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The Most Popular AI Tools: What Developers Use and Why
AI tools have become a core part of modern software development. Developers rely on them throughout the life cycle, from writing and refactoring code to testing, documentation, and analysis.
Once experimental add-ons, these tools now function as everyday assistants and are firmly embedded in routine workflows. But why have AI tools become so essential – and how are developers actually using them?
The insights in this article draw on findings from the JetBrains State of Developer Ecosystem Report 2025, which tracks how developers use tools, languages, and technologies, including AI tools, in real-world environments. Shifting the focus from technical model performance, this article looks at usage patterns, developer preferences, and adoption trends across tools, regions, and workflows.
Before we work through which AI tools developers use most, why they choose them, and how these tools fit into everyday work, let’s first clarify what AI tools are and why they matter so much right now.
Disclaimer: Please note that the findings in the article reflect data collected during the specific research period set out in the report.
Table of Contents
· What AI tools are and why they matter now
· Most popular AI tools among developers
· What makes developers choose one AI tool over another
· How developers use AI tools in daily workflows
· Global snapshot: How AI tool adoption differs across regions
· Barriers to adopting AI tools
· Future of AI tools: What developers want next
· FAQ
· Conclusion
What AI tools are and why they matter now
Today’s AI tools for developers span several categories. They include code assistants that suggest or generate code, as well as tools that review code autonomously. Many come as IDE integrations that understand project context.
There are also AI-powered search and navigation tools, refactoring helpers, and documentation generators. In addition, teams now use testing assistants and autonomous or semi-autonomous agents to support more complex workflows.
Understanding today’s AI tools list for developers matters because these tools directly address growing pressures in modern development. They shorten development cycles, reduce manual tasks, and help teams maintain quality, which is especially important as codebases grow.
This growing reliance makes it important to understand which tools developers actually use most. In the next section, we will see what these AI tools are.
Most popular AI tools among developers
Developers rarely rely on a single AI tool. Instead, they combine multiple tools depending on their IDE, workflow style, and project requirements. According to the AI usage insights in the the JetBrains State of Developer Ecosystem Report 2025, adoption clusters around three main categories: IDE-native assistants, standalone AI-powered development environments, and browser-based or cloud chat tools.
Across these categories, the most popular AI assistants are GitHub Copilot, JetBrains AI Assistant, Cursor, Windsurf, and Tabnine. Adoption of these top AI tools varies based on ecosystem, IDE choice, and workflow style.
IDE-native assistants, such as GitHub Copilot and JetBrains AI Assistant, remain among the most popular AI tools because they operate inside the editor and integrate directly into existing workflows, making them more context-aware.
Standalone AI-focused editors and assistants, such as Cursor and Windsurf, often emphasise more experimental or agent-style workflows. This is an area that is evolving across the ecosystem, with increasing convergence between IDE-native tools and more agent-driven capabilities.
Other tools focus on specific priorities. For example, Tabnine attracts teams that prioritize privacy and local inference. Region-specific tools also play an important role in areas with strong domestic AI ecosystems or regulatory constraints.
This diversity becomes clearer when comparing the best AI tools for developers side by side.
Comparison table: AI tools overview
| AI tool | Typical use case | Underlying models | Distinct features | Integration type |
| GitHub Copilot | Code generation and completion | GPT family | Tight GitHub + VS Code workflows | IDE / Cloud |
| JetBrains AI Assistant | Context-aware help, refactoring | Claude / GPT / Gemini | Deep IDE context + privacy focus | In-IDE |
| Cursor | Inline edits, debugging, chat | Claude / Gemini | Fast UI, multi-step edits | IDE plugin |
| Windsurf | Autonomous task execution and code changes | Claude / GPT | Agent-like capabilities | Standalone |
| Tabnine | Privacy-oriented code suggestions | Proprietary / DeepSeek | Local inference options | IDE plugin |
What makes developers choose one AI tool over another
Developers are not choosing AI tools solely on novelty. They evaluate how well a tool fits existing workflows, how reliable the output feels, and whether the tool aligns with team constraints. The JetBrains State of Developer Ecosystem Report 2025 identifies several of these practical considerations that shape decision-making.
Integration quality ranks among the most important factors. Developers prefer AI coding tools that work seamlessly inside their preferred IDE. A tool that interrupts flow or requires constant context switching often fails to gain long-term adoption.
Accuracy and code quality are equally crucial. Developers expect AI coding tools to produce reliable results that they can trust. When outputs require extensive correction, confidence drops quickly.
Privacy and data security also influence developer AI preferences. This is especially true in enterprise environments. Tools that offer local processing or clear privacy guarantees often see stronger uptake in regulated industries.
Finally, pricing, transparency, and vendor reputation affect adoption. Developers value clear pricing models, flexible access, and vendors with a track record of supporting developer tools. Trust builds over time through consistency and ongoing communication.
Let’s see how developers evaluate each of these factors in this AI assistant comparison.
Key factors influencing tool choice
| Factor | Why it matters | How developers evaluate it |
| IDE integration | Supports smooth workflows | Works natively in their preferred IDE |
| Code accuracy and quality | Affects trust and usability | Produces correct, clear, and maintainable code |
| Privacy and security | Protects source code and IP | Provides clear data handling and local mode options |
| Pricing and access | Impacts adoption at scale | Offers flexible tiers and predictable costs |
| Transparency | Builds confidence | Discloses model provider and data policies |
| Vendor reputation | Signals long-term reliability | Demonstrates a history of dev tools and quality support |
How developers use AI tools in daily workflows
Developers integrate AI tool usage throughout the development life cycle rather than limiting it to a single task. Most workflows combine several forms of AI access depending on the problem at hand.
When coding with AI tools, developers may use in-IDE assistants for context-aware code help and chat-based interfaces for problem-solving and prototyping. In addition, developer AI assistant usage may combine browser tools for quick inline answers, APIs for automation and CI/CD tasks, and local models for privacy-restricted environments.
Across these use cases, developers are clearly no longer relying on a single tool. AI workflows increasingly involve choosing the right tool for the task at hand, be it writing code, refactoring, debugging, generating documentation, testing, or understanding unfamiliar code.
The JetBrains State of Developer Ecosystem Report 2025 indicates that developers frequently switch between AI access points in this way. They choose the interface that best fits the task rather than expecting one tool to handle everything.
Workflow types and examples
| Workflow type | Typical use case | Example tools | Integration context | Developer benefit |
| In-IDE assistance | Code suggestions, refactoring | JetBrains AI Assistant, GitHub Copilot | IDE | Immediate, context-aware help |
| Chat-based interaction | Explanations, brainstorming, regex, prototyping | ChatGPT, Claude | Browser / Cloud | Fast iteration and reasoning |
| API integration | Automation, CI tasks, documentation | OpenAI API, Anthropic API | Backend / DevOps | Scalable automation |
| Browser extensions | Quick inline code insights | Codeium, AIX | Web | Lightweight access |
| Local/private models | Secure, offline coding | Tabnine, DeepSeek (self-hosted models) | On-premises / Enterprise | High privacy and control |
With AI firmly established in daily workflows, the next section looks at regional differences in AI tool adoption.
Global snapshot: How AI tool adoption differs across regions
Global AI adoption patterns do not look the same everywhere. Regional ecosystems, regulations, and developer communities shape which tools gain traction. The JetBrains State of Developer Ecosystem Report 2025 highlights clear regional AI trends.
In North America, developers commonly adopt mainstream tools such as GitHub Copilot, JetBrains AI Assistant, and Claude-based assistants. Strong cloud infrastructure and rapid LLM innovation encourage experimentation with multiple tools.
European developers balance adoption with privacy considerations. Data residency and compliance requirements influence tool selection, leading to broader interest in solutions that offer transparency and local processing options.
In the Asia-Pacific region, developers often combine global tools with regional offerings. Mobile-first development cultures and fast-growing ecosystems drive rapid experimentation, particularly with cloud-based assistants.
Mainland China stands out due to its strong domestic AI ecosystem. Developers there frequently rely on local tools and models such as DeepSeek, Qwen, and Hunyuan, which align better with infrastructure and regulatory realities.
Regional highlights and local leaders
| Region | Most used tools | Local ecosystem drivers | Notable observations |
| North America | GitHub Copilot, JetBrains AI Assistant, Claude | Strong cloud and LLM innovation | High multi-tool adoption |
| Europe | JetBrains AI Assistant, GitHub Copilot | Privacy regulations, data residency | Balanced adoption across tools |
| Asia-Pacific | GitHub Copilot, Gemini | Mobile/cloud-first development cultures | Rapid experimentation and growth |
| Mainland China | DeepSeek, Qwen, Hunyuan | Strong domestic AI ecosystem | Preference for locally hosted models |
While AI tool usage worldwide is undoubtedly gaining momentum, barriers to AI adoption also exist, which we explore in the next section.
Barriers to adopting AI tools
Despite growing interest, not all developers or teams adopt AI tools easily. The JetBrains State of Developer Ecosystem Report 2025 shows that such AI adoption challenges often stem from uncertainty rather than opposition.
Privacy and security concerns remain the most common AI coding tool barriers. Teams worry about exposing sensitive code or intellectual property, especially when tools rely on cloud processing. Without clear guarantees, organizations may restrict or ban usage.
Legal and ownership questions are other reasons why developers avoid AI tools. Developers and managers want clarity about who owns AI-generated code and how licensing applies. Uncertainty leads many teams to limit AI use to non-critical tasks.
Individual barriers matter as well. Some developers lack confidence in using AI tools effectively or struggle to evaluate output quality. Others distrust AI suggestions due to past inaccuracies.
Cost, licensing, and infrastructure constraints can also limit adoption, particularly for larger teams. Per-seat pricing and usage caps further complicate budgeting and rollout decisions.
Obstacles and evaluation criteria
| Barrier | Why it matters | Typical impact |
| Privacy and security concerns | Increases the risk of exposing sensitive code | Usage blocked or restricted |
| IP and code ownership concerns | Creates legal uncertainty | Hesitation to rely on AI for core code |
| Lack of knowledge or training | Reduces confidence in using tools | Slower individual adoption |
| Accuracy and reliability issues | Impacts trust in outputs | More manual review required |
| Internal policies and processes | Requires compliance and complex approval workflows | Delayed tool rollout |
| Cost and licensing | Exceeds budget or per-seat limits | Partial or limited deployment |
In the next section, we move from the barriers of today to developers’ hopes for the future.
Future of AI tools: What developers want next
Developers do not simply want more AI features. They want better ones. The JetBrains State of Developer Ecosystem Report 2025 not only indicates greater adoption but also shows that developers are hopeful about the future of AI. Their expectations focus on reliability, integration depth, and control rather than novelty.
Higher code quality tops developer AI expectations. Developers want fewer hallucinations, cleaner outputs, and suggestions that respect project conventions. Trust grows when AI behaves predictably.
Deeper IDE integration also ranks high. Developers expect future AI tools to understand entire projects, not just individual files. Context retention across sessions and multi-file awareness are increasingly important.
Privacy remains central. Many developers want local or on-device options that allow them to use AI without sharing code externally. Transparent data handling builds confidence.
Pricing clarity and explainability also influence future AI assistant trends. Developers want predictable costs and better insight into why tools suggest certain changes.
But most significantly, as AI tools evolve, developers want support for complex workflows and architecture reasoning. The goalpost is also shifting. Developers now expect future AI tools to move beyond basic autocomplete and act as collaborative partners.
Developer expectations and trends
| Expectation | Why developers want it | Example improvements |
| Higher code quality | Trust and reliability | Fewer hallucinations, cleaner output |
| Deeper IDE integration | Seamless workflows | Context retention, multi-file awareness |
| Privacy and control | Secure code handling | On-device or local LLM options |
| Transparent pricing | Predictable team adoption | Usage-based models, clearer tiers |
| Explainability and reasoning | Trust in decisions | Clearer chain-of-thought summaries |
| Context awareness | Handling real projects | Larger context windows, project-wide understanding |
The following FAQ addresses some of the most common questions developers ask when evaluating and using AI tools.
FAQ
What are the most popular AI tools among developers today?
According to the report’s findings, developers commonly use tools such as GitHub Copilot, JetBrains AI Assistant, Cursor, and Tabnine, often combining them rather than using a single tool.
Are AI tools safe for use with private or proprietary code?
Safety depends on the tool. Developers increasingly prefer tools that provide clear privacy policies or local processing options.
Which AI tools work best inside IDEs?
IDE-native tools tend to perform best for daily coding tasks because they understand project context and workflows.
Do developers prefer local AI models or cloud-based solutions?
Preferences vary. Some developers value cloud flexibility, while others prioritize local models for privacy and compliance.
How do AI tools help with debugging and documentation?
They explain code, identify errors, suggest fixes, and generate comments or documentation drafts.
Are AI tools suitable for enterprise teams with strict security requirements?
Many are, especially when they offer strong privacy guarantees, administrative controls, and predictable pricing.
Can AI tools speed up development without reducing code quality?
Yes, when developers use them intentionally. AI tools speed up repetitive tasks such as code generation, refactoring, testing, and documentation, while reviews, IDE checks, and automated tests help maintain quality.
Conclusion
AI tools have evolved from optional add-ons into essential components of modern software development. Developers now rely on them for coding, refactoring, documentation, testing, and learning, integrating AI assistance throughout daily workflows.
Current adoption trends show that developers value accuracy, deep integration, and privacy above experimental features. The JetBrains State of Developer Ecosystem Report 2025 reflects broad and growing use across regions, tools, and development styles.
As AI tools continue to evolve, they move toward deeper context awareness, stronger reasoning, and more secure deployment options.
For developers, AI no longer represents a future possibility. It has become a practical, everyday partner in building software.
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