Why Do Observability Platforms Even Exist?

At a crucial time when enterprises grapple with the complexities of AI systems and their observability, Middleware, a full-stack cloud observability platform, asserts that enterprise-level companies typically invest rather heavily in observability. According to estimates, these companies spend approximately 30% of their infrastructure budget, roughly between $500,000 and $1 million annually, on this. 

Busting a few myths and explaining why an on-prem infrastructure would require observability, Sawaram Suthar, founding director of Middleware, highlighted that open-source tools come with hidden costs. 

“Many assume that open-source tools are a cost-effective solution, but the reality is far more nuanced. While the initial download may be free, its true costs lie in the significant investments of time, resources, and expertise required for implementation, customisation, and ongoing maintenance,” he told AIM. 

Suthar added that this can be particularly daunting in the context of on-prem infrastructure, underscoring the critical need for comprehensive observability to ensure seamless operations and maximise ROI. 

The Evolution of Modern Observability

Deploying AI models is just the starting point. To maximise their value, we need to understand how they perform in real-world scenarios. 

“This requires a deeper level of visibility into their performance, including metrics like API calls, model accuracy, and resource utilisation. By gaining this insight, we can optimise our AI systems for better efficiency, cost-effectiveness, and overall performance,” Param Teraiya, AI team lead at Middleware, told AIM. 

The misconception about observability platforms being unnecessary for on-premise solutions is quickly dispelled by modern realities. 

“When it comes to on-prem solutions, they are already using their own infrastructure. Even if you are deploying it on-prem, you would be required to measure the relevancy of the answers you would get out of the models and see that your model is not hallucinating,” said Teraiya, emphasising the universal need for robust monitoring.

In addressing cost challenges, Middleware has made a strategic decision to opt for Llama over ChatGPT. “Every observability platform struggles with cost, complexity, cardinality, and signal-to-noise ratio. We want to operate at a scale where we also manage the cost,” notes Param. 

The decision reflects a deeper understanding of scaling challenges in production environments. The traditional approach of using legacy observability tools like New Relic has become prohibitively expensive. 

Implementing and maintaining open-source observability tools requires significant expertise and effort. Middleware addresses this challenge by offering managed services that reduce costs while providing robust functionality.

AI is transforming observability by automating tasks that were previously manual and time-consuming. For example, Middleware uses AI to develop tools like Query Genie, which allows users to query infrastructure data using natural language instead of complex filters or database queries. 

Teraiya elaborated on this innovation: “Imagine asking your infrastructure questions like you would a colleague. That’s the reality we’re creating with Query Genie.” This approach not only simplifies the user experience but also accelerates problem resolution. 

Meanwhile, AI is also being used for predictive analytics. Middleware is working on features that can forecast potential issues before they occur. For instance, instead of alerting when CPU usage exceeds 80%, their platform can predict hours in advance when a file system will reach critical capacity.

The Shift to Cloud-Native Architectures

The industry is undergoing a significant shift toward cloud-native architectures. Gartner predicts that by 2025, 95% of systems will adopt cloud-native approaches. Middleware has embraced this trend by building a cloud-agnostic platform based on open standards like OpenTelemetry.

“Whether you use AWS, Azure, or GCP, we just take OpenTelemetry data, which is common for all clouds,” Suthar said, emphasising the importance of flexibility. This approach ensures compatibility across different cloud providers while simplifying implementation for customers. 

However, challenges remain despite these advancements. 

One major issue here is the “black box” nature of many AI systems. Teraiya acknowledged this problem and stressed the need for explainable AI: “We always wanted to identify how [a model] predicts or gives a particular output.” Transparency is crucial for building trust in AI-driven observability platforms. 

Another challenge is scalability. As data volumes grow exponentially, observability platforms must evolve to efficiently handle petabytes of information. Middleware addresses this by using machine learning algorithms for anomaly detection and optimising resource utilisation through intelligent data pipelines.

“We recognise that excessive data collection can be a costly burden. That’s why we’re empowering our customers to take control of their data, filtering out noise and focusing on what truly matters. By doing so, they can reduce their observability costs by up to 80% and shift from a ‘collect everything’ approach to a targeted, efficient monitoring strategy that drives real value,” Suthar concluded.

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