Bazel vs Packer: What are the differences?

Introduction:

1. Performance: Bazel is focused on providing fast and incremental builds by only rebuilding what is necessary, while Packer is mainly used for creating machine images for multiple platforms, which typically does not require the same level of performance optimization for builds.

2. Use case: Bazel is primarily used for building and testing software applications across different platforms, whereas Packer is tailored for creating identical machine images for multiple platforms and environments, such as virtual machines, containers, and cloud instances.

3. Language support: Bazel supports multiple programming languages, including Java, C++, and Python, while Packer is more focused on configuration languages like JSON and HCL (HashiCorp Configuration Language) for defining machine images and infrastructure setups.

4. Dependency management: Bazel has built-in support for managing dependencies and ensuring reproducible builds, while Packer relies on external tools and plugins for managing dependencies and integrating with other systems.

5. Workflow integration: Bazel is designed to integrate seamlessly with continuous integration/continuous deployment (CI/CD) pipelines for automating software delivery processes, while Packer is commonly used as part of infrastructure as code (IaC) workflows to automate the creation of machine images within cloud environments.

6. Community and support: Bazel is backed by Google and has a large community of contributors, while Packer is supported by HashiCorp and benefits from the company's expertise in infrastructure automation tools, leading to active development and support for the tool.

In Summary, Bazel and Packer differ in performance optimization, use cases, language support, dependency management, workflow integration, and community support.