Best AIOps Tools for GitHub
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Compare the Top AIOps Tools that integrate with GitHub as of August 2025
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This a list of AIOps tools that integrate with GitHub. Use the filters on the left to add additional filters for products that have integrations with GitHub. View the products that work with GitHub in the table below.
What are AIOps Tools for GitHub?
AIOps tools combine artificial intelligence and machine learning to enhance IT operations by automating the detection and resolution of issues in complex IT environments. They analyze massive amounts of data from various sources—like logs, metrics, and events—to identify anomalies and predict potential outages before they impact business. These tools enable proactive incident management, reduce noise by correlating alerts, and provide actionable insights for faster troubleshooting. AIOps supports continuous monitoring and root cause analysis, helping teams improve system reliability and performance. By integrating with existing IT infrastructure and tools, AIOps enhances operational efficiency and enables smarter decision-making. Compare and read user reviews of the best AIOps tools for GitHub currently available using the table below. This list is updated regularly.
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1
Datadog
Datadog
Datadog is the monitoring, security and analytics platform for developers, IT operations teams, security engineers and business users in the cloud age. Our SaaS platform integrates and automates infrastructure monitoring, application performance monitoring and log management to provide unified, real-time observability of our customers' entire technology stack. Datadog is used by organizations of all sizes and across a wide range of industries to enable digital transformation and cloud migration, drive collaboration among development, operations, security and business teams, accelerate time to market for applications, reduce time to problem resolution, secure applications and infrastructure, understand user behavior and track key business metrics.Starting Price: $15.00/host/month -
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Sedai
Sedai
Sedai is an autonomous cloud management platform powered by AI/ML delivering continuous optimization for cloud operations teams to maximize cloud cost savings, performance and availability at scale. Sedai enables teams to shift from static rules and threshold-based automation to modern ML-based autonomous operations. Using Sedai, organizations can reduce cloud cost by up to 50%, improve performance by up to 75%, reduce failed customer interactions (FCIs) by 75% and multiply SRE productivity by up to 6X for their modern applications. Sedai can perform work equivalent to a team of cloud engineers working behind the scenes to optimize resources and remediate issues, so organizations can focus on innovation.Starting Price: $10 per month -
3
Seerene
Seerene
Seerene’s Digital Engineering Platform is a software analytics and process mining technology that analyzes and visualizes the software development processes in your company. It reveals weaknesses and turns your organization into a well-oiled machine, delivering software efficiently, cost-effectively, quickly, and with the highest quality. Seerene provides decision-makers with the information needed to actively drive their organization towards 360° software excellence. Reveal code that frequently contains defects and kills developer productivity. Reveal lighthouse teams and transfer their best-practice processes across the entire workforce. Reveal defect risks in release candidates with a holistic X-ray of code, development hotspots and tests. Reveal features with a mismatch between invested developer time und created user value. Reveal code that is never executed by end-users and produces unnecessary maintenance costs. -
4
Selector Analytics
Selector
Selector’s software-as-a-service employs machine learning and NLP-driven, self-serve analytics to provide instant access to actionable insights and reduce MTTR by up to 90%. Selector Analytics uses artificial intelligence and machine learning to conduct three essential functions and provide actionable insights to network, cloud, and application operators. Selector Analytics collects any data (including configurations, alerts, metrics, events, and logs), from various heterogeneous data sources. For example, Selector Analytics may harvest data from router logs, device or network metrics, or device configurations. Once collected, Selector Analytics normalizes, filters, clusters, and correlates metrics, events, and alarms using pre-built workflows to draw actionable insights. Selector Analytics then uses machine learning-based data analytics to compare metrics and events and conduct automated anomaly detection. -
5
Synergy
Unframe
Synergy is an AI-native command center for enterprise IT operations that unifies siloed monitoring, ticketing, logging, and documentation into a single pane of glass. It continuously correlates signals across tools like Splunk, New Relic, Jira, ServiceNow, and Confluence to turn alert storms into clear, prioritized insights. Synergy’s Smart Incident Workflows automate routine tasks, suggest next steps, flag ownership gaps, and accelerate resolution to cut mean time to detection and repair. Its proactive monitoring detects risks before traditional alerts trigger, flags error spikes and missed escalations, recognizes emerging patterns, and answers investigative queries in natural language. Built-in root cause analysis traces incidents end-to-end across time, logs, metrics, tickets, and post-mortems, links to similar events for instant context, and generates concise summaries.
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