GitHub Copilot Reviews & Product Details

What I like best about GitHub Copilot is its exceptional accuracy in code completion and intelligent suggestions. It significantly accelerates our development workflow, reduces repetitive coding tasks, and helps write clean, efficient code seamlessly right inside the editor. Review collected by and hosted on G2.com.

Occasionally, it may suggest boilerplate code that requires minor adjustments for specific project requirements, and handling complex multi-file logic sometimes requires careful review to ensure optimal context accuracy. Review collected by and hosted on G2.com.

I really like the new agent-based features in GitHub Copilot. It can handle larger coding tasks, work across multiple files, help review code, and even use the integrated browser to test web applications. The ability to run multiple coding sessions in parallel and see changes more clearly in VS Code also makes development faster and more organized. Review collected by and hosted on G2.com.

GitHub Copilot could improve by making its suggestions more consistently accurate and reducing unnecessary or repetitive code. It would also be helpful to provide clearer explanations when a suggested solution is incorrect, improve context awareness across larger projects, and make AI credit/usage limits easier to understand. Review collected by and hosted on G2.com.

GitHub Copilot helps speed up development by suggesting code snippets, methods, test cases, and logic in real time as I code. I find it especially useful for repetitive tasks, API implementations, and generating boilerplate.

What I personally like most is that it cuts down the time I spend searching for syntax or common implementation patterns, so I can stay focused on the business logic instead. Review collected by and hosted on G2.com.

The suggestions aren’t always accurate or well optimized, especially when dealing with project-specific logic or more complex enterprise use cases. At times it produces outdated approaches or incomplete code, so manual review and validation are still necessary.

In a few backend implementation scenarios, Copilot suggested methods that looked syntactically correct but didn’t fit our project architecture or align with our coding standards, which meant I still had to adjust and verify the output. Review collected by and hosted on G2.com.

As an AI automation and DevOps engineer, what I love most about GitHub Copilot is how it dramatically cuts down the cognitive load of switching between endless YAML configurations, CI/CD pipelines, and bash scripts. Instead of wasting time digging through documentation for standard boilerplate code or syntax rules, Copilot anticipates what I need and generates it instantly. This lets me stay in the zone and focus on the high-level architecture and security of the infrastructure, rather than the tedious syntax. Review collected by and hosted on G2.com.

From a DevOps and automation standpoint, what I dislike most is its tendency to confidently suggest outdated or legacy syntax for complex infrastructure-as-code and CI/CD pipelines. Because it lacks a holistic view of a multi-repository cloud architecture, it often generates code that works in isolation but violates specific security policies or network topologies. This requires me to spend extra time meticulously auditing its suggestions to prevent deployment failures or compliance risks. Review collected by and hosted on G2.com.

It provides a comprehensive interface for interacting with multiple LLMs at the same time, while abstracting away the complexity involved. It also includes several useful capabilities, such as planning, agent mode, tool calls, and more. Its performance is very good as well. It integrates well with existing vscode IDE for a unified experience. Pricing was extremely good until june. June onwards, they changed to pay per usage model and made claude models pricy. Still, its worth it. Very much a corner stone for our whole AI SDLC approach. Review collected by and hosted on G2.com.

Pricing model is very expensive based on June changes. Review collected by and hosted on G2.com.

Its biggest advantage is how quickly it generates useful code, completes repetitive tasks, and suggests fixes while I’m coding. It saves time and helps me solve problems without constantly searching for examples online. Review collected by and hosted on G2.com.

The main downside is that Copilot can sometimes generate incorrect or overly complex code, so I still need to review and test its suggestions carefully. Review collected by and hosted on G2.com.

GitHub Copilot has really improved my coding workflow. The UI easily gets integrated with Visual Studio Code feels smooth and easy to use. It gives fast AI code suggestions, help complete functions and save a lot of typing time. Performance is quick, thought sometime suggestions are inaccurate. Overall, it makes learning and coding faster and more productive. Review collected by and hosted on G2.com.

I'm currently using the student plan which was giving access to multiple AI models from different model providers (including Anthropic, Google, OpenAI...), but with the latest update to GitHub Copilot they just removed most flagship models and limit access to remaining, which is quite disappointing. Review collected by and hosted on G2.com.

What I like the most is that it saves me time on repetitive tasks. With FastAPI or Django, it suggests almost complete endpoints, models, and CRUD logic, which speeds things up quite a bit.

It also helps when I integrate APIs or write SQL queries: it's not always perfect, but it gives me a quick foundation to work from. Review collected by and hosted on G2.com.

What I don't like is that sometimes it suggests things that "seem correct," but aren't entirely so. With FastAPI, for example, it has suggested poorly constructed validations or dependencies that pass at first glance, but then fail in logic or security. Review collected by and hosted on G2.com.

It automatically corrected code ,installs requirements .txt file ,run python commands parallely as well as sequentially .also run commands in a virtual env if possible.it is very beneficial for coders have tons of files made and they donnot want to dig in for errors finding.just ask to GitHub copilot chat and it will make that sorted for you.Also it helps in running the total project from scratch from installing dependencies to testing project on corner test cases. Review collected by and hosted on G2.com.

Many a times when it don't find solution it not stops ,instead it go on a infinite loop checking the same condition again and again and hence wastage of cpu cycles and resources happen.also manyatimes it do not take user responses as they are said for.it automatically starts its computation if the user is simply asking to show file . Review collected by and hosted on G2.com.

The best part is how familiar the platform feels, especially for beginners, and the free credits are a nice bonus. You can use both online and offline models on it, and there are plenty of configuration options. Overall, it has really made writing code and building solutions much easier for me. Review collected by and hosted on G2.com.

There are very few plugins and MCPs available, and the options become even more limited when I try to use tools while working on code that’s on another server. This is especially noticeable when I’m connected to that server in VS Code through an SSH tunnel. Review collected by and hosted on G2.com.