Best AI for Coding in 2025

Although it may seem recent, Artificial Intelligence has not simply materialized from thin air. It goes back nearly half a century, to a time when electrical signals and hardware engineering trumped coding. A time when cable spaghetti and cardboard logic were the de facto ways to get any sort of expected output.

As computing matured, the center of gravity shifted, from physical circuitry to lines of code, as software gradually took over the complexity once hardwired into machines.

Today, we heavily rely on coding in all aspects of tooling creation. Just 10-15 years ago, to build an application, you’d need a team comprised of the following:

• Backend Developer
• Frontend Developer
• Sysadmin / Infrastructure Engineer
• Database Administrator (DBA)
• QA Engineer
• Build/Release Engineer

Today, we’ve seen Moore's Law (The idea that computing power will double every two years at minimal cost) firsthand. AI has exponentially grown within just a few years. And the ability to ship quality code faster than your competitors is key to succeeding in this market. So, instead of building a team of 6 people and spending weeks planning, then weeks developing, how do you better utilize your time? And how can AI Coding Assistants accelerate your cycle from weeks to hours?

What are AI Coding Assistants?

So, we have an understanding that AI has been here for a while, and its development shows no signs of slowing down. Now, what exactly are AI coding assistants, and why has it become the top of everyone’s mind?

At their core level, AI coding assistants are tools that use artificial intelligence to help developers with coding tasks, such as code completion, code review, and code generation. You can think of these as your personal assistant who does the backend research for you and even breaks down complex coding tasks if needed. All the while, having on hand the fully reviewed, running, and functioning code base just waiting for you to say the word to push to production or testing.

Not only do they write, recommend, and review code, but the true power lies in their ability to automate tasks. Automation is the single most important aspect for serving users of your application accurately and rapidly. This spans the whole architecture of your application.

If one of the tenets of your application is automated, let’s say the customer-facing side, that’s great! Your customers will thank you for the speedy responses. But let’s say your internal processes take long, arduous hours validating and constantly completing repetitive tasks; this will surely move up the architecture. Ultimately, it slows the shipping process, and at the end of the day, unloads the burden onto the user.

Touching upon user experience, of course, anyone can push functioning code fast. It’s sometimes recommended in the beginning stages of building applications to “just get something out there”. Given the number of users connected globally, are you certain the code will scale with future demand?

This is where one of AI’s key features stands out: ensuring code quality. You don’t have time to consistently ensure your code is set up for possible harmful edge cases. Your focus is on building the application. So, having something like a coding assistant constantly check the quality of your code, repo-wide and across multiple files, in the background is crucial.

NLP and Code Awareness

One question that most developers ask when trying out any new tool or doing their research is: Will this be a hassle to integrate?

And the simple answer is: No, it won’t be a hassle. AI coding assistants can be integrated with popular development environments like Visual Studio Code (VS Code) and can support multiple programming languages. Some are directly integrated, some are IDE/VS Code extensions, and others are completely standalone platforms.

What about those who are not incredibly familiar with coding or would like to build but don’t have the experience to do so? Their main question may be: How fast can I get something up and running with minimal knowledge?

And the simple answer is: Incredibly fast. AI coding assistants can generate code by utilizing Natural Language Processing (NLP), an increasingly popular buzzword. NLP parses, understands, generates, and infers meaning much like a human could. So, instead of constantly changing your search phrase to narrow in on an article, AI coding assistants give both tech-savvy and non-tech-savvy developers the ability to go from prompt to working code in half the time.

This shift in how we interact with code naturally brings up a comparison to the earlier evolution of developer tools.

Remember when code completion was groundbreaking? You would simply just have to write the code, and the underlying “intelligent sensing” would suggest the rest of the string. Though this was not AI, and more so, taking patterns into consideration, or drawing from what is in memory at that time. There is no awareness of context in the code you’re writing. This is, as you guessed, where AI coding assistants can help.

AI coding assistants leverage deep neural language models trained on large-scale source code. Unlike traditional autocomplete (as stated previously), these AI coding assistants use the models to understand:

• Syntax
• Semantics
• Project-wide context

The harmonious combination of code modeling, real-time inference, and IDE integration. Everything from context-aware code suggestions to more advanced AI capabilities like agents being able to write or refactor entire codebases.

AI-Powered Testing and QA

Finally, one of the most underrated yet game-changing features of AI coding assistants is their ability to automatically generate tests. Writing comprehensive unit tests and integration tests is often a tedious, time-consuming process, one that developers either put off or only partially complete. This leaves blind spots in the codebase and increases the risk of bugs surfacing in production.

AI assistants can analyze your functions and modules in real-time, understand their expected behavior, and auto-generate relevant test cases, complete with assertions, edge cases, and mocking where necessary. This not only enforces better code coverage but also ensures that your application remains resilient as it evolves.

Think of it as having a QA engineer embedded within your IDE; one who never gets tired, never overlooks a corner case, and always keeps up with the latest testing frameworks and patterns.

By pairing code generation with automated test creation, AI coding assistants help developers not only move faster but with confidence, shifting the focus from “does this work?” to “what’s next to build?”

Benefits of Using AI Coding Tools

AI programming tools bring many benefits to developers and organizations that adopt them. Some offer the assistance of pair programming with an extremely fluent developer, while others can run tasks end-to-end where teams just need to review and comment on a PR before merging into production. Here are a few high-level benefits of AI-powered code completion and agentic development:

• AI coding tools can improve coding efficiency and productivity by automating tasks and providing real-time code suggestions.
• They can help reduce common coding errors and improve code quality by providing intelligent code generation and code review capabilities.
• AI coding tools can support multiple programming languages and can be integrated with popular development environments like VS Code.
• They can also provide features like code optimization, code structure improvement, and automated documentation generation.

Current AI Coding Tools EcoSystem

If you've done any research in this space at all, you'll realize that these tools are abundant and vary widely in their capabilities. While some are focused solely on assisting developers through natural language code generation and autocomplete, others are focused on full automation and can run end-to-end tasks such as bug fixes and performance improvements through to fully implementing production-ready features. Let's look at some of the most popular and best AI coding tools currently available.

Tembo

Tembo is an end-to-end AI coding assistant designed not just for writing code but for managing your entire codebase lifecycle. Tembo stands out from the average AI-powered coding tool, offering a deep contextual understanding that focuses on large-scale projects and production-ready code and architecture.

Highlights

• Native support for complex projects
• Strong PostgreSQL AI-native integrations
• Understands and navigates repo-wide context
• Offers opinionated best practices tailored to enterprise environments
• Integrated documentation and testing support

Cursor

Cursor is an AI pair programmer built directly into your IDE. It emphasizes real-time code suggestions, debugging, and single-function updates with high accuracy.

Highlights

• Lightweight and fast
• Effective inline suggestions
• VS Code-native experience

Challenges

• Less effective on large codebases
• Limited in orchestrating multi-file changes
• Not ideal for long-term architectural decisions

Devin

Devin markets itself as an “AI software engineer” capable of understanding tickets, writing code, and pushing to Git, all autonomously.

Highlights

• End-to-end automation for small tasks
• Great at MVP/prototype-level workflows
• Can operate independently on GitHub

Challenges

• Overpromises autonomy (still needs supervision)
• Works best in sandboxed environments
• Not always reliable in production-grade deployments

CodeWhisperer (AWS)

Now part of Amazon's Q Developer platform, CodeWhisperer is built into the AWS development ecosystem, with tight integration for cloud-native workflows.

Highlights

• Strong AWS ecosystem integration
• Support for Java, Python, and more
• Helpful for infrastructure-as-code

Challenges

• Limited usefulness outside AWS
• Can feel rigid compared to more flexible tools

GitHub Copilot

One of the first widely used AI pair programmers. Copilot offers inline code completions and suggestions across a variety of languages.

Highlights

• Tight integration with GitHub
• Support for dozens of languages
• Huge developer community

Challenges

• Limited in repo-wide context
• Requires third-party tools for full pipeline assistance
• Lacks task-level project management capabilities

Tabnine

Tabnine is an autocomplete AI tool focused on privacy and speed. It offers on-device deployment for teams with strict data controls.

Highlights

• On-prem/private model deployment
• Blazing fast autocomplete
• Support for enterprise security

Challenges

• No full project awareness
• Primarily focused on syntax, not semantics
• Doesn’t offer architectural recommendations

Conclusion

AI coding assistants have changed the game, but most still act like temporary helpers, narrowly focused on autocompletion or simple suggestions. Tembo is different.

It’s not just a productivity booster. It’s a full-stack engineering partner. With deep codebase awareness, automated testing, integrated documentation, and architectural intelligence, Tembo isn’t one piece of your workflow. It is the workflow.

While others assist, Tembo owns the entire coding lifecycle, from understanding your intent to writing production-grade code, validating it, and preparing it for scale. It goes beyond just moving fast, but truly redefines what developing with AI is all about. Try Tembo today and start building with AI agents built for engineering teams.