10 critical observability cost factors

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Observability costs are a big concern for organizations. What are the main factors that make it so expensive?

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Torsten Volk | Managing Research Director | EMA

Torsten Volk is Managing Research Director (Artificial Intelligence, Machine Learning, DevOps, Containers, and FaaS) with Enterprise Management Associates (EMA), a leading industry analyst and consulting firm that specializes in going “beyond the surface” to provide deep insight across the full spectrum of IT and data management technologies. Currently, his research interests evolve around cloud native applications and machine learning, with a special focus on observability, compliance, and generative AI.


This excerpt is from the recent EMA Research Report “Observability: Challenges, Priorities, Adoption Patterns, and Solutions” lists the top ten reasons your observability costs are on the rise and why they are impacting your overall business. 

In the realm of observability for cloud native applications and platforms, the total cost of ownership is influenced by a variety of factors ranging from technology to business models.

  1. Tool and platform costs: The acquisition and operation of observability tools can be a significant cost factor. These costs can include the initial purchase price, implementation fees, and ongoing maintenance or update expenses.
  2. Subscription and usage metrics: Subscription fees often vary based on different types of usage metrics like data volume or number of monitored hosts. This may make costs variable and potentially unpredictable, depending on usage patterns.
  3. User base: A larger number of end users necessitates more training and potentially more software licenses. More users usually mean increased costs in training, licensing, and possibly in user support.
  4. Resource utilization: Processing and storing telemetry data require significant computational and storage resources. The cost associated with CPU, memory, storage, and networking needs to be factored into the overall expenses.
  5. System and cloud costs: The size and complexity of the monitored systems, as well as any additional cloud costs, also factor into the total cost of ownership. The larger and more complex the system, the more expensive it is to monitor; additionally, cloud hosting fees can add to the expense.
  6. Data management: Increased data volume and limitations on data ingestion and retention can raise costs. Techniques like data filtering and compression, as well as managing retention policies, can affect the cost structure.
  7. Compliance and security: Protecting confidential telemetry data requires additional resources, which can inflate costs. Ensuring data encryption, access controls, and compliance with regulatory standards can add layers of expense.
  8. Pricing models and billing: Different vendors have varied pricing and billing models, adding complexity to cost calculations. Whether it’s usage-based or quantity-based pricing, the chosen model can have a large impact on cost predictability.
  9. Integration complexity: Costs can escalate when integrating new observability platforms with existing systems and software stacks. The more complex the existing ecosystem, the more resources will be needed for successful integration.
  10. Vendor quality: The level and quality of support and service from vendors can impact the overall costs. Poor customer service or lack of adequate support can result in hidden costs due to fines and reputation loss incurred through application downtime.Understanding and navigating these 10 factors with added context is crucial for managing the cost and effectiveness of an observability strategy in a cloud native environment.
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The observability cost trap

The cost of observability for modern cloud applications has escalated, leading to situations in which observability cost is higher than the cost of the respective cloud resources themselves. This observability cost includes: 

  1. 5x more observability users 
  2. 1,000x higher data resolution 
  3. 100x more entities to monitor 
  4. 20x more releases 
  5. 10x more tools 
  6. 100x more data 
  7. 50%-100% data overlap 
  8. 500x increase in data transfer 
  9. 5x increase in observability infrastructure
  10. 5x management overhead resulting from requirements for tool integration, data integration, and complex pricing models.

We will explain each one of these key cost factors with the chart below.

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You can read the full EMA Research Report “Observability: Challenges, Priorities, Adoption Patterns, and Solutions” here.

About EMA

Founded in 1996, Enterprise Management Associates (EMA) is a leading IT analyst research firm that specializes in going “beyond the surface” to provide deep insight across the full spectrum  of IT management technologies.

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