Why Traditional Observability Tools Fall Short for GenAI
By Summer Lambert | | 2 min read

Traditional observability tools are ill-equipped to handle the specifics of AI applications, necessitating specialized solutions like Graphsignal.

As AI steadily embeds itself into the fabric of modern applications, the challenges of overseeing such complex systems grow exponentially. For years, traditional observability tools have been the standard for monitoring the performance and behavior of software applications. These tools typically excel at tasks like tracking HTTP requests, evaluating server health, or providing simple analytics. However, the application development landscape is changing, and these traditional tools are finding it increasingly difficult to keep up with the unique demands of AI-powered applications.

What exactly is the issue with the current observability solutions? At a fundamental level, traditional tools cannot dig deep into the AI-specific metrics that are becoming crucial in today's tech ecosystem. When an application relies on LLMs to function, performance metrics extend far beyond CPU usage or data throughput. We must consider chains of prompts and completions, and other AI-related factors to assess and ensure the success of user interactions. These are insights that traditional observability tools were not designed to track.

Beyond metrics, the complexity of AI applications creates interconnected dependencies between the AI model and other application components. As a result, pinpointing the problem becomes a herculean task when something goes awry. This added layer of complexity is often more than what traditional observability tools can handle, leaving developers in the dark when they need insights the most. Furthermore, the cost of running AI models in production is an essential factor that traditional observability tools must address. AI operations are expensive, and without proper insight into cost management, businesses can find themselves quickly running over budget.

Given these limitations, Graphsignal positions itself as a specialized solution tailored to meet the demands of AI-powered applications. One of its defining features is AI-native observability. Unlike generic tools, Graphsignal understands the unique challenges that AI applications bring and offers specialized monitoring capabilities to address them. Moreover, Graphsignal supports various popular AI frameworks and libraries, providing unparalleled flexibility. This broad integration allows developers to use the tools they are most comfortable with rather than adapting to their observability solution's limitations. Cost tracking, another standout feature, will enable developers to monitor and manage the often exorbitant costs of running AI in production. Lastly, Graphsignal promotes a collaborative framework that allows real-time sharing of insights, breaking down the traditional silos that can stymie effective team problem-solving.

In summary, as AI becomes integral to modern applications, the tools we use to observe and manage these systems must evolve. Though useful for more straightforward applications, traditional observability tools must catch up in our new, complex landscape. It's high time to adopt more innovative, specialized solutions like Graphsignal, designed explicitly for the challenges and opportunities of AI integration.