Best AIOps Tools for GitLab

Compare the Top AIOps Tools that integrate with GitLab as of September 2026

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This a list of AIOps tools that integrate with GitLab. Use the filters on the left to add additional filters for products that have integrations with GitLab. View the products that work with GitLab in the table below.

What are AIOps Tools for GitLab?

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 GitLab currently available using the table below. This list is updated regularly.

1

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

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2

Cloudgeni

Cloudgeni

Cloudgeni is an agentic AIOps platform for cloud infrastructure that investigates incidents, drift, compliance, and FinOps issues, then fixes them as validated Infrastructure-as-Code pull requests. It brings cloud state, IaC, and operational signals into one context layer so agents can understand dependencies, identify root causes, generate changes in the organization’s own conventions, validate them before production, and verify that problems are resolved after changes land. Its agents cover compliance remediation, configuration drift and resource import, DevOps work, pull request review, IaC pipelines, cost optimization, SRE workflows, AI infrastructure governance, and custom automation. Cloudgeni connects with AWS, Azure, GCP, OCI, Kubernetes, OpenShift, Terraform, OpenTofu, Terragrunt, Bicep, Pulumi, Helm, GitHub, GitLab, Azure DevOps, security tools, observability platforms, and internal knowledge systems.

Starting Price: $79 per month

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3

NudgeBee

NudgeBee

NudgeBee is an AI Agents and Agentic Workflow platform built for SRE, CloudOps, and DevOps teams. It combines pre-built AI Assistants for incident troubleshooting, cloud cost optimization, and Kubernetes operations with a visual no-code Workflow Builder for custom automation. NudgeBee's AI engine auto-investigates alerts using a live semantic Knowledge Graph, grounded in your actual infrastructure topology. It queries data in place from existing tools (Prometheus, Datadog, Grafana, Loki) with zero data ingestion. The Workflow Builder supports 20+ action categories, native AWS/Azure/GCP CLI nodes, A2A and MCP protocol support, and human-in-the-loop approval gates. 49+ integrations. Enterprise-ready with RBAC, audit trails, BYOM (Bring Your Own Model), and self-hosted deployment. SOC-2 Type II and ISO 27001 compliant.

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4

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.

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5

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.

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