Top 6 AI Observability Tools - Hindustan Times

Top 6 AI Observability Tools

Published on Apr 23, 2024 06:38 PM IST

Discover the best AI-enhanced observability tools for monitoring, analyzing, and improving AI system performance to ensure real-time reliability and efficiency

Top 6 AI Observability Tools
Top 6 AI Observability Tools
ByHT Brand Studio

When we say AI Observability, there are two points of view

1. Observability for AI systems and models

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2. Observability tools that use AI to make their services better

Here, we concentrate mostly on the second point of view. However, the first point of view is considered one of the use cases of the second point of view—AI Observability Tools that can observe and monitor AI systems and models that generate massive amounts of data.

Coming back to the crux of the article -

Why do you need AI Observability?

Observability is complex in itself. Exploding volumes of data and the massive adoption of distributed microservice architectures that involve many moving components and dependencies have made it even more complex. In addition to the complexity, it has also become a cost-efficiency issue.

As a solution, AI-enhanced observability tools, or AI observability in short, are taking the foreground.

These tools offer insights into the system's inner workings by analyzing outputs with the aid of AI. By analyzing outputs, they offer unparalleled insights into the intricacies of distributed and dynamic systems. Their strength lies in their ability to dissect these complex environments at a granular level.

Beyond this, they give a cohesive and comprehensive view of infrastructures, especially those that use modern hybrid and multi-cloud architectures. This holistic view ensures that every component, every output no matter how small or dispersed, is accounted for and understood in the broader context of the system's overall health and performance.

Dealing With Large Volumes

Using AI's advanced analytics capabilities, ML-based clustering algorithms, and LLMs' text-reading capabilities, these AI observability tools can sift through the vast, often overwhelming, amounts of data generated by systems to filter out the noise and focus on what truly matters.

Automated Observability And Predictive Analytics

AI observability goes beyond traditional monitoring by offering automation in analyzing telemetry data at scale. It recognizes patterns and makes meaningful predictions on what to monitor, thus enabling organizations to anticipate and mitigate incidents before they occur. This predictive analytics capability extends to identifying the root causes behind issues and proposing solutions, all in an automated manner.

Advanced Anomaly Detection and Automated Alerting

Another reason why IT infrastructures should move to AI observability tools lies in their automated anomaly detection and alerting capabilities. These tools eliminate the need for manual customization and setup of alerts. Instead, they rely on advanced analytics and pattern recognition. They also use the same to streamline workflows, reduce mean time to detect (MTTD) and mean time to resolution (MTTR), and enhance the real-time detection and diagnosis of issues.

These capabilities, combined with a deep understanding of the context and relevance of incidents emphasizes the importance of moving to AI observability.

Here are 8 such tools driving the scene of AI observability and challenging the status quo set by traditional observability and monitoring platforms. If you're looking to stay ahead in the tech game, go through these modern AI observability tools that are redefining the industry, offering cutting-edge solutions for today's complex IT environments and transform your approach to observability.

Best AI Observability Tools for Optimal Performance

Edge Delta

EdgeDelta is leading the AI-enhanced observability scene from the front. It offers a suite of tools designed to enhance the monitoring and analysis of complex IT environments. By integrating AI and ML into its core, Edge Delta provides real-time anomaly detection, automated insights, and streamlined troubleshooting processes. This platform stands out as an innovator in the field with its ability to analyze data directly at its source to handle massive volumes of data, offering deep insights into distributed systems and a unified view of hybrid and multi-cloud architectures.

When it comes to cost-effectiveness, this at-the-source data processing also gives EdgeDelta the edge and puts it in the top of the table of cost-effective alternatives for big popular names in the observability industry.

Edge Delta's approach to AI observability is not just about monitoring; it's about understanding, predicting, and optimizing IT infrastructure performance. With the innovative use of AI and ML and a special focus on automation and scalability, Edge Delta is the best AI observability tool in 2024.

Key Features

  • Automated Anomaly Detection: Utilizes AI to automatically identify anomalies, eliminating the need for manual threshold setting and enabling proactive monitoring. Reduces MTTR with automated anomaly detection, alerting, and troubleshooting.
  • AI-Assisted Troubleshooting: Oncall AI, the observability copilot, offers conversational summaries of anomalies and other recommendations, accelerating the troubleshooting process.
  • Real-Time Insights: Act on issues quickly and improve system reliability and user satisfaction.
  • Petabyte-Scale Log Search: At-the-source analysis and upstream data processing allow you to do petabyte-scale log search and analysis, ensuring cost-effective observability at scale without compromising data fidelity.
  • Unified Observability: Uses AI and ML for enhanced visibility and context in complex systems.
  • AI-Powered Insights into Log Analysis: Transforms log analysis with AI and ML, offering advanced insights and methods for better decision-making.
  • Streamlined Observability for Data Pipelines: Offers insights into the management of data pipelines, distinguishing Edge Delta from traditional monitoring solutions and highlighting the role of AI in DevSecOps.
  • Extensive Integration and Compatibility, Reduced latency, Enhanced security, and Better resource optimization

Middleware's innovative approach to cloud observability, powered by AI, offers a comprehensive solution for monitoring, understanding, and resolving issues across cloud infrastructures.'s AI-driven capabilities, such as anomaly detection and resolution, are designed to boost the productivity of DevOps teams and allow them to address issues more effectively. By integrating AI-based insights and recommendations, enables faster troubleshooting and fixing hidden bottlenecks, enhancing operational efficiency and customer satisfaction.

With its easy integration, superior monitoring features, and AI-driven insights, is setting a new standard for AI-enhanced cloud observability platforms.

Key Features of

  • AI-Powered Insights:'s AI detection identifies issues across infrastructure and applications, offering detailed recommendations for resolution, and enhancing the speed and accuracy of troubleshooting.
  • Real-Time Monitoring: The platform provides real-time monitoring capabilities, allowing users to track metrics, logs, events, and traces on a comprehensive dashboard, ensuring timely detection of potential issues.
  • Unified View: By consolidating metrics, logs, traces, and events into one timeline, offers a holistic view of the cloud environment, simplifying the monitoring process.
  • Cost Optimization: optimizes observability costs through data compression, reducing expenses and accelerating mean time to detection and resolution.
  • Lightweight Agent: The platform's lightweight agent ensures minimal resource usage while achieving higher efficiency and faster results.
  • Customizable Dashboards: Users can create custom dashboards to focus on the data that matters most to them, enhancing the observability experience.


Splunk, one of the big names in the Observability Industry, offers a suite of AI-powered tools that allow organizations to manage the complexities of modern IT environments more effectively. These tools are revolutionizing cloud security and observability by leveraging artificial intelligence, machine learning, and generative AI to accelerate the observability processes of detection, investigation, and response to incidents. They are designed to sift through vast volumes of data, identifying anomalies and ensuring systems remain operational and secure.

Splunk's AI philosophy emphasizes the responsible use of AI, human-in-the-loop decision-making, and open, extensible models. This approach ensures that AI outcomes are trustworthy and tailored to the unique challenges of security and observability.

By automating tedious operations and providing deep insights into system performance and security threats, Splunk enables businesses to achieve higher productivity, reduce resolution times, and maintain a secure and efficient IT infrastructure.

Key Features of Splunk's AI-Powered Tools

  • Generative AI Solutions: Using natural language prompts and advanced data summarization techniques, these Splunk AI-powered tools seamlessly integrate into security and observability workflows. This integration significantly boosts team productivity and enhances overall operational effectiveness, streamlining processes and improving outcomes.
  • Foundational AI Solutions: They use machine learning to distinguish between critical signals and noise, improving incident detection and problem understanding faster.
  • AI for Observability: These tools use AI and machine learning to improve visibility into complex IT and operational systems and provide better context around incidents that arise.
  • Custom Anomaly Detection: Allows users to detect anomalies in IT operations data using powerful machine learning algorithms, facilitating early detection of business-impacting issues.
  • AI-Driven Security and Observability: Embedded AI capabilities within Splunk's platform support cybersecurity, IT operations, and engineering teams in predicting and preventing incidents before they occur.
  • Digital Resilience: Splunk's AI solutions are geared towards accelerating digital resilience, enabling organizations to deal with disruptions and maintain operational continuity.


Aisera Observability enables businesses to achieve higher productivity, reduce resolution times, and maintain a secure and efficient IT infrastructure.

Through the integration of AI, machine learning, and generative AI technologies, Aisera's AI-powered observability and AI Discovery tools provide a proactive approach to managing and resolving IT incidents, ensuring continuous operations, and preventing outages and revenue loss.

Aisera offers a comprehensive suite of tools designed to automate, predict, and enhance the efficiency of IT infrastructures. These tools offer a unified platform for full-stack visibility across business, cloud, and IT operations, minimizing disruptions by providing observability, prediction, and remediation services.

Aisera is a significant advancement in the field of AIOPS by automating critical IT operations and offering deep insights into system performance and issues.

Key Features of Aisera's AI-Powered Tools

  • AI Observability: Offers a single pane of glass for actionable insights from cross-domain data ingestion and analytics, covering the entire tech stack including network, infrastructure, databases, storage, applications, and business services.
  • Major Incident Detection and Prediction: Uses advanced noise reduction and correlation techniques to find early signs of issues that could lead to performance issues and outages of critical services.
  • Automated AI Discovery: Dynamically discovers applications, devices, and cloud resources on the network, providing continuous visibility into data center and cloud assets, and maintaining an up-to-date CMDB.
  • AI-Driven Discovery: Offers a dynamic mapping of tech stacks, discovering configuration items and their relationships without compromising security, eliminating inefficient manual processes.
  • Automated Root Cause Analysis: Analyzes incidents and telemetry data to pinpoint the root causes of major incidents, leveraging AI to reduce detection and resolution times significantly.


Elastic's AI-powered observability, with its advanced analytics and user-friendly interface, is an efficient way to manage modern, complex IT environments. It integrates AI and machine learning (ML) across its observability and security platforms to offer a comprehensive solution that addresses the vast amounts of telemetry data in modern IT environments. This integration of AI into observability practices not only improves operational efficiency but also supports proactive problem-solving and decision-making.

It is designed to automate anomaly detection, streamline root cause analysis, and enhance overall system performance monitoring. With these features, organizations can reduce mean time to detection (MTTD) and mean time to resolution (MTTR), leading to improved system reliability and user satisfaction.

Key Features of Elastic's AI-Powered Observability Tools

  • Generative AI Integration: Elastic's observability tools leverage generative AI to provide contextual insights and recommendations, enhancing the ability to quickly identify and resolve issues.
  • Comprehensive Data Analysis: By consolidating logs, metrics, traces, and profiling data, Elastic offers a unified view of IT infrastructure, enabling more accurate and actionable insights.
  • AI Assistant for Observability: Elastic introduces an AI Assistant that transforms observability with relevant, context-aware AI-powered insights, making it easier to understand application errors and optimize code efficiency.
  • Advanced Anomaly Detection: Elastic's AI and ML algorithms automate the detection of anomalies across your systems, reducing the time and effort required for manual monitoring and analysis.
  • Interactive Chat Interface: The AI Assistant's interactive chat interface allows teams to query and visualize telemetry data in one place, streamlining the troubleshooting process.
  • Customizable Integration: Elastic supports integration with large language models (LLMs) and proprietary data, enabling tailored observability experiences that meet specific business needs.'s approach to AI-powered observability is a significant leap forward in managing the complexity and volume of data in modern IT environments.

By leveraging machine learning and artificial intelligence, offers a dynamic observability pipeline that evolves with your data, automating the process to provide deeper insights, faster deployment, and significant cost savings. This innovative platform not only reduces the volume of log data by over 80% but also enhances anomaly detection, data enrichment, and compliance, ensuring sensitive data is discovered and protected. stands out for its ability to integrate with over 50 sources and destinations, offering unparalleled control and flexibility over observability data.

Key Features of

  • Data Optimization and Reduction:'s smart summarizer drastically reduces log data volume, cutting observability costs by more than half. This feature ensures that only valuable data is analyzed, optimizing resource usage.
  • Smart Routing: The platform intelligently routes data from any source to any destination, avoiding vendor lock-in and ensuring data is utilized where it has the most value, enhancing overall system efficiency.
  • Anomaly Detection:'s pipeline learns what is normal for your data, identifying anomalies and integrating with common alerting systems for real-time notification, thus lowering the mean time to resolution (MTTR) for critical incidents.
  • Data Enrichment: The platform enriches telemetry data in-stream, adding context to help route and analyze data more effectively. This feature speeds up queries and reduces the computational load on analytics platforms.
  • Searchable Low-Cost Data Lake: enables the creation of a full-fidelity data lake in low-cost storage. This makes data highly compressed, searchable, and retrievable through natural language queries, significantly reducing storage costs.
  • Compliance and Sensitive Data Discovery: The platform proactively detects sensitive and classified data, secures it through obfuscation or hashing, and ensures compliance with privacy regulations.

How To Choose The Right AI Observability Tool?

Choosing the best AI observability tool is crucial for modern IT environments that are complex, distributed, and ever-changing. The ideal tool should not only monitor and alert but also provide deep insights and actionable recommendations to enhance system reliability and performance.

Here’s a comprehensive guide to help you select the best AI observability tool for your organization:

1. Automated Anomaly Detection: Look for a tool that leverages AI to detect anomalies across your systems automatically. This feature should identify unusual behaviors without needing predefined thresholds, enabling you to address issues before they impact your operations proactively. Automated anomaly detection saves time and resources by eliminating manual monitoring efforts.

2. AI-Assisted Troubleshooting With Generative AI and Causal AI: Consider tools that incorporate generative AI and causal AI. These technologies provide a deeper understanding of why problems occur, enabling faster and more accurate problem resolution.

3. Data Standardization and AI Integration: The tool should promote data standardization across the ecosystem and fully integrate AI into IT operations. This is essential for rapidly and reliably detecting patterns and identifying root causes of impaired performance.

4. Hyperautomation: Future-proof your observability strategy by selecting a tool on the path to hyperautomation.

5. Scalability and Cost-Effectiveness: With the exponential growth of data, your chosen tool must handle petabyte-scale log search and analysis without breaking the bank. It should offer cost-effective observability at scale, allowing you to store and search all your data without resorting to sampling or filtering, which can miss critical insights.

6. Advanced Insights into Log Analysis: AI-powered insights into log analysis transform traditional methods by providing deeper understanding and streamlining the identification of root causes. The tool should evolve log analysis with AI and ML, offering advanced insights that aid in better decision-making.

7. Predictive Capabilities: Look for tools that leverage AI not just for visibility but for predictive insights. AI's ability to predict and prevent issues before they arise transforms IT teams from reactive problem solvers to proactive solvers. This improves reliability and performance, customer satisfaction and business continuity.

8. Streamlined Observability for Data Pipelines: Observability extends beyond traditional monitoring. The right tool should provide insights into managing data pipelines, especially in environments with microservices and distributed systems. This includes understanding the pillars of observability—logs, metrics, and tracing—and the indispensable role of tools like OpenTelemetry.

9. Cloud-Native Support: Ensure the tool is optimized for cloud environments. With much of the enterprise data environment transitioning to the cloud, observability must also shift to manage and monitor these distributed systems effectively. The tool should support cloud-native technologies and be capable of providing insights into cloud operations, security, and compliance.

10. Cost Management: The right AI observability tool should also offer capabilities to manage cloud costs effectively by identifying inefficiencies and redundancies. This helps optimize resource utilization and control expenses, ensuring operational excellence and cost-effectiveness.

11. Unified Observability Across Domains: Every good observability tool must provide a comprehensive view of what incident is happening, where, and why. Good AI Observability tools use AI and ML to improve this process, providing end-to-end visibility and context of the incidents even in the most complex distributed microservices systems.

12. Integration and Extensibility: Your AI observability tool should seamlessly integrate with existing systems and workflows. It should be open and extensible, allowing you to bring AI where you need it most.

13. Customer and Community Support: Choose a tool backed by a strong community and customer support. Testimonials from leading engineering teams and case studies can provide insights into the tool’s effectiveness and reliability. Additionally, look for tools recognized by industry analysts for their innovation and performance.

14. Ease of Use and Deployment: The tool should be easy to set up and use, with minimal resource usage. Look for features like one-minute or one-line installation and the ability to collect data across your tech stack with a single script. This ensures higher efficiency and faster results without a significant learning curve.

Choose an AI observability tool that aligns with your enterprise goals: It’s crucial to select a tool that supports your organization's journey toward becoming a fully intelligent enterprise. For instance, if you are concerned about reducing the observability costs or the vast amounts of indexed logs without compromising the performance and security of your system, tools that do stream processing at the edge, like EdgeDelta, might be the right choice. Tools like Elastic might be the right choice if you want to leverage open-source solutions with generative AI assistants to help you diagnose issues.

This list was compiled by ImprovMedia.

Disclaimer: This article is a paid publication and does not have journalistic/editorial involvement of Hindustan Times. Hindustan Times does not endorse/subscribe to the content(s) of the article/advertisement and/or view(s) expressed herein. Hindustan Times shall not in any manner, be responsible and/or liable in any manner whatsoever for all that is stated in the article and/or also with regard to the view(s), opinion(s), announcement(s), declaration(s), affirmation(s) etc., stated/featured in the same.

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