Atlassian Bamboo vs. AWS Lambda

If you value integration over cost, Bamboo is clearly the way to go. It offers tight integration to the rest of the Atlassian suite, and when you need traceability from issue to build, Atlassian is the right way to go. However, if you find yourself needing to save on costs, you may consider taking an approach of rolling your own build system with open source alternatives, such as Jenkins, if you don't [mind] putting in a little extra elbow grease.

Incentivized

Lambda excels at event-driven, short-lived tasks, such as processing files or building simple APIs. However, it's less ideal for long-running, computationally intensive, or applications that rely on carrying the state between jobs. Cold starts and constant load can easily balloon the costs.

Incentivized

• Levels of granularity. Organization has many projects that have many build plans that have many jobs that have many tasks, etc. And branch builds allow source control branches to be built separately.
• Versatility. I can use bamboo to manage my Java, node, or .NET build plans. I can use it to spin up Windows or Linux build agents, or install it on a Mac to build there as well.
• Bamboo integrates with other Atlassian products like Bitbucket, Stash, JIRA, etc. If a company commits to the entire Atlassian stack then work can be tracked through the whole development lifecycle which is really useful.

Incentivized

• No provisioning required - we don't have to pay anything upfront
• Serverless deployment - it gets executed only when request comes and we pay only for the time the request is getting executed
• Integrates well with AWS CloudWatch triggers so it is easy to setup scheduled tasks like cron jobs

Incentivized

• Extremely hard barrier to entry for non-backend developers
• Blackbox makes it hard to customize functionality
• The inability to add features without breaking core functionality
• No cloud solution
• Tasks cannot be put in if/else statements
• No clear right way to form build plans

Incentivized

• Developing test cases for Lambda functions can be difficult. For functions that require some sort of input it can be tough to develop the proper payload and event for a test.
• For the uninitiated, deploying functions with Infrastructure as Code tools can be a challenging undertaking.
• Logging the output of a function feels disjointed from running the function in the console. A tighter integration with operational logging would be appreciated, perhaps being able to view function logs from the Lambda console instead of having to navigate over to CloudWatch.
• Sometimes its difficult to determine the correct permissions needed for Lambda execution from other AWS services.

Incentivized

Bamboo offers solid usability for teams looking for an integrated, scalable CI/CD solution, especially those using Atlassian tools. Its interface is intuitive for existing Atlassian users, and its focus on deployment automation makes it a strong option for continuous delivery. However, its complexity and cost may pose challenges for small teams or those new to CI/CD. Overall, Bamboo’s usability shines in environments where ease of integration and streamlined workflows are prioritized. Still, it may require more effort for teams unfamiliar with its setup or without dedicated resources.

Incentivized

I give it a seven is usability because it's AWS. Their UI's are always clunkier than the competition and their documentation is rather cumbersome. There's SO MUCH to dig through and it's a gamble if you actually end up finding the corresponding info if it will actually help. Like I said before, going to google with a specific problem is likely a better route because AWS is quite ubiquitous and chances are you're not the first to encounter the problem. That being said, using SAM (Serverless application model) and it's SAM Local environment makes running local instances of your Lambdas in dev environments painless and quite fun. Using Nodejs + Lambda + SAM Local + VS Code debugger = AWESOME.

Incentivized

Bamboo is a fairly small product but having said that it was fairly easy to get assistance. Especially for the small to easy things. Anything large or fairly complex was an issue finding detailed answers for. This caused a lot of trial and error on our part to try to find a solution.

Amazon consistently provides comprehensive and easy-to-parse documentation of all AWS features and services. Most development team members find what they need with a quick internet search of the AWS documentation available online. If you need advanced support, though, you might need to engage an AWS engineer, and that could be an unexpected (or unwelcome) expense.

Incentivized

We selected Bamboo because its capabilities to integrate with other Atlassian products specially Jira Software, Bitbucket and in some useful scenarios with Confluence. Also, we found these pros important for us: great user interface, easily agent deployment, Docker compability, simply to maintain / manage, and straightforwardly integration with different notification platforms

Incentivized

AWS Lambda is good for short running functions, and ideally in response to events within AWS. Google App Engine is a more robust environment which can have complex code running for long periods of time, and across more than one instance of hardware. Google App Engine allows for both front-end and back-end infrastructure, while AWS Lambda is only for small back-end functions

Incentivized

• It helped us achieve the Continuous Deployment and Continuous Integration goals for our applications, a huge milestone that saved a lot of time for developers in making the builds and deployments and saved time for QA in running the automated tests.
• Helped with DevOps: we moved the formal approval from the email to the system and allowed the approver to actually push the button for the production deployments.
• Biggest positive impact of using Bamboo is that it improved our response time to customers and increased the frequency of our deliveries to them.

Incentivized

• Positive - Only paying for when code is run, unlike virtual machines where you pay always regardless of processing power usage.
• Positive - Scalability and accommodating larger amounts of demand is much cheaper. Instead of scaling up virtual machines and increasing the prices you pay for that, you are just increasing the number of times your lambda function is run.
• Negative - Debugging/troubleshooting, and developing for lambda functions take a bit more time to get used to, and migrating code from virtual machines and normal processes to Lambda functions can take a bit of time.

Incentivized