The retention of data is an enormously expensive affair. Therefore, create a pathway to tiered storage with intelligent data retention policies.  

Short-term or long-term storage. High granularity of data needs to be retained for smaller time periods (7-30 days for detailed troubleshooting), and on the other extreme, older data needs to be archived and stored long-term (in S3 or Glacier for compliance and historical analysis) if it is comparatively accessed less.

Retention by type of data. Not all the data can fit into the same retention period. Some data, such as application logs for immediate debugging, may need only a few days, while others, such as audit logs, could require several years of retention.

Archiving/deletion on autopilot. Automate based on the retention policies defined to archive or delete data accordingly.   

3. Right-sizing and resource optimization  

Observability tools help you identify inefficiencies in your cloud infrastructure, leading to cost savings.  

Find idle and unutilized resources. Observability data can help you find idle or underutilized resources (EC2 instances, databases, load balancers, etc.) that should be stopped or right-sized.

Autoscaling. Utilize autoscaling to automatically scale the compute capacity proportional to demand so that you pay only for what you actually use. This removes over-allocation of resources in the low usage times.

Spot instance/savings plans/reserved instance. For predictable workloads, check out the discounts offered by the cloud providers in the form of Reserved Instances or Savings Plans. For the fault-tolerant and interruptible workloads, Spot instances offer a considerable discount.

Storage optimizations. Optimize using different classes of storage (e.g., S3 Standard, S3 Intelligent-Tiering, S3 Glacier) driven by data access patterns and retention requirements.  

4. Decentralized and distributed observability   

Consider strategies that reduce reliance on a single, expensive observability platform for all data:   

Open-source solutions (self-hosting). Organizations with expertise can consider self-hosting open-source tools like Grafana, Prometheus, Loki and Jaeger to save costs that go only into infrastructure. Do keep in mind the operational overhead. 

Mixed-mode approaches. Use commercial observability platforms like Middleware, DataDog, etc., for mission-critical applications and rely less on open source or native cloud logging solutions for some other less critical data or use cases. 

Native cloud observability tools. Use the monitoring/logging services provided by your cloud provider (e.g., AWS CloudWatch, Google Cloud Monitoring, Azure Monitor). These are usually the least expensive options for ingesting and storing basic telemetry.   

5. Foster a FinOps and cost-conscious culture  

Cost optimization of observability is not only a technical challenge but a cultural one.  

Education for the teams. Train developers and operations teams about the cost implications of the observability choices they make. Set a ‘cost-aware’ development culture.

Set budgets and alerts. Set a clear budget for observability expenditures and create alerts when teams approach or exceed that budget.

Cost allocation and chargeback. Tagging and labeling should be put in place so observability costs can be fairly charged to teams, projects or business units. This creates accountability.

Conduct regular reviews of observability spending. Review the observability spending regularly. Troubleshoot high-cost areas, analyze the usage patterns and search for other optimization opportunities. Cost management dashboard tools can be really helpful here.   

6. Utilization of AI and machine learning  

More cost optimization is performed with the help of AI and ML:   

Anomaly detection. Identify any strange spikes in terms of data ingestion or resource utilization, which might indicate inefficiency or misconfiguration.

Predictive analytics. Allow observability requirements and costs to be predicted on the basis of historical trends, enabling subsequent proactive optimization.

Automated remediation: Some platforms can automate actions (e.g., reduce resources) based on detected anomalies, which will help eliminate more wastage.  

As far as judging cloud observability is concerned, the quintessential question arises if there must be money spent without limit. Organizations can reduce cloud observability costs by strategically optimizing data ingestion and effectively managing retention. And embracing automation while ensuring that the level of visibility supported is optimal for maintaining resilient and high-performing cloud environments. The absolute necessity is that one must be proactive, analytical and forever seeking to fine-tune one’s observability approach in sync with operational requirements, as well as budget constraints.  

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