Top 7 AI Tools To Use for DevOps Teams in 2025

Quick Summary

Introduction

Imagine if your DevOps teams could spend less time on routine tasks, and focus more on other, high-priority and creativity-oriented tasks. Sounds great, right? With AI, that is already being achieved by many companies worldwide!

With some specific AI tools, the whole DevOps landscape is witnessing a transformation. The AI tools are changing the way DevOps teams operate by automating repetitive tasks, speeding up processes, and identifying issues before they become major problems.

In fact, A recent study even shows that 60% of companies that use AI in their development process are able to deliver projects faster and experience fewer bugs. So, it is clear that with the right AI tools for DevOps, teams can boost efficiency, improve software quality, and deliver greater value to the business.

Having said that, let’s have a look at seven such tools in detail.

Top 7 AI Tools for DevOps Teams

Here’s a detailed breakdown of the seven key AI tools that DevOps teams can use for better software development and delivery.

1. GitHub Copilot

Developed by GitHub and OpenAI, GitHub Copilot is an AI-powered virtual assistant for developers. When developers start typing any piece of code, this tool suggests more code lines or even whole functions based on their current work. These suggestions help developers complete their code on time.

What are the benefits of this tool?

• Supports multiple languages: Provides support for Python, JavaScript, Java, and many such languages.
• Improves code quality: Helps reduce common mistakes with smarter code suggestions.
• Boosts productivity: Assists in completing time-consuming tasks fast so developers can focus on solving more important problems.

This tool is ideal for:

Developers who want to code faster without impacting the quality, especially those working in teams with tight deadlines.

2. AIOps by Splunk

Splunk’s AIOps platform uses machine learning to monitor IT systems. It collects and analyzes data in real time to help DevOps teams detect any unusual activity, predict possible failures (like system outages), and suggest ways to fix issues before they get worse.

What are the benefits of this tool?

• Early problem detection: Quickly finds unusual patterns that may lead to system failure.
• Faster incident response: Provides insights for teams to act in case of an incident, saving user experience from getting impacted.
• Improves uptime: Reduces the chances of system failure by predicting issues before they occur.

This tool is ideal for:

IT operations and DevOps teams managing large, complex environments where system health and uptime are critical.

3. Dynatrace

Dynatrace is an AI-powered monitoring tool that gives DevOps teams full visibility into their applications, infrastructure, and user experience. It uses its built-in Davis AI engine to automatically track system performance, detect issues, and even find the root cause of a problem.

What are the benefits of this tool?

• Real-time insights: Provides a live overview of the health of systems and applications.
• Quick troubleshooting: Automatically finds and explains the root cause of issues.
• User experience tracking: Helps improve customer satisfaction by monitoring frontend performance.

This tool is ideal for:

DevOps teams and SREs (Site Reliability Engineers) working in large-scale environments where fast issue detection, deep visibility, and high user satisfaction are important.

4. Harness

Harness is an AI-powered platform that can automate many parts of the continuous delivery (CD) process. This means your team can push new features or updates to production without having to follow all the manual steps usually involved. This tool can also easily integrate with popular CI/CD tools like Jenkins, GitHub Actions, and more, making the release process smoother from start to finish.

What are the benefits of this tool?

• Safe deployments: Harness automatically checks if your release is performing as expected. If not, it stops the rollout and reverses the changes.
• Faster releases: By reducing the manual steps in your pipeline, it speeds up how often and how safely you can deploy code.
• Built-in verification: It tracks performance and user impact during and after each deployment.

This tool is ideal for:

DevOps teams, release engineers, and developers who manage CI/CD pipelines and want risk-free, frequent deployments.

5. DataDog

DataDog is an AI tool that uses machine learning to look for patterns and identify odd behaviour across different components of the DevOps Lifecycle. For example, if your application suddenly starts responding slowly, DataDog can show whether it is due to high traffic, a database issue, or a failing microservice. It does this by collecting real-time data from all your systems and services and providing a visual dashboard to see how everything is working.

What are the benefits of this tool?

• Unified monitoring: Monitors infrastructure and applications and logs them in one platform, reducing the need to switch between tools.
• Anomaly detection: Uses AI to identify strange or unexpected system behavior before it impacts users.
• Improved collaboration: Teams can easily share insights, alerts, and reports to work together efficiently.

This tool is ideal for:

Tech teams that want a centralized view of their infrastructure and faster issue detection.

6. MLOps by Google Cloud

Google Cloud’s MLOps platform is designed to help teams manage the full machine learning (ML) lifecycle. From building models to deploying them and tracking their performance in real-world environments, this tool covers it all. It offers built-in tools for version control, testing, and continuous monitoring, so your models stay accurate and useful even after they’re live.

This platform works well with Google Cloud services like Vertex AI, but also supports integration with DevOps tools. That means your ML workflows can fit smoothly into your existing CI/CD pipelines.

What are the benefits of this tool?

• Model management: This tool keeps track of every version of your ML models, so you are aware of the production environment and can confidently roll back or update any version.
• Automates workflows: Helps automate model training, testing, and deployment, saving time and reducing manual involvement.
• Built-in monitoring: Constantly checks how your ML models are performing in real time and alerts you in case something goes wrong.

This tool is ideal for:

Teams using ML models who want better control and automation in production.

7. JFrog Xray

JFrog Xray is actually an AI-based DevSecOps tool that is designed to keep your software supply chain secure. Using AI and threat intelligence, it will scan your codebase, container images, and open-source dependencies to find any security risks in your code before it goes into production.

What are the benefits of this tool?

• Automated compliance: Helps ensure your software follows internal policies and industry regulations.
• Smooth CI/CD integration: Works directly with other DevOps tools like Jenkins, GitHub Actions, and GitLab to integrate security into your delivery process.
• Detailed impact analysis: Shows how a vulnerability could affect your application, helping teams prioritize what to fix, based on impact analysis.

This tool is ideal for:

DevOps and security teams that want to embed security into every stage of development, reduce risk from open-source code, and maintain full visibility over what goes into their software.

Conclusion