Azure DevOps Jira integration: A build vs buy analysis

There are five approaches to integrating Azure DevOps and Jira:

1. Model-based integration solutions. Model-based integration is the easiest to set up and maintain because it requires no custom coding.
2. Native integrations within Jira and Azure DevOps. Both ADO and Jira offer basic integration capabilities, but most organizations need more than these.
3. DIY integrations using open APIs. This method involves building your own connectors using ADO’s and Jira’s APIs. It’s the lowest upfront cost but the most work.
4. Plugins. Plugins are pieces of software that add specific functionalities, such as integrations, to your application.
5. Point-to-point integration solutions. These off-the-shelf solutions usually involve some custom coding to set up and maintain.

The best method is the one that allows you to maximize cross-tool collaboration, efficiency, and quality without breaking the bank. Knowing the pros and cons of each will help you decide the best integration method for your organization.

But before we get into the details, let’s start by revisiting why integration is so important.

In organizations where people and teams are working and siloed across Jira and Azure DevOps, keeping each group happy and confident that they are working with up-to-date information in the same level of detail is a challenge.

Without comprehensive, flexible, and proven integrations to bridge the gap between tools, developers spend hours every week manually copy-pasting information. This tedious work wastes time and leaves your organization alarmingly vulnerable to human error. Imagine workers having to cart stacks of paper back and forth throughout the building to collaborate; messy, time-consuming, and error-prone.

You can remedy this problem by integrating Azure DevOps and Jira today. With real-time integration, you can realize the benefits of both tools without sacrificing connectivity, focus, or speed:

• Full visibility for managers. Product managers working in Jira can see the real-time status of ADO work items without switching tools.
• Higher quality output. Automation minimizes the risk of human error, thereby minimizing the risk of rework and delays.
• More capacity for high-value work. An Azure DevOps Jira integration eliminates manual data transfer, freeing up hundreds, even thousands of hours for developers each year.
• Employee happiness. Workers are happier when they can work efficiently in their preferred tool.
• Confidence that updates are not being missed. No data updates are overlooked, thus promoting seamless collaboration and efficient project management.

Should you build your integration or buy a commercial, off-the-shelf solution? Let’s compare the advantages and disadvantages of different integration methods.

Azure DevOps and Jira offer simple built-in integration capabilities, allowing you to set up basic synchronizations between the two tools without writing any code.

Setting up native integrations is relatively simple. First, you’ll have to download and configure the Jira Software Cloud app from ADO’s marketplace. Then, navigate to the project you want to integrate with Azure DevOps. From there, go to “Settings” and create a “New Service Connection” to Jira. You’ll have to map the fields you want to integrate between Azure DevOps work items and Jira issues; then, you can set up synchronization rules and triggers, determining when and how data flows between the tools.

Advantages of using native integrations

Native integrations are included in most licenses for Azure DevOps and Jira, so there is zero upfront cost. And, as mentioned above, setting up the integration is straightforward. It can be done within the ADO or Jira user interface by one admin, and you likely won’t need customer service assistance.

Disadvantages of using native integrations

Although the simplicity of a native integration is attractive, most organizations find them too simplistic to be useful.

Native integrations are very limited. They rarely support custom fields or accommodate complex integration patterns. Even with such an integration, software developers must switch back and forth between Jira and ADO to get all the information they need.

Additionally, native Azure DevOps and Jira integrations are not scalable. Each time new projects wish to utilize the integration, you’ll have to configure an integration all over again. You’ll also have to maintain and update your integrations regularly as the applications change and increment versions. Over time, and especially for large organizations with hundreds of projects, this maintenance work is unmanageable.

Finally, synchronization is not executed in real-time, and there are rarely conflict resolution mechanisms built into these integrations. This means that if changes are made to corresponding fields simultaneously in ADO and Jira, the integration won’t know how to resolve the discrepancy.

Remember that having high-quality integration capabilities is not a priority for Atlassian or Microsoft. They naturally focus on perfecting their core capabilities, so secondary features, including native integrations, are minimalistic.

If you have unique requirements or complex workflows that cannot be accommodated by ADO and Jira’s built-in integrations, you may consider custom coding using the respective APIs provided by Azure DevOps and Jira.

An API, or Application Programming Interface, is a set of rules and instructions for communicating with a certain application. Using APIs, developers can build custom integrations that allow Azure DevOps and Jira to send information back and forth.

Azure DevOps and Jira both have open APIs, meaning developers can freely access them to build a connector between the two tools. Connectors, like bridges, can be as robust or minimal as you require, depending on how much and what type of data you want to synchronize.

Advantages of building your own Jira to Azure DevOps integration

The main advantage of building your own connectors using Azure DevOps and Jira APIs is that you have complete control over what information flows and under what conditions. As a result, you can accommodate your business’s idiosyncratic use cases.

There’s no upfront cost, but this is deceptive. Opportunity costs and maintenance costs loom large.

Disadvantages of building your own Jira to Azure DevOps integration

Building your own integrations is not cost-effective in the long run. Leaders often underestimate the task’s complexity and time it takes to build. Assuming you assign two full-time senior developers to the task, integrating one artifact type between Jira and AzureDevOps can take up to eight months. That’s eight months that your developers could have been adding value to your own products!

Additionally, the DIY integrations must be maintained constantly. Tools change their APIs regularly, so your code would need to be reworked. Over the course of a year, you would spend upwards of $120,000 to troubleshoot and maintain the simplest Azure DevOps Jira integration.

When you choose to build everything yourself, it can be a challenge to add new use cases or change existing ones. This method is inflexible, unscalable, and a poor use of your software developers’ valuable time.

Third-party plugins are similar in nature and function to Jira and ADO’s native integrations. They extend the functionality of your platform by enabling it to exchange data with other tools or databases.

You can purchase low-to-moderate cost plugins on Atlassian Marketplace, such as Move Work Forward’s “Azure DevOps for Jira” (low cost) or Exalate’s “Azure DevOps Connector for Jira” (medium cost). The former is a one-way integration that imports data from Azure DevOps into Jira, allowing workers in Jira to track development and release work in ADO. The latter provides bi-directional integration but requires custom coding for any divergent use cases or unique mappings.

Advantages of plugins Azure DevOps Jira integration

Plugins are easy to acquire and are a relatively low-cost option. Plugins are easy to set up for simple integrations, like synching the creation and status of Jira Issues and ADO Work Items.

Your maintenance costs will be less than if you built your own connectors because plugin providers typically update their connectors to conform with the latest APIs.

Disadvantages of plugins for Azure DevOps Jira integration

First, like native integrations in Jira and Azure DevOps, plugins are limited in their functionality. They usually satisfy one specific need. For example, some plugins only integrate Azure DevOps-> Git or Azure DevOps Repo -> Jira, but not the rest of the development environment. When your needs change, and you want to add more use cases, you’ll have to add another plugin, write custom code, or simply endure a suboptimal integration.

Second, plugins act like a closed pipe and limit scalability. As your organization grows and you want to connect more tools, you’ll have to add plugins upon plugins upon plugins, which can slow down your applications.

Finally, consider that plugins are usually developed by very small companies, often in other locations. Timely support can be difficult when you need it. If your integration goes down, you might be waiting a while for a fix.

Point-to-point integration tools are out-of-the-box platforms that facilitate bi-directional flow of data between endpoints. Like plugins and native integrations, they create a bridge between two endpoints, such as Jira or ADO instances. The difference is that they provide a graphical interface to help you map fields and manage integrations.

These tools have a point-and-click UI to set up basic integrations, and you may have to write code to integrate custom fields.

Advantages of point-to-point integration for Azure DevOps and Jira

Unlike plugins and native integrations, point-to-point tools can integrate more than just Azure DevOps and Jira. You can set up integrations through the tool’s UI between any two supported endpoints.

Since these tools are purpose-built for integration, they are typically more configurable and secure than plugins.

Disadvantages of point-to-point integration for Azure DevOps and Jira

Point-to-point integration tools seem great in their demo videos, but many organizations are surprised by how much maintenance and overhead are required to keep their integrations operational.

Many of these tools require you to map every Jira project to every Azure DevOps project. This means that you’ll have to configure your integrations afresh for each new project.

If you expand your integration network to include tools other than ADO and Jira, the number of integrations you’ll have to configure will rise exponentially. The graphic below illustrates this point.

These tools’ interfaces give the impression of a central hub, but in reality, they create a complicated web of integrations. The complexity and maintenance skyrockets as you add more tools and more projects within those tools. For this reason, we don’t recommend these tools for large or growing organizations.

Model-based integration solutions use a common data model to normalize data across all connected tools. A common data model involves mapping a standardized schema, or model, onto the specific artifact types in your tools.

Models are universal translators; they “tell” the integration solution how to interpret and normalize similar data from heterogeneous tools. This approach makes configuring integrations faster and more scalable because you only need to map each artifact type once.

For instance, a generic “defect” model helps your integration solution extract and normalize relevant data from defect-type artifacts in various tools, like Bugs in Jira and Azure DevOps or incidents in ServiceNow. For each tool that you want to connect, you would map fields such as status, priority, assignee, and description onto the corresponding fields in the model. Then, seamlessly synchronize as many projects as you want between as many tools as you want.

Point-to-point integration tools will have you mapping each project pair individually, whereas models allow you to apply a single mapping to all participating projects.

Advantages of model-based Azure DevOps Jira integration

Model-based integration lets you connect your heterogeneous development environment, creating seamless flow from idea to outcome. Why limit yourself to Azure DevOps and Jira? Models allow you to add an unlimited number of integrations without having to create hundreds of mappings between tools.

Model-based integration solutions enable complete flexibility when it comes to adding tools and changing integration patterns. The reusability of models means that model-based integration tools require 90% less maintenance than point-to-point solutions.

Models make it easy to flow complex information, including custom fields, rich text, artifact relationships, and folder structures. Workers in every tool benefit from having complete, real-time information.

One example of a model-based integration tool is Planview Hub, which integrates more than 60 best-of-breed tools, including Azure DevOps and Jira. Planview tests its integrations 500,000 times a day to make sure they are running smoothly, and it proactively updates connectors when APIs change, so customers never have any downtime.

See how to set up an integration quickly with Planview Hub

Planview Hub has a graphical UI that makes it easy to set up, modify, and visualize your integrations.

Disadvantages of model-based Azure DevOps <-> Jira integration

Model-based integration solutions have the highest upfront cost. Since one of the main advantages is scalability, small organizations may not see as much ROI as larger organizations.

Although no coding is required, there is a learning curve when it comes to model-based integration. Some training may be required for your admin, so we recommend looking for a vendor that provides customer support as part of the package.

When choosing an integration method, there are several factors to consider, including the size of your company, the complexity of your use cases, your growth trajectory, the flexibility you require, and whether you want to integrate other tools.

Consider these questions when you evaluate your options:

• Flexibility: Is it easy to modify? Can I use it for every use case?
• Scalability: Is it suitable for a larger organization?
• Overhead: How expensive is it to maintain?
• Upfront cost: How expensive is it to buy?
• Time-to-value: How long does it take to get up and running?

Any integration methods that require you to write custom code will be less adaptable and scalable than out-of-the-box solutions. Model-based integration offers a faster, simpler alternative that eliminates the heavy lifting for large organizations that can’t afford to spend hours mapping projects one by one between Azure DevOps and Jira.

Integrations are the connective tissue that holds your tools and teams together. The stronger and more flexible your integrations, the better your teams can collaborate and execute.

Watch the demo: integrating Azure DevOps and Jira with Planview Hub

Still not sure how to integrate Jira and ADO?

• Download the Build vs. Buy infographic
• Read the white paper: Why Models Are The Only Way to Scale Software Delivery Toolchain Integration
• Read the ebook: Which integration solution is right for you?
• Learn how companies like BMW, Accenture, and Telus use Planview Hub to optimize collaboration and save time