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GenAI Observability Vendor Landscape – 2024

The report outlines the fundamental concepts necessary for understanding the GenAI observability market. It then identifies the relevant tools and vendors in this space. Finally, the report ranks these vendors on AIM Research’s Penetration and Maturity (PeMa) Quadrant.

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Report on Vendors and Tools for monitoring and observability of LLM-powered Generative AI applications:

The goal of this report is to explore the emerging landscape of tools and vendors that are focused on addressing the challenges of monitoring and observing Generative AI (GenAI) infused applications.

AIM Research defines GenAI Observability as the practice of monitoring, analyzing, and visualizing the internal workings of artificial intelligence, specifically generative models like Large Language Models (LLMs). In our analysis of the current market offerings for observability of LLM-powered generative AI applications, we found that vendor capabilities primarily focus on logs and analytics, evaluation, observation, security guardrails, and cost optimization. While a few of the vendors offer tools for end-to-end observability of GenAI applications, the majority of organizations offer tools for multiple components in the GenAI application stack.

After the launch of OpenAI’s GPT, we saw 2023 as the year of experimentation for LLMs. Now, in 2024, organizations are moving beyond experimentation and bringing LLM-powered GenAI applications into production. With increased usage and integration, the need for observability becomes pronounced.

 

Key Findings:

  • Focus Shifts from AI Observability to GenAI Observability: Several leading players in the AI Observability market, including Dynatrace, Datadog, and New Relic, have expanded their offerings to include observability capabilities tailored for GenAI-infused applications, addressing the specific needs of this emerging field.

 

  • End-to-End Observability: There is a growing trend towards providing end-to-end observability for GenAI systems, covering the entire model lifecycle. This includes monitoring not only the model’s performance but also data quality, training processes (pre-production), and infrastructure. By offering a holistic view, observability tools help identify issues at any stage and enable more effective troubleshooting and optimization.

 

  • Geographical Distribution of Vendors: About 80% of the GenAI Observability tool providers are headquartered in the United States, indicating a strong concentration of innovation and development in this region.


  • There is rise in pure play GenAI Observability Startups: The Generative AI ecosystem experienced significant growth between 2022 and 2023. Due to the emergence of LLMs such as OpenAI’s ChatGPT, there is increased demand for tools that could help manage and optimize AI applications. This created a ripe opportunity for startups to address the evolving needs of the LLMOps (LLM Operations) market.

 

  • Open-source and Free-to-Use Observability Tools: We have noticed organizations offering observability tools on different structures, such as open-source, free (for experimentation with limited features), growth plan (for production), and enterprise plan (for scale-up or customised features). For organizations looking to cut costs, vendors offering open-source and free-to-use tools may become a viable option.

 

Table of Contents:

  1. Executive Summary
  2. Key Findings
  3. Introduction to GenAI Observability
    1. Introduction
    2. Need for Observability 
    3. Identifying High-Need Areas for Observability in the GenAI Application Framework
    4. Categorization of Tools
  4. Deep Dive Into GenAI Observability Tools and VendorsPeMa Quadrant
    1. Logging Analytics
    2. Evaluation
    3. Observe/Real-time Monitoring of Application
    4. Security Guardrails
    5. Cost Optimization
    6. Outlook of GenAI Observability Vendor Landscape
    7. Open-source and Free-to-Use GenAI/LLM Observability Tools
  5. PeMa Quadrant
  6. List of Vendors
  7. Glossary
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