Top 9 Observability Platforms for LLMs: Unlocking Advanced Monitoring for AI Systems

As with any ‘traditional’ software application, observability is a key success factor when you’re integrating AI into your systems. AI powered applications have created a new tech stack, including more unpredictable APIs as well as vector databases and (data) orchestration frameworks. This shift in tech stack warrants a new look at observability. In this article we will first highlight the conventional aspects of observability and then explain the additional steps you need to monitor your AI application. We finish by taking a look at which open source and proprietary observability tools are there to choose from.

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What is observability?

In simple terms, observability means that we can see why an application is slow or broken. Or, in fancy words; the ability to understand a system's state based on it’s outputs, the telemetry data collected. Developers should be able to ask arbitrary questions about their application, even ones that were not anticipated, even after their application has been deployed already.

So, what’s the difference between monitoring and observability? Observability goes a level deeper then monitoring because we want to find out why our system behaves the way it does. We would like to find out the root cause of the problem, rather than simple monitoring it’s behaviour.

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What’s different in LLM Observability?

An application using LLMs behaves partly the same as a regular software application, but it adds a level of complexity. Mainly because a LLMs are by nature unpredictable. AI models are often a black box, where we can’t really look inside to see what’s happening. Although output can be controlled and tweaked a little bit, we can’t make any assumptions about it. Furthermore, in many AI applications, the inputs into an LLM can vary widely as well, as prompts are often generated by users or other LLMs.

So, in addition to ‘traditional’ observability, we have to add some specific telemetry for LLMs. We will have to look at the inputs and outputs and compare them with a baseline or with other benchmarks done in the past. This way, we can deduce where errors have arisen, quickly trace back where our root cause is located and see if model responses are deviating from a baseline or behave unexpectedly (like accuracy and hallucinations).

Another important aspect is to monitor costs, as these are also harder to predict than in traditional systems, especially when multiple LLMs are combined or when used in an agentic setup.

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“According to the Elastic 2024 Observability Report, 69% of organizations struggle to handle the data volume generated by AI systems, making observability essential for managing complexity and costs” (galileo)

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We can summarise the ‘traditional’ main aspects of observability as follows:

Comprehensive data collection: Gathering metrics, logs, traces, and events from all components of a software system. This includes measurements of costs for our external api calls.
Real-time monitoring: Continuously tracking system performance and behavior to detect issues as they occur
Root cause analysis: Quickly identifying the source of problems in complex, distributed systems

Note: there is definitively more to it, but these are the most important.

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For LLMs we note the following aspects of observability:

LLM Metrics and evalution: LLM Metrics and evaluation: Measuring LLM output quality through key metrics like accuracy, precision, recall, and F1 score. It also includes monitoring hallucinations of our models.
Retrieval performance (RAG) Retrieval performance in LLM observability focuses on evaluating the effectiveness of the retrieval component in Retrieval Augmented Generation (RAG) systems, assessing metrics like context relevance, recall, and precision

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Best practices in LLM observability

To effectively monitor AI systems, it's important to have a well-thought-out plan. One key aspect is creating a feedback loop that allows for ongoing improvements. This means regularly updating AI models based on how they perform, ensuring they remain flexible and effective. It's also crucial to select the right performance metrics and set appropriate alert thresholds. These metrics should be meaningful and align with the organization's goals, focusing monitoring efforts on the most important aspects of system performance and behavior.

As AI systems become more complex and handle larger amounts of data, it's vital to have observability solutions that can scale and adapt. This ensures that organizations can continue to effectively monitor their AI systems as they grow. Additionally, promoting a culture of observability within the organization is important. This involves training teams to understand and use observability data, which can greatly improve the success of these monitoring practices.

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Tools for LLM observability

There are various paid and open source tools available for us to choose from. Some, like Datadog and Traceloop are built on already existing observability tools and expanded into LLM observability. Considerations for choosing the best tool are:

Your existing observability platform. If your existing tool already provides AI observability, there is a good case to be made to explore that. Or check out if your existing monitoring can easily integrate with the observability tool.
Costs Paid solutions can quickly raise costs, especially when we’re having to trace a large-scale, multi-LLM application. On the other hand, with open source solutions, we have to take hosting, development and uptime into consideration.
Data Visualization: Utilize visualization features to represent data trends and anomalies, making it easier to interpret complex information
Alerting capabilities: The tool should have capabilities for setting up real-time alerts to monitor performance thresholds
Cost analysis: Consider tools that provide token usage tracking and cost breakdowns, especially for resource-intensive LLM applications
Language and sdk support It matters which language your using now and how you’d like to integrate observability into your tech stack

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Paid observability platforms

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1. Eden AI Observability & Monitoring Tools

Summary: A comprehensive platform designed to enhance the performance, transparency, and reliability of AI systems, with advanced observability and monitoring tools.
Features:

Real-Time Monitoring: Track response times, error rates, and resource utilization in real-time to ensure smooth AI operations.
Anomaly Detection: Identify and address anomalies early to prevent disruptions and maintain trust in AI deployments.
Centralized Dashboards: Access a unified, intuitive view of your AI system’s health and performance.
Multi-Model and Multi-Provider Compatibility: Monitor diverse AI models across multiple platforms, ensuring seamless integration.
Log Tracing and Detailed Analytics: Dive deeper into system behavior with comprehensive logs and analytics for effective issue resolution.
Customizable Alerts: Set specific thresholds and receive real-time alerts to stay ahead of potential problems and maintain optimal performance.

Eden AI aims to simplify AI monitoring and observability, helping businesses optimize efficiency, build trust, and ensure accountability in their AI operations.

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