Modern tech stacks require a new approach to observability that enables continuous monitoring and optimization of performance metrics. This approach streamlines operations, reduces costs, and improves customer experience by speeding up issue resolution.
High observability costs have emerged as a significant concern for businesses across industries, such as the staggering $65 million Observability bill and similar eye-opening figures from other industry giants, all stark reminders of the financial burden imposed in managing and analyzing vast volumes of observability data.
However, amid the seemingly daunting challenge of managing observability costs, there are a number of strategic opportunities to be explored. In this blog, we delve into five strategies aimed at mitigating the financial burden associated with Observability solutions. To start, let’s examine the primary reasons behind the soaring costs of observability.
Reasons for Rising Observability Costs
Data Volume and Complexity: The rise of cloud-native technologies, microservices, and CI/CD pipelines has significantly amplified the volume of data produced by modern infrastructures. As a result, organizations need to analyze extensive amounts of data to effectively monitor, observe, analyze and enhance their application and infrastructure performance. This has led to increased storage, processing, and operational costs for SaaS observability vendors, which impacts their customers as well. Additionally, organizations face network egress fees to transfer data to these providers for analysis.
Observability Vendor Pricing Models: SaaS observability vendors commonly lock customers into annual contracts with hefty markups to offset their cloud operational costs for hosting and managing customer data. Additionally, some vendors use usage-based pricing models, which can lead to dramatic cost increases during high data production periods like seasonal peaks or detailed troubleshooting phases. As a result, many organizations struggle with unexpected overage charges due to the lack of control over data consumption.
Scalability and Customer Growth: As organizations scale their operations to meet the growing demands of their customers, they often encounter scalability and performance limitations with their existing observability solutions. In these cases, scaling up infrastructure and compute resources to accommodate increasing data volumes and user loads can lead to escalating costs.
Tool Proliferation: Organizations have acquired a multitude of observability tools and platforms to monitor different aspects of their environment. However, most observability platforms either excel or just focus on one area of observability, such as metrics, logs, traces, and events. Managing multiple tools has resulted in cumulative expenses ranging from different licenses, tool administration costs, costly integration efforts to unify data across platforms, and investments in training and skill development for engineering teams.
Long-Term Retention Policies: When it comes to Real User Monitoring or Security Observability, higher data retention rates are a common practice. Longer retention is also prominent in regulated industries, such as Financial Services and Healthcare, where years of data is needed for compliance reasons. Often, in the fear of not losing data, organizations end up keeping too much data, which can lead to additional costs.
See also: Smart Talk Episode 7: Cardinality, Control and Costs in Observability
Solutions for Managing Observability Costs
Consolidate Your Observability Solutions
To reduce the complexity and cost, both from a licensing/pricing standpoint and from a manual integration and engineering standpoint, organizations are considering consolidating their observability tools.
Standardizing on a unified platform that provides comprehensive monitoring and troubleshooting capabilities across metrics, logs, traces, and events simplifies management, lowers licensing costs, and enhances engineering productivity.
Self-Host, Own the Savings
SaaS vendors often employ inflexible pricing models and charge for overages. Increased data volumes during ingest or intensive query analysis for diagnosis and troubleshooting can lead to significant spikes in costs. Additionally, network egress fees for transferring data from hosted applications and infrastructures to SaaS observability solutions further increase expenses.
In contrast, hosting observability platforms within Virtual Private Clouds (VPCs) allows organizations to gain control over their observability costs and resources while avoiding data transfer fees. In a VPC deployment, a two-tier architecture is used: a Data Plane for collecting and processing observability data and a Control Plane that oversees the “volume-to-cost” ratio. This setup allows organizations to adjust data ingestion and retention policies and modify infrastructure resources based on usage patterns, performance needs, and the level of detail required for thorough root cause analysis.
Invest in Open Source, The Right Way
Many organizations have learned valuable lessons from getting locked into proprietary tools. To stay adaptable, organizations should look for future-proof architectures. This includes open-source alternatives like OpenTelemetry (OTel), agent-free methods such as eBPF, and open query languages like PromQL, LogQL, TraceQL, GraphQL, and SQL for root cause analysis.
Although open-source solutions can cut licensing costs, they come with expenses for hosting, scaling, backup, and recovery and often lack support.
A better approach is to use a platform that integrates with open-source tools and provides robust management capabilities for cloud hosting, resource allocation, cost optimization, advanced features, and premium support beyond what basic open-source solutions offer.
Shape Your Data, Slash Your Costs
To manage the high volumes of high-cardinality data and mitigate associated costs, organizations should leverage advanced data shaping techniques. These techniques include comprehensive cardinality analysis for metrics, logs, and traces, which helps identify high-cardinality data early in the ingestion process. Techniques such as data aggregation, dynamic filtering, and mapping can convert high-cardinality data into lower-cardinality formats. This process eliminates irrelevant data points, reduces storage needs, improves query performance, and ultimately leads to significant cost savings.
Refine Retention and Storage Footprint
Observability solutions with fine-grained retention policies are able to classify data based on importance, lifespan, and compliance requirements. For instance, highly critical data (e.g., trace data from frequently accessed services like a payment service) will be retained for a longer period, while less crucial data (e.g., informational logs lacking errors or warnings) will be discarded. This approach ensures observability expenses are minimized by retaining only the most pertinent data.
Additionally, implementing deduplication and compression techniques on observability data can significantly reduce storage requirements, thereby lowering storage costs.
See also:
Closing Thoughts
In the current environment of complex tech stacks and cloud-native architectures, it’s common for organizations to use multiple monitoring tools, often averaging around ten. Recent surveys show that 74% of organizations plan to implement a unified observability platform within the next year to address this fragmentation.
Ultimately, why not take control of the system that observes all your other systems? By adopting unified observability platforms deployed in a VPC, organizations equip their entire engineering team—including site reliability engineers, DevOps teams, and infrastructure and cloud engineers—with tools that enable continuous monitoring and optimization of performance metrics. This approach streamlines operations, reduces costs, and improves customer experience by speeding up issue resolution.
References:
Dimensional Research. (2024). The 2024 Observability Landscape, A Survey of Observability Decision Makers. Retrieved from https://www.elastic.co/pdf/dimensional-research-elastic-the-2024-observability-landscape.pdf
