Best AIOps Tools for GitHub

Datadog is the cloud-age monitoring, security, and analytics platform for developers, IT operation teams, security engineers, and business users. Our SaaS platform integrates monitoring of infrastructure, application performance monitoring, and log management to provide unified and real-time monitoring of all our customers' technology stacks. Datadog is used by companies of all sizes and in many industries to enable digital transformation, cloud migration, collaboration among development, operations and security teams, accelerate time-to-market for applications, reduce the time it takes to solve problems, secure applications and infrastructure and understand user behavior to track key business metrics.

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.

Seerene’s Digital Engineering Platform offers advanced software analytics and process mining capabilities that scrutinize and visualize your company’s software development workflows. By identifying inefficiencies, this platform transforms your organization into a streamlined entity, enabling software delivery that is not only efficient and cost-effective but also rapid and of superior quality. It equips leaders with the insights necessary to steer their teams towards achieving comprehensive software excellence. The platform can uncover code segments that are prone to defects, adversely affecting developer efficiency, and identify high-performing teams, allowing their exemplary processes to be adopted organization-wide. Additionally, it highlights potential defect risks in release candidates through a thorough examination of code, development hotspots, and testing methodologies. It also brings to light features where there is a discrepancy between the time invested by developers and the value delivered to users, as well as code that remains unused by end-users, which incurs unnecessary maintenance expenditure. Ultimately, Seerene empowers organizations to optimize their software development lifecycle and enhance overall productivity.

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.

Synergy serves as an AI-driven command center designed for enterprise IT operations, consolidating fragmented monitoring, ticketing, logging, and documentation into a cohesive interface. By continuously integrating data from tools such as Splunk, New Relic, Jira, ServiceNow, and Confluence, it transforms overwhelming alert storms into well-organized, prioritized insights. Its Smart Incident Workflows streamline routine processes, recommend subsequent actions, identify ownership gaps, and expedite resolutions, thereby reducing the average time for detection and repair. Additionally, Synergy’s proactive monitoring capabilities identify potential risks ahead of conventional alerts, highlight error surges and missed escalations, detect emerging trends, and respond to investigative inquiries using natural language. Furthermore, its integrated root cause analysis tracks incidents comprehensively across timelines, logs, metrics, tickets, and post-mortem evaluations, connecting to related events for immediate context and producing succinct summaries to aid in understanding. Overall, Synergy enhances operational efficiency and effectiveness for IT teams, ensuring they remain ahead of potential issues.