Top AI Tools for Developers
Tags: developer, devops, ai, tooling
Published: 2023-06-11 07:12:40

While the DevOps landscape is becoming broader, many new tools and platforms are emerging. All these tools and platforms' main goal is to enhance the developer experience and increase productivity. With the introduction of tools like [ChatGPT](http://openai.com/chatgpt) and [Bard](https://brad.google.com), a new breed of AI tools is popping up to help developers speed up their tasks. DevOps, when combined with the power of AI, becomes a powerful combination. We can already see the use of AI in DevOps practices in detecting vulnerabilities in code, streamlining the development pipeline, monitoring applications and infrastructure, making code recommendations etc. AI has become a boon for DevOps practitioners, and today, we will see some top AI tools for DevOps and how they are helping developers. 

## DevOps & AI: Common user cases
The medley of AI and DevOps is getting more popular day by day and is getting highly recommended to use both in conjunction with the power of automation. This duo is changing how developers used to write code, manage software deployment pipelines, monitor applications etc. DevOps focuses on breaking down the barrier between Dev and Ops, keeping automation at the utmost priority. Conversely, AI enables machines to learn from the data inputs, make intelligent decisions and help developers automate repetitive tasks. 

- **Software Development**
When these two domains merge, we can witness wonders in software development. This synergy between AI and DevOps empowers developers with the tools that help them reduce the time it takes to do repetitive tasks and will help them focus on writing features. Firstly, AI-driven code scanning tools provide all the code insights on different types of vulnerabilities present inside your code. Then, help you with powerful suggestions and quick fixes before the code enters the production environment.

- **Analytics & Monitoring Tools**
We have analytics and monitoring tools that provide real-time insights into the performance and health of complex systems. Whenever there is any anomaly or deviation from the expected behavior, the alert is sent through a preferred ChatOps channel. Over time, these monitoring tools enabled with AI can predict possible failures, downtime duration, mean time to recovery, rollback strategy etc. 

- **Team Collaboration & Communication**
These days, collaboration and communication within DevOps teams are happening through AI-assisted DevOps tools that act as virtual assistants. Not just that, you can even automate your development workflows, provision infrastructure, manage resources, and create CI/CD pipelines using more powerful AI-assisted tools such as Kubiya. DevOps engineers can rely on AI-powered chatbots to access relevant documentation, troubleshoot issues, and receive recommendations, fostering knowledge sharing and enhancing productivity.

## Top AI Tools for DevOps in 2023
![ai tools for devops](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/syo2dqsslil8faefwqds.png)

There are several AI tools that are making a big buzz in the DevOps market. Today, we will see some wonderful tools that are built on top of AI and ML capabilities to assist DevOps engineers and organizations. Let’s go!

### Kubiya
![kubiya workflow](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/whwnkhb1jcpr9t00kkc3.gif)
**_Source: [Kubiya](https://kubiya.ai/)_**

[Kubiya](https://kubiya.ai/), the new AI virtual DevOps assistant, has created a significant buzz in the DevOps space, offering a game-changing solution for teams involved in software development and operations. With its advanced AI capabilities, Kubiya leverages Large Language Models throughout its entire stack, integrating conversational AI into its algorithms where it automates repetitive tasks, provides actionable insights, and facilitates seamless collaboration within DevOps teams. In addition, by integrating with existing DevOps tools and platforms, Kubiya streamlines processes such as code deployment, testing, monitoring, knowledge retrieval, and incident management, enabling teams to focus on higher-level strategic activities. The introduction of Kubi marks a paradigm shift in the DevOps landscape, where for the first time, organizations can achieve greater efficiency, agility, innovation and SLAs in their software development lifecycle without needing to add headcount.

![kubiya devops workflow](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/0iakmrbktmg3crogtl1h.png)

Repetitive and mundane tasks in DevOps can be daunting and can drain the energy out of your developers. So it is time to say goodbye to those complex configuration tools that add a burden to your engineering team. Kubiya helps you manage your developers and DevOps engineers' time so they can do more in less time. Kubiya can be integrated with any of your favorite cloud-native tools and be used in your software delivery pipelines. The DevOps workflows can be automated and deployed faster with more confidence. 

### Amazon CodeGuru
![AWS CodeGuru](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/kb899y8495h5m3xf0bgl.png)
**_Image Source: [Amazon AWS](https://aws.amazon.com/blogs/aws/find-your-most-expensive-lines-of-code-amazon-codeguru-is-now-generally-available/)_** 

AWS CodeGuru is an AI-powered development tool that revolutionizes the software development pipeline and provides invaluable assistance to DevOps engineers. Leveraging machine learning techniques, CodeGuru analyzes code and offers intelligent recommendations to optimize performance, identify potential bugs, and improve overall code quality. By employing AI, CodeGuru can detect code issues, such as resource leaks, concurrency problems, and inefficient algorithms. It provides developers with actionable insights and suggestions for code improvements, enabling them to address issues proactively and deliver high-quality code faster. This leads to reduced debugging time and enhanced application performance.

Furthermore, CodeGuru integrates seamlessly into the DevOps workflow. It automatically scans code repositories, identifies critical areas for improvement, and generates detailed reports. These insights help DevOps engineers prioritize their efforts, allocate resources efficiently, and streamline the development process.

### Sysdig
![sysdig](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/1113rw5n5k9vaovdcvot.png)
**_Image Source: [Sysdig](https://sysdig.com/ecosystem/kubernetes-containers/)_**

Sysdig is an innovative platform that employs AI to assist DevOps engineers throughout the software development pipeline. By leveraging machine learning and advanced analytics, Sysdig provides comprehensive visibility and monitoring capabilities for containerized environments.

Using AI, Sysdig can automatically detect and analyze patterns, anomalies, and potential security threats within the software stack. It enables DevOps engineers to proactively identify and resolve issues, ensuring the stability and security of their applications. By automating the monitoring process, Sysdig reduces the manual effort required for troubleshooting and enables faster incident response.

Moreover, Sysdig utilizes AI-driven insights to optimize performance and resource allocation. It analyzes the behavior and performance of containers, microservices, and infrastructure components, identifying areas of improvement and recommending optimizations. This empowers DevOps engineers to fine-tune their applications, enhance scalability, and optimize resource utilization.

### PagerDuty
![pagerduty ai](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/i2xvbfdxb69d19mfi2fk.png)
**_Image Source: [PagerDuty](https://www.pagerduty.com/ty/webinar/harness-the-power-of-aiops/)_**

PagerDuty is a leader in the field of incident management, and it recently launched a new solution that caters to AI enthusiasts: [PagerDuty AIOps](https://www.pagerduty.com/blog/introducing-pagerduty-aiops/). It is not just about setting up your CI/CD pipelines, you need to have better incident management in place, and that is where PagerDuty shines by notifying the team about the incidents that occurred in the deployments, so the team can take immediate action when an unintended event occurs (unsuccessful deployment, error in deployment etc.) 

PagerDuty AIOps is powered with intelligence and automation capabilities to help engineering teams to reduce noise, triage efficiently to drive the right actions towards resolution, and remove manual and repetitive work from the incident response process. PagerDuty AIOps works out of the box without requiring long implementations or heavy, ongoing maintenance. 

### Atlassian Intelligence
![Atlassian Intelligence ai](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/l4gz8upnv6yel0q5beid.png)
_**Image Source: [Atlassian](https://www.atlassian.com/software/artificial-intelligence)**_

Atlassian recently introduced its AI-powered virtual assistant called '[Atlassian Intelligence](https://www.atlassian.com/software/artificial-intelligence)', which leverages the power of AI and reacts to customer queries with auto-generated ChatGPT-like responses in a way a human can. For instance, if you want it to summarize the action items from a recent meeting you had, you only have to tell it to generate a summary and link the document with the transcript for it to generate a list of decisions and action items. Atlassian Intelligence also helps people that use JIRA software for support tickets; the tool can respond wisely so the JIRA support staff and engineers can efficiently manage their time on critical tickets. 

IT teams can easily generate summaries for the projects and can also track the status of where they stand on a weekly basis using the help of Atlassian Intelligence. 

### Dynatrace’s Davis
![dynatrace ai](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/eu1cvkhy9kiveg7n97wh.png)
**_Image Source: [Dynatrace](https://www.dynatrace.com/platform/artificial-intelligence/)_**

Dynatrace is known for monitoring infrastructure and notifies information regarding log monitoring that shows the CPU speed and usage, the response time of the processes, traffic to your network etc. Now, it combines the power of AI and has a new solution known as 'Davis'. Dynatrace's Davis AI is an intelligent, automated engine that is designed to assist IT and Ops engineers in managing and optimizing complex IT environments. Davis AI leverages artificial intelligence and machine learning algorithms to analyze vast amounts of monitoring data and provide actionable insights and recommendations.

Davis continuously evaluates billions of dependencies in milliseconds, does the root cause analysis, detects anomalies in seconds, and provides intelligent insights, in-depth analysis and speedy remediation. 

### Datadog APM
![data dog](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/olrryp6hfgksnzsa034a.png)
**_Source: [DataDog APM](https://www.datadoghq.com/product/apm/)_**

DataDog better understands the performance issues and gives complete visibility of your application to help you troubleshoot whenever there are any application anomalies. DataDog APM has revolutionized modern application management. Datadog Application Performance Monitoring (APM) equips AI-powered technology that helps DevOps and security teams with granular-level analysis and tracing of applications, backend services and databases. It collects the logs, metrics, and user data to help provide better visibility of application performance, resource usage, etc. Moreover, in case of any anomaly in the application's expected behaviour immediately enables you to detect the root cause analysis and help solve the issues faster. 

DataDog APM helps you with advanced code performance, easy tracking, alerting and fixing anomalies. This way, you can abort or roll back any application deployments if issues occur before production. It also helps your company with application reliability by always making it highly available. 

### Snyk
![snyk ai](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/yx492kvbfxvyqy4tl4bn.png)
**_Source: [Snyk](https://docs.snyk.io/scan-application-code/snyk-code/introducing-snyk-code/key-features/ai-engine)_**

Snyk is a company that offers a platform focused on helping developers and DevOps professionals improve the security of their applications and containers. Snyk incorporates AI and machine learning techniques into its platform to provide automated and intelligent security testing and vulnerability management. Snyk has become a trusted member when it comes to application security scanning. Snyk combines different real-time data sources to understand and model an application's security posture from the original code. In addition, Snyk provides more in-depth and valuable application information to help security professionals take proper action. 

[Snyk uses AI](https://snyk.io/blog/ai-in-developer-security/) for semantic code analysis and presents accurate vulnerability data with quick fixes. Snyk uses AI not just for application vulnerabilities; it also uses AI to monitor social and community channels around to filter unique issues and bring them to the security team's attention. Even open source package vulnerabilities can be easily uncovered using Snyk's advanced natural language processing. 

### Harness
[Harness](https://www.harness.io/) utilizes AI in its CI/CD platform to revolutionize software release processes. With AI-powered automation and analysis, Harness empowers developers and teams to streamline their workflows and optimize application deployments. The platform leverages AI algorithms to automate testing, enabling the generation of test cases based on historical data and identifying potential areas of risk. Additionally, AI aids in code quality analysis, offering insights and suggestions for code improvements and ensuring high standards of code integrity. Harness also utilizes AI for continuous monitoring, detecting anomalies and performance issues in real time while providing proactive alerts and recommendations for remediation.

#### Conclusion
We have seen so many AI-enabled tools that assist developers in their day-to-day tasks. But today’s LLM-enabled tooling has enabled DevOps to take software development to another level.  These tools are getting trained and fine-tuned every day, and there comes a day when developers simply focus on the things that matter most instead of doing mundane and repetitive tasks. Even complex DevOps functions can be managed by tools like [Kubiya](https://kubiya.ai/), which is mandatory for any company practising DevOps. The competition is fierce, and customers have many options in the market to choose from; however, the best way to make an impact on your company’s bottom line is by delivering software/features much faster.  

In an exciting world of LLM-enabled AI tooling, we live in a very exciting time to be a DevOps. Let’s embrace the power of LLMs and in combination with our domain-specific experience, we will in partnership with intelligent machines, deploy software with more confidence!