Best AIOps Tools for Prometheus

Find and compare the best AIOps tools for Prometheus in 2025

Use the comparison tool below to compare the top AIOps tools for Prometheus on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    New Relic Reviews
    Top Pick
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    Transform your business operations with New Relic's AIOps solutions, featuring a robust Incident Management software that offers a holistic approach to swiftly identifying, addressing, and resolving incidents. Tailored for large-scale enterprises, our integrated data platform consolidates telemetry data from your entire software ecosystem, equipping you with powerful full-stack analysis tools to quickly pinpoint issues and their underlying causes. With real-time monitoring, automated notifications, and flexible workflows, New Relic empowers teams to optimize incident response strategies, reduce downtime, and uphold service reliability. Enhance your incident resolution efficiency, foster team collaboration, and deliver exceptional customer experiences through New Relic's AIOps-enabled Incident Management features.
  • 2
    Edge Delta Reviews

    Edge Delta

    Edge Delta

    $0.20 per GB
    Edge Delta is a new way to do observability. We are the only provider that processes your data as it's created and gives DevOps, platform engineers and SRE teams the freedom to route it anywhere. As a result, customers can make observability costs predictable, surface the most useful insights, and shape your data however they need. Our primary differentiator is our distributed architecture. We are the only observability provider that pushes data processing upstream to the infrastructure level, enabling users to process their logs and metrics as soon as they’re created at the source. Data processing includes: * Shaping, enriching, and filtering data * Creating log analytics * Distilling metrics libraries into the most useful data * Detecting anomalies and triggering alerts We combine our distributed approach with a column-oriented backend to help users store and analyze massive data volumes without impacting performance or cost. By using Edge Delta, customers can reduce observability costs without sacrificing visibility. Additionally, they can surface insights and trigger alerts before data leaves their environment.
  • 3
    Sedai Reviews

    Sedai

    Sedai

    $10 per month
    Sedai intelligently finds resources, analyzes traffic patterns and learns metric performance. This allows you to manage your production environments continuously without any manual thresholds or human intervention. Sedai's Discovery engine uses an agentless approach to automatically identify everything in your production environments. It intelligently prioritizes your monitoring information. All your cloud accounts are on the same platform. All of your cloud resources can be viewed in one place. Connect your APM tools. Sedai will identify and select the most important metrics. Machine learning intelligently sets thresholds. Sedai is able to see all the changes in your environment. You can view updates and changes and control how the platform manages resources. Sedai's Decision engine makes use of ML to analyze and comprehend data at large scale to simplify the chaos.
  • 4
    Selector Analytics Reviews
    Selector’s software-as-a-service leverages machine learning and natural language processing to deliver self-service analytics that facilitate immediate access to actionable insights, significantly decreasing mean time to resolution (MTTR) by as much as 90%. This innovative Selector Analytics platform harnesses artificial intelligence and machine learning to perform three critical functions, equipping network, cloud, and application operators with valuable insights. It gathers a wide array of data—including configurations, alerts, metrics, events, and logs—from diverse and disparate data sources. For instance, Selector Analytics can extract data from router logs, device performance metrics, or configurations of devices within the network. Upon gathering this information, the system normalizes, filters, clusters, and correlates the data using predefined workflows to generate actionable insights. Subsequently, Selector Analytics employs machine learning-driven data analytics to evaluate metrics and events, enabling automated detection of anomalies. In doing so, it ensures that operators can swiftly identify and address issues, enhancing overall operational efficiency. This comprehensive approach not only streamlines data processing but also empowers organizations to make informed decisions based on real-time analytics.
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