Whether you use them for autocompleting code or help with debugging, AI-powered coding assistants can really save you time and improve your code quality.
In this article, we’ll explore some of the best AI coding tools to transform your development process, including tools for bug fixing, code writing, and more.
What is an AI coding assistant?
An AI coding assistant is a software tool that uses artificial intelligence to help developers with a variety of coding-related tasks. These assistants can analyze code snippets, suggest improvements, identify bugs, propose fixes, or even generate entire sections of code based on natural language prompts.
They often operate within integrated development environments (IDEs) like VS Code, JetBrains, or browser-based platforms, supporting a wide range of programming languages.
Examples of popular AI coding assistants include GitHub Copilot, Cursor, Claude Code, and OpenAI Codex.
Programming AI assistants typically rely on machine learning models, particularly large language models (LLMs) trained on extensive datasets comprising open-source projects, repositories, language documentation, and tutorials. The diversity and volume of their training data enable them to identify coding patterns, predict developer intent, and deliver contextually relevant recommendations. This can include code suggestions, documentation drafting, code reviews, and guidance on API usage.
How we review software at Spacelift
We aim to make our recommendations practical and vendor-neutral. For each tool we include, we evaluate category fit, core capabilities, integrations, documentation quality, security/governance features (when relevant), and pricing transparency. We also reference public review signals to validate common strengths and limitations. Review data is included for context and reflects what was publicly available at the time of writing.
Spacelift is the publisher of this article. We’ve included Spacelift Intelligence so you can compare it directly, but treat it as a vendor perspective, not an independent ranking.
20 AI coding tools
The growing popularity of AI coding assistants means you have many options to choose from. Below, we’ll explore some of the best AI coding tools available.
The top AI coding assistant tools include:
1. GitHub Copilot
GitHub Copilot is an AI coding assistant built by GitHub (a Microsoft subsidiary) in collaboration with OpenAI, and one of the most widely used tools in this category. It started as inline code completion and has grown into a broader platform.
Alongside real-time suggestions, it now has Copilot Chat, an agent mode that edits across multiple files and runs terminal commands on its own, and a coding agent that can take a GitHub issue and open a pull request for review. It works inside editors like Visual Studio Code, Visual Studio, JetBrains IDEs, Neovim, and Xcode, and on paid plans you choose which model handles a request, including models from OpenAI, Anthropic, and Google.
Copilot is comfortable with HCL and will scaffold Terraform resource blocks, variable definitions, and module calls quickly. It still guesses at provider arguments and module sources it hasn’t seen, so treat the plan output as a draft, not a deploy.
We walk through this in How to Use GitHub Copilot for Terraform Infrastructure.
Key features
- Multiple language support: GitHub Copilot supports many programming languages including Python, JavaScript, TypeScript, Ruby, and, Go, and is most effective in languages with large amounts of public code.
- Multi-model choice: On paid plans you switch models per request, picking from OpenAI, Anthropic, and Google options depending on the task.
- Agent mode: Copilot plans and executes multi-step tasks, editing across files, running commands like tests or installs, and iterating until the task is done. Generally available in VS Code and JetBrains.
- Coding agent and code review: Assign Copilot a GitHub issue and it works in the background to open a pull request, and it can review PRs as part of your workflow.
Pricing:
- Free plan with monthly usage caps, Pro and Pro+ for individuals, and Business and Enterprise billed per user (Enterprise also requires GitHub Enterprise Cloud, raising the real per-seat cost).
Website: https://github.com/features/copilot
Ratings and reviews:
- G2: 4.5/5 (228 reviews)
For and against
- With access to common patterns from a huge body of public code, Copilot speeds up boilerplate and gives you a strong starting point, especially in widely used languages.
- Because it learns from public repositories, suggestions can echo insecure or outdated patterns, and there are licensing questions around generated code. GitHub offers a filter to block suggestions matching public code and IP indemnity on paid plans, but human review still matters.
2. Tabnine
Tabnine is a privacy-focused AI-powered coding platform, built for teams that can’t send their code to a third-party cloud. It started as a code-completion tool and now covers completion, chat across the development lifecycle, and AI agents for tasks like code review and refactoring. It runs in the cloud, on-premises, or fully air-gapped, supports bringing your own large language model (LLM), and works across most major IDEs.
Key features
- Enterprise Context Engine: Builds an organization-wide map of your codebase, standards, and patterns, so suggestions match your conventions instead of generic public code.
- Private, flexible deployment: Cloud, on-premises, or air-gapped, with zero code retention and bring-your-own-LLM support.
- AI agents: Agents for code review, testing, and refactoring, with MCP integration to connect tools like issue trackers.
Pricing:
- A time-limited trial leads into paid plans: Code Assistant for completion and chat, an Agentic Platform tier that adds AI agents and tool integration, and Enterprise with on-premises, air-gapped, and bring-your-own-LLM deployment.
Website: https://www.tabnine.com/
Ratings and reviews:
- G2: 4.1/5 (48 reviews)
For and against
- Zero code retention plus on-premises and air-gapped deployment give Tabnine the strongest privacy and compliance story among the mainstream assistants, which is why regulated teams choose it.
- It costs more than Copilot or Cursor with no free tier, and its general-purpose completions aren’t as sharp, so you’re picking it for privacy and governance rather than raw suggestion quality.
3. Cursor AI
Cursor AI is an AI-native code editor, built as a fork of Visual Studio Code, designed to improve the efficiency of software development. Both beginners and experienced developers find it useful because it automates repetitive coding tasks, reduces errors, and provides refactoring suggestions. Its primary goal is to speed up the coding process and improve overall code quality.
Cursor’s multi-file edits are useful when you refactor a Terraform module and the change ripples across environments. On the flip side, it will confidently rewrite state-sensitive code, so review diffs on anything that touches a backend or a provider version before you apply.
Key features
- AI-powered code suggestions: By offering intelligent, real-time code suggestions based on the context of what you’re writing, Cursor AI can help prevent common errors and accelerate coding speed.
- Autocomplete and syntax correction: Cursor AI provides autocompletion to help developers write code faster, and it also checks for syntax errors, ensuring that the code follows best practices.
- Composer and Agent mode: Cursor makes coordinated edits across multiple files and runs longer agentic tasks on its own, going beyond single-line suggestions. It integrates with Git and supports MCP connections to outside tools.
Pricing:
- Free Hobby plan with limited usage, then paid tiers: Pro for individuals, Pro+ and Ultra for heavier use, Teams for organizations (shared rules and centralized billing), and a custom Enterprise plan.
- Students get Pro free.
Website: https://www.cursor.com/
Ratings and reviews:
- G2: 4.6/5 (175 reviews)
For and against
- Cursor AI is great for automating mundane tasks like autocompleting code, identifying syntax errors, and offering suggestions for better code structure,
- Cursor AI may struggle with more creative or abstract coding problems where human insight is critical.
4. Sourcegraph Cody
Developed by Sourcegraph, Cody is an AI coding assistant that provides intelligent code suggestions, automates repetitive tasks, and improves code search and comprehension. It uses Sourcegraph’s code intelligence platform to search, understand, and generate code across large codebases, and it’s built for enterprises and teams that need strong privacy controls and scalable search.
As of 2026, Cody is an enterprise-only product. Sourcegraph discontinued the free and individual Pro plans in July 2025 and now points individual developers to Amp, a separate agentic coding tool. So Cody is the entry to know if you’re an enterprise with a large, multi-repo codebase; if you’re a solo developer, look at Amp instead.
Key features
- Code search integration: Cody’s deep integration with Sourcegraph’s code search lets you find, navigate, and reference code across large and complex codebases, even across different repositories.
- Codebase understanding: Cody uses Sourcegraph’s indexing and cross-repository code intelligence to base suggestions on public datasets and your own code.
- Cross-repository insights: You can use Cody to see how similar functions or components are implemented across a large, multi-repo project, which improves both collaboration and consistency across teams.
Pricing:
- Enterprise only. The free and individual Pro plans were discontinued in July 2025, and individual developers are now directed to Amp, Sourcegraph’s separate agentic tool.
Website: https://sourcegraph.com/cody
For and against
- Cody goes beyond simple code completion with cross-repository insights and project-wide code comprehension, which makes it useful for large-scale and enterprise codebases.
- It’s now enterprise-only with no individual plan, and on very complex private codebases the AI can take time to learn the project, so initial setup and integration may require effort.
A note on Amp: Amp is Sourcegraph’s agentic coding tool, and the path it now recommends for individual developers. It sits on top of Sourcegraph’s code search, so it carries the same large-codebase context Cody is known for, but works as an agent that plans and carries out multi-step edits rather than only suggesting code.
It runs as a VS Code extension and a CLI, is free to start, and has paid options for teams and enterprises. Sourcegraph spun Amp out as a separate company in late 2025, so it’s developed independently now.
5. Replit
Replit is an online, browser-based development platform that lets you write, test, and deploy code in many programming languages without setting up anything locally.
It started as an IDE and has become an AI-first, app-building platform. You can describe what you want in plain language and have Replit’s agent build and deploy it, or write code yourself with AI assistance in the editor. It combines coding, collaboration, and deployment in one place.
Replit is built for standing up apps fast, not for managing your own infrastructure. It’s handy for a quick internal tool or a prototype, but it deploys to Replit’s own hosting rather than to the cloud accounts your Terraform manages, so it sits outside a typical IaC workflow rather than inside it.
Key features
- Replit Agent: An autonomous AI that builds full applications from a natural-language prompt, handling the code, database, and deployment.
- In-editor AI assistance: Real-time completions, explanations, and code generation for when you’d rather write code yourself. This was previously branded Ghostwriter and is now part of Replit’s AI.
- Real-time collaboration: Multiplayer lets several people work in the same workspace at once, with AI help for each person.
Pricing:
- Free Starter plan with limited Agent credits, then paid tiers: Core for solo builders, Teams for collaboration, and higher tiers for heavier or governed use.
- Agent runs on effort-based credit pricing, so cost scales with how complex the build is.
Website: https://replit.com/
Ratings and reviews:
- G2: 4.5/5 (355 reviews)
For and against
- Replit’s all-in-one model is its strength: you build, run, deploy, and collaborate in one browser workspace, and Multiplayer lets a team work on the same project in real time with AI help for each person.
- The browser environment is slower and less extensible than a local editor, and Agent and deployment usage run on credits, so heavy use can lead to costs that are hard to predict on lower tiers.
6. Aider
Aider is an open-source AI pair programmer that runs in your terminal rather than inside an IDE. You point it at your project, describe what you want in plain language, and it edits the relevant files directly, then commits each change to Git with a descriptive message. It’s model-agnostic, so you connect it to whichever large language model (LLM) you prefer, from Claude or GPT to Gemini or a model running locally.
Aider fits infrastructure work well because it’s git-native. Run it against a Terraform or OpenTofu repo and every edit lands as its own reviewable commit, so you can diff, undo, or roll back a change the same way you would any other code change.
Being model-agnostic also means you can point it at a local model when your IaC holds things you’d rather not send to a hosted API.
Key features
- Git-native edits: Aider edits files directly and commits each change with a clear message, so the AI’s work shows up as normal, reviewable Git history you can diff or revert.
- Bring any model: It connects to most major LLM providers and to local models, so you’re not locked into one vendor and can match the model to the task or to your privacy needs.
- Repository-aware context: Aider maps your codebase so the model understands how files relate, which keeps suggestions grounded in the wider project rather than a single file.
- Architect mode: For larger changes, a planning step separates designing the change from writing it, which helps on multi-file refactors.
Pricing:
- Aider itself is free and open source. You bring your own LLM API key and pay only for the model usage, or run a local model at no token cost.
Website: https://www.codiga.io/
For and against
- Because Aider lives in the terminal and commits through Git, it slots into an existing command-line and version-control workflow without asking you to switch editors, and the model freedom means no vendor lock-in.
- It’s terminal-only with no visual IDE, requires you to set up and pay for your own model access, and has a steeper learning curve than click-to-accept tools like Cursor or Copilot.
7. Sourcery
Sourcery is an AI code review tool built for teams shipping code quickly, including AI-generated code.
It began as a Python refactoring assistant inside the IDE and now leads with automated pull request reviews: when someone opens a PR, Sourcery adds a summary and line-by-line feedback on bugs, code quality, and security. It still provides real-time refactoring suggestions in the editor, and it can investigate production issues through a Sentry integration and propose fixes.
Key features
- Automated PR reviews: On each pull request, Sourcery posts a summary and line-by-line comments on bugs, quality, and security issues.
- In-editor refactoring: Real-time suggestions in VS Code and JetBrains IDEs that simplify logic, cut duplication (the DRY principle), and tidy structure as you write.
- Production issue fixes: A Sentry integration investigates production errors, traces root causes, and proposes code fixes, closing the loop from review back to production.
Pricing:
- Free for open-source and public repositories.
- Private repositories need a paid plan: Pro, billed per seat, and a custom Enterprise plan.
Website: https://sourcery.ai/
For and against
- Sourcery’s automated PR reviews and refactoring reduce technical debt and take routine review work off your senior engineers, and its Python analysis is among the deepest available.
- Its review coverage spans many languages, but the depth drops noticeably outside Python and JavaScript, and there’s no free tier for private code, so polyglot teams may prefer a more general code reviewer.
8. Snyk Code (formerly DeepCode AI)
Snyk Code is the static application security testing (SAST) part of the Snyk developer security platform. It scans your source code for vulnerabilities and quality issues as you write and in CI, using Snyk’s DeepCode AI engine, and it suggests fixes you can apply.
DeepCode began as an ETH Zurich project that Snyk acquired in 2020. Today, it’s the engine inside Snyk Code rather than a separate product.
Key features
- AI-driven SAST: Snyk Code scans your code in real time and in CI, tracing data flow across files to find issues like injection and broken authentication, with a focus on fewer false positives.
- Snyk Agent Fix: The autonomous remediation feature (formerly DeepCode AI Fix) generates one-click fixes and can open pull requests, for both human- and AI-generated code.
- One platform across the stack: Snyk Code sits alongside Snyk Open Source (dependencies), Container, and IaC, so you can scan source, libraries, containers, and infrastructure config from one place.
- Broad language and tool support: It covers many languages and plugs into IDEs, GitHub, GitLab, Bitbucket, and Azure Repos, and CI/CD pipelines. Snyk doesn’t train on your code.
Pricing:
- Free plan with a monthly cap on scans
- Paid plans: Team (billed per developer, with a minimum seat count) and a custom Enterprise plan. Some automated-fix capabilities sit on the higher tiers.
Website: https://snyk.io/product/snyk-code/
For and against
- Snyk Code’s AI-driven scanning catches real security issues across source, dependencies, containers, and IaC in one platform, and Agent Fix can propose and open fixes rather than just flagging problems.
- It’s a security tool, not a general coding assistant, so it won’t write features for you, and on very large codebases scans with the full platform layered on can take longer.
9. Hugging Face
Hugging Face is the most widely used open-source platform for machine learning, where the community shares models, datasets, and demo apps. It’s best known for the Transformers library and its model Hub. It isn’t a coding assistant you install in your editor. It’s where the open models that power coding tools live, and where you can run, fine-tune, or self-host them yourself.
Key features
- Open model Hub: A large catalog of open models for many tasks, including code generation, that you can use as-is or fine-tune. Open code models such as StarCoder2, which Hugging Face co-created with the BigCode community, live here alongside other current coder models.
- Spaces: A place to build and host ML apps and demos, including AI coding environments, so you can try a model in the browser before committing to it.
- Run it your way: Use models locally, through Hugging Face’s inference options, or self-hosted, which gives teams control over where their code and data go.
Pricing:
- Free to use the Hub and explore models.
- Paid options add a Pro plan for individuals and Team and Enterprise plans billed per seat, plus usage-based pricing for inference and the compute behind Spaces and endpoints.
Website: https://huggingface.co/
For and against
- Hugging Face’s strength is its huge library of open models and its community, which makes it the natural place to find, compare, and self-host a code model rather than depending on a single vendor.
- It’s a platform, not a ready-made assistant, so it takes more setup than a plug-in tool, and larger models need real compute, which can be a constraint in resource-limited environments.
10. Amazon SageMaker
Amazon SageMaker is AWS’s platform for building, training, and deploying machine learning models.
At the end of 2024, AWS restructured it: the classic model-building environment is now called Amazon SageMaker AI, and it sits inside a broader, next-generation Amazon SageMaker, a unified studio that also pulls in AWS analytics and data tools.
It isn’t a coding assistant like GitHub Copilot. It’s the place you’d build, fine-tune, and host a model yourself, including a code model, rather than something that suggests code in your editor.
Key features
- AutoML capabilities (SageMaker Autopilot): Builds and tunes models automatically, which helps teams that aren’t ML specialists train a model for tasks like code suggestion or bug prediction.
- Hyperparameter tuning: Automatic tuning optimizes a model’s accuracy and speed without manual trial and error.
- Managed and scalable infrastructure: AWS runs the servers behind training and inference, so your team builds models instead of managing machines.
Pricing: Pay-as-you-go pricing model
Website: https://aws.amazon.com/sagemaker/
Ratings and reviews:
- G2: 4.2/5 (55 reviews)
For and against
- SageMaker handles large datasets, offers pre-built algorithms, and supports real-time inference, which makes it a solid place to build and host a custom model, including one for coding tasks, inside AWS.
- It’s an ML platform, not a ready-to-use coding assistant, so there’s real setup involved, and the pay-as-you-go model can run up costs if training jobs and endpoints aren’t watched.
11. Amazon Q Developer
Amazon Q Developer is a generative AI-powered assistant from AWS that helps developers throughout the entire software development lifecycle (SDLC). Built on top of Amazon Bedrock, it automates coding tasks, debugging, architecture design, and cost optimization.
In 2025, Amazon Q Developer has evolved to support multi-agent orchestration, allowing it to not only generate code but also suggest architectural designs, plan deployments, and automate complex AWS workflows.
Note: Kiro is AWS’s agentic development tool, and the successor to Amazon Q Developer. AWS announced Q Developer’s end of support in April 2026 and is moving developers to Kiro over a 12-month window, so Kiro is the entry to know going forward. Where Q Developer focused on inline chat and completion, Kiro is built around spec-driven development: you describe what you want as a structured specification, and Kiro implements against it.
Key features
- Conversational AI assistant: Allows developers to interact in natural language to generate solutions, code snippets, architecture diagrams, and configuration templates.
- AWS ecosystem integration: Automates cloud-native application development by setting up services like Lambda, DynamoDB, S3, and API Gateway.
- Multi-agent workflows: Supports intelligent orchestration of multiple tasks (e.g., setting up monitoring, cost estimation, and CI/CD pipelines) using multiple AI agents.
- Cost management tools: Integrated with AWS Cost Explorer for proactive cost analysis and optimization.
Pricing:
- Free tier and Amazon Q Developer Pro ($19 per month per user) — note that new sign-ups are being blocked from May 15, 2026 as part of the move to Kiro (see above)
Website: https://aws.amazon.com/q/developer/
Ratings and reviews:
- G2: 4.6/5 (34 reviews)
For and against
- Amazon Q is highly optimized for developers working within the AWS environment, assisting with tasks such as setting up services, managing infrastructure, and implementing cloud-native solutions.
- However, Amazon Q’s utility outside the AWS ecosystem is limited compared to more general-purpose AI code tools.
12. Qodo (formerly CodiumAI)
Qodo (formerly CodiumAI) is an AI code-integrity platform, built on the idea that AI should help verify code quality, not just generate more of it. It started as a test-generation tool and rebranded to Qodo in 2024 as it grew into three products:
- Qodo Gen, an in-IDE and CLI assistant that writes code, fixes errors, and generates tests
- Qodo Merge, which automatically reviews pull requests
- Qodo Command, for scripting agents in the terminal and CI
Its 2026 release moved PR review to a multi-agent architecture, with separate agents checking for bugs, quality, security, and test-coverage gaps at once.
Key features
- Qodo Gen: Indexes your whole repository for context, then generates code, fixes errors, and writes meaningful unit tests (real assertions and edge cases, not stubs) inside VS Code and JetBrains IDEs.
- Qodo Merge: Reviews each pull request with a multi-agent architecture, ranks findings by severity so reviewers focus on what blocks a merge, and supports GitHub, GitLab, Bitbucket, and Azure DevOps. Its PR-Agent core is open source and self-hostable.
- Codebase-aware context: A retrieval-based context engine takes the wider codebase, including multiple repositories, into account rather than only the diff in front of it.
Pricing:
- Free Developer plan for individuals, with monthly caps on PR reviews and on credits for IDE and CLI use. Paid plans add Teams, billed per user, and a custom Enterprise plan with deployment options including SaaS, VPC, on-premises, and air-gapped.
Website: https://www.qodo.ai
Ratings and reviews:
- G2: 4.8/5 (62 reviews)
For and against
- Qodo is the rare tool that pairs automated PR review with genuine test generation in one platform, and grouping findings by severity keeps reviews focused instead of drowning you in lint-style noise.
- The credit-based pricing is more layered than competitors that charge a flat per-seat rate, so usage can be harder to predict, and it sits at the higher end for what is mainly a quality-and-review layer rather than a code generator.
13. Claude Code
Claude Code is Anthropic’s agentic coding tool. Rather than suggesting lines as you type, it works as an agent: you describe a task in plain language and it reads your codebase, edits files across the project, runs commands like tests and installs, and iterates until the task is done.
It runs in the terminal as a CLI, in a desktop app with parallel sessions and visual diff review, and through a web interface, and it can be tagged on a GitHub issue to open a pull request back.
Claude Code suits infrastructure work because it operates on your whole repository and runs commands. Pointed at a Terraform or OpenTofu project, it can make a change across modules, run a plan, read the output, and adjust, rather than handing you a snippet to paste.
A CLAUDE.md file at the repo root lets you encode your conventions and guardrails so it follows them on every run. As with any agent, treat its plan as a proposal: review the diff before you apply, especially on anything touching state or providers.
Key features
- Agentic, multi-file workflow: Claude Code plans and carries out multi-step tasks across files, runs terminal commands, and self-corrects, which suits larger refactors and debugging rather than single-line completion.
- Runs where you work: Available as a terminal CLI, a desktop app with parallel sessions and visual diff review, and a web interface, plus a GitHub integration that turns an issue into a pull request.
- Project memory and extensibility: A CLAUDE.md file gives it persistent project context, and it supports MCP connections, plan mode, subagents, hooks, and skills for tailoring it to your workflow.
Pricing:
- Claude Code is included with paid Claude plans (Pro, Max, Team, and Enterprise) and is also available through Anthropic’s API for usage-based billing.
- The free Claude.ai plan doesn’t include it.
For and against
- As an agent that works across the whole project and runs its own commands, Claude Code handles complex, multi-file tasks that single-line assistants can’t, and the CLAUDE.md memory plus MCP support let teams shape how it behaves.
- It needs a paid Claude or API account (no free tier), an autonomous agent can move several steps in the wrong direction before you catch it, and it tends to spin on vague requirements, so clear instructions and diff review matter.
14. AskCodi
AskCodi is an AI coding assistant whose main draw is model flexibility. Instead of tying you to one provider, it acts as a single gateway to several large language models (LLMs), including GPT, Claude, and Gemini, so you can switch between them for code generation, autocomplete, unit tests, and documentation.
It runs across a wide range of editors and bills by token usage from a shared pool rather than per seat, which is its pitch against the per-seat incumbents.
Key features
- Multi-model access: One tool, many LLMs, with the ability to switch between providers per task instead of committing to a single vendor.
- Token-based, shared pricing: A team draws from one token pool rather than paying per seat, and unused tokens roll over, which can make budgeting more predictable for small teams.
- Broad editor support and API: Works across VS Code, JetBrains IDEs, Zed, Sublime Text, and Neovim, and offers an OpenAI-compatible API that plugs into tools like Continue.dev.
Pricing:
- Free tier for individuals, then paid plans on a token-usage model: flexible monthly plans that share a token pool across a team with rollover, plus annual individual tiers and custom Enterprise pricing.
Website: https://www.askcodi.com/
Ratings and reviews:
- G2: 4.8/5 (100 reviews)
For and against
- AskCodi’s one-gateway, multi-model approach avoids vendor lock-in and per-seat pricing, which suits indie developers and small teams who want to pick the right model per task and pay only for what they use.
- It’s a lighter assistant than agentic tools like Cursor or Claude Code, reviewers note thinner documentation, and the token model, while flexible, means cost tracks usage rather than a flat predictable seat price.
15. Gemini Code Assist
Gemini Code Assist is Google’s AI coding assistant, powered by its Gemini models. It offers inline completion, a chat assistant, and an agent mode that handles multi-step tasks across your project, and it works in VS Code and JetBrains IDEs as well as in a command-line agent, Gemini CLI. Its standout trait is deep Google Cloud integration: it understands GCP services natively, which shapes both how it’s priced and who it’s best for.
A transition to know about: Google is unifying its developer AI into a new platform called Antigravity. Starting June 18, 2026, the Gemini Code Assist IDE extensions and CLI stop serving free and individual users, who are moved to Antigravity. The paid Standard and Enterprise editions keep agent mode and the CLI, so if you’re evaluating this as an individual, check whether you’re really signing up for Antigravity.
Key features
- Agent mode and Gemini CLI: A reasoning agent that carries out multi-step tasks in the IDE, plus an open-source command-line agent for terminal-based work, with MCP support for connecting outside tools.
- Native Google Cloud integration: Deep context on GCP services, so it’s strongest for teams building and operating on Google Cloud.
- Large context and source citations: A very large context window for working across big codebases, and citations on suggestions drawn from public code.
Pricing:
- Free edition for individuals with generous daily limits (being migrated to Antigravity, see above), then paid Standard and Enterprise editions billed per user, with Enterprise adding code customization on your private codebase and fuller GCP integration.
Website: https://cloud.google.com/products/gemini/code-assist
For and against
- For teams on Google Cloud it’s hard to beat: native GCP context, Terraform-for-GCP help, a capable agent mode and CLI, and a free tier that’s genuinely usable for getting started.
- The native advantage is tied to Google Cloud and fades on other clouds, and the in-progress move to Antigravity makes the individual-tier story unsettled right now, so confirm exactly what you’re subscribing to.
16. CodeGeeX
CodeGeeX is a free, open-source AI coding assistant that runs as a plugin in your editor. It began as a research project at Tsinghua University and is now developed under Z.ai (formerly Zhipu AI). Beyond code completion, it handles inline chat, code review, unit test generation, and repo-aware question answering, and it works in VS Code, JetBrains IDEs, and HBuilderX.
It’s multilingual in two senses: it covers a wide range of programming languages and takes prompts in both English and Chinese.
Key features
- Free, multi-feature plugin: Completion, inline chat, code review, test generation, and repo-aware Q&A in one extension, at no cost.
- Broad language and IDE support: Covers a wide range of programming languages, including Python, Java, C++, JavaScript, and Go, across VS Code, JetBrains IDEs, and HBuilderX.
- Open source and self-hostable: The model and code are openly available, so you can run it on your own infrastructure for full control over data and privacy.
Pricing: Free and open source. You can use the hosted plugin at no cost or self-host the model, paying only for whatever compute you run it on.
Website: https://codegeex.cn/en-US
For and against
- CodeGeeX bundles completion, chat, review, and test generation into one free, open-source plugin, and self-hosting gives privacy-conscious teams full control over their code and data.
- Its suggestions are less polished than the leading paid tools, it has no agentic mode (so no multi-step, multi-file task execution like Cursor or Claude Code), and its Chinese origin may raise governance questions for some organizations.
17. Continue.dev
Continue.dev is an open-source AI coding assistant built around one principle: you choose your own model and keep control of your data. It works in VS Code and JetBrains IDEs with chat, autocomplete, and inline edits, and it connects to more or less any large language model (LLM), including hosted options like Claude, GPT, and Gemini, or local models through Ollama.
In 2026 it added an Agent Mode that plans and carries out multi-file changes, and it has expanded toward “Continuous AI,” an open-source CLI that runs agents on pull requests, with a headless mode for CI/CD pipelines.
Continue.dev is a strong fit for platform teams for two reasons. First, model freedom plus self-hosting means you can run it fully offline against a local model, so IaC with sensitive values never leaves your environment. Second, the Continuous AI CLI can run agents on your Terraform or OpenTofu pull requests inside CI, enforcing team rules and flagging issues automatically. The trade-off is that this power comes with real setup.
Key features
- Bring any model, no lock-in: Connect 50+ models from any provider, or run local models for full privacy. You’re never tied to one vendor.
- Agent Mode: Describe a goal and the agent analyzes the codebase, plans, and makes multi-file changes, going beyond chat and autocomplete to larger refactors.
- Continuous AI and CI integration: An open-source CLI runs async agents on pull requests, with headless mode for CI/CD and integrations for GitHub, Sentry, and Snyk.
Pricing:
- Free and open source (Apache 2.0). You bring your own model keys, so you pay only for model usage, or nothing at all when running local models.
- An optional paid hub/team layer adds shared agents, access controls, and enterprise support.
Website: https://continue.dev/
For and against
- Continue.dev gives you complete control over your models, data, and costs, the deployment flexibility (cloud, on-premises, fully offline) suits regulated environments, and the new CI agents extend it from an editor tool into review infrastructure.
- It’s more configure-it-yourself than plug-and-play tools like Cursor or Copilot, the CLI-first Continuous AI direction adds setup overhead, and suggestion polish depends on which model you wire up rather than coming tuned out of the box.
18. Windsurf
Windsurf is an agentic AI code editor.
It began as Codeium, rebranded to Windsurf as it shifted from code completion to a full AI IDE, and in July 2025 was acquired by Cognition, the company behind the autonomous engineer Devin. Its defining feature is Cascade, an agent that understands your entire codebase and can plan and make changes across it, rather than suggesting one line at a time.
Under Cognition it added a proprietary fast model and a visual code-navigation feature, and it works as a standalone editor plus plugins for 40+ other IDEs.
Key features
- Cascade agent: A reasoning agent that maps your full codebase, then plans and executes multi-file changes, blending AI actions with hands-on editing.
- SWE-1.5 and Fast Context: A proprietary coding model tuned for speed, paired with fast code retrieval, so the agent responds quickly even on large projects.
- Codemaps: AI-annotated visual navigation of a codebase, a feature competitors haven’t matched, which helps with understanding legacy or unfamiliar code.
Pricing:
- Free tier to start, then paid plans (Pro and higher tiers) priced on daily and weekly usage quotas. Windsurf retired its older credit-based pricing in March 2026.
Website: https://windsurf.com/
Ratings and reviews:
- G2: 4.2/5 (27 reviews)
For and against
- Cascade’s deep codebase understanding, fast proprietary model, and unique Codemaps navigation make Windsurf strong on large and legacy codebases, and its compliance certifications suit regulated environments. It ranked at the top of at least one 2026 AI dev-tool ranking.
- The March 2026 pricing change left some existing users feeling they got less for the same money, context limits can bite on very large projects, support is thin, and the long-term product direction under Cognition (and how Devin folds in) is still settling.
19. Spacelift Intelligence
A note on Saturnhead AI: If you’ve seen Spacelift’s earlier AI assistant, Saturnhead AI, this is its evolution. Saturnhead AI launched in 2025 to translate infrastructure run logs into plain-language explanations of what went wrong and how to fix it. That log-intelligence capability now lives inside Spacelift Intelligence’s Infrastructure Assistant, alongside the broader query, policy, and provisioning features.
Spacelift Intelligence is the AI layer of Spacelift, the infrastructure orchestration platform. Rather than just helping you write Terraform faster, it puts AI inside the infrastructure workflow itself.
It has two parts that work together: an Infrastructure Assistant, a conversational interface that knows the state of your stacks, run history, and the shape of your environment, and Spacelift Intent, a natural-language path for provisioning and managing infrastructure.
The idea is to shorten the distance between what an engineer wants and what gets provisioned, measured in interactions rather than pipeline stages, while keeping IaC and GitOps as the system of record for production.
This is the one tool on the list built only for infrastructure, so the angle is the whole point.
- The Infrastructure Assistant answers questions about state, recent changes, and failed runs without making you dig through logs or code, and handles policy creation, drift management, and troubleshooting.
- Intent gives platform teams a fast, governed way to spin up environments for prototyping and testing while production stays on Terraform or OpenTofu pipelines.
Both run under the same control plane, so the AI works within your existing security and compliance guardrails rather than around them.
Key features
- Infrastructure Assistant: A conversational AI across the platform that answers questions about your infrastructure state and changes, generates diagnostics for failed runs, helps create policies, and guides onboarding.
- Spacelift Intent: A natural-language deployment path (now generally available) for rapid prototyping and experimentation, complementing rather than replacing IaC and GitOps.
- Governed and model-flexible: Both components operate under Spacelift’s existing control plane for security and compliance, run on Claude Sonnet 4.6 by default, and support bringing your own LLM.
Pricing:
- Part of Spacelift’s platform
For and against
- Spacelift Intelligence is purpose-built for infrastructure, so it understands your actual environment rather than treating IaC as generic code, and it keeps AI actions inside your governance and compliance controls.
- It’s most valuable to teams already on (or considering) the Spacelift platform; it’s an infrastructure operating layer, not a general-purpose coding assistant for application work.
20. OpenAI Codex
OpenAI Codex is OpenAI’s agentic coding system. The name causes real confusion, so it’s worth clearing up: the original Codex was a code-completion model from 2021 that OpenAI retired in 2023.
The Codex developers use today is a different product, relaunched in 2025, and it’s now one of OpenAI’s flagship offerings, with millions of developers using it each week.
Rather than autocompleting line by line, it reads your repository, edits files across the project, runs tests and commands, and opens pull requests for review. It runs as an open-source terminal CLI, an IDE extension, a cloud agent you delegate to through ChatGPT, and a GitHub bot, all sharing one account and model.
Because Codex works as an agent against your whole repository, you can point it at a Terraform or OpenTofu project and have it make a change, run a plan, read the output, and open a pull request, rather than handing you a snippet. Its underlying models have grown notably stronger on long, multi-step work like large refactors and migrations, which suits sprawling IaC.
The standing caution applies: an agent’s plan is a proposal, so review the diff before you apply, especially on anything touching state or providers.
Key features
- Agentic, multi-surface workflow: One system across a terminal CLI, IDE extension, cloud delegation through ChatGPT, and a GitHub bot, so you can run a quick local task or hand off a long one and review the resulting pull request.
- Built on the current GPT-5 model family: Codex runs on OpenAI’s latest agentic-coding models, tuned for long-horizon tasks, large code changes, and reliable tool use.
- Open-source CLI: The Codex CLI is open source (built in Rust), so you can inspect it, run it locally, and sign in with either a ChatGPT plan or an API key.
Pricing:
- Included with paid ChatGPT plans (Plus, Pro, Business, Edu, and Enterprise)
- The CLI itself is open source and free, with model usage drawn from your plan or billed through the API by token when you use an API key.
Website: https://openai.com/codex/
For and against
- As an agent that works across the whole project and runs its own commands, Codex handles complex, multi-file tasks that line-by-line tools can’t, it lives in the surfaces developers already use (terminal, IDE, GitHub), and the open-source CLI avoids lock-in at the tooling layer.
- It needs a paid ChatGPT plan or API usage (no free tier), the model naming is genuinely confusing (the same word covers a 2021 model, today’s tool, and a family of models), and as with any agent it can move several steps down the wrong path, so diff review matters.
As a software company that uses data and AI to make the meat industry more optimized and sustainable, Völur knew that transitioning to infrastructure as code (IaC) would enhance their engineering team’s productivity and speed. Spacelift's self-service platform enhanced their developer velocity by accelerating the speed at which code runs successfully in production.
Pros and cons of using AI coding tools
AI programming tools are becoming increasingly popular, offering assistance and speeding up workflows. However, you should be aware of their advantages and limitations. Let’s look at some pros and cons of using AI coding assistants.
| Benefits of using AI coding tools | |
| Enhanced efficiency | AI programming tools allow developers to work faster by automating repetitive or time-consuming tasks. This means they can focus more on the creative and complex aspects of programming while the AI handles mundane code generation. |
| Error detection and debugging assistance | One of the major strengths of AI tools is their ability to identify errors as the code is written. By providing real-time error detection, these tools help programmers spot mistakes early, saving time that would otherwise be spent on debugging later. |
| Access to advanced learning resources | AI code assistants provide valuable learning resources by providing developers with coding suggestions, examples, and explanations. These tools can act like a tutor for beginners, offering guidance on best practices. For seasoned developers, AI offers a way to learn new techniques and stay updated with evolving programming trends. |
| Disadvantages of using AI coding tools | |
| Over-reliance on AI assistance | Relying too heavily on AI is risky. Depending on AI for coding suggestions without fully understanding the underlying logic could hinder developers’ growth and problem-solving abilities. Over time, this reliance may prevent them from developing strong independent coding skills. |
| Security and privacy concerns | Many AI coding tools require access to cloud-based platforms, raising concerns about the security and privacy of the code being shared. Sensitive information could potentially be exposed during the process, leading to data breaches or security vulnerabilities. Developers need to be cautious, especially when handling confidential or proprietary code. |
| Lack of contextual understanding | AI tools, though powerful, often lack the depth of understanding required for highly complex or context-dependent problems. They work well for standard coding tasks but may offer inaccurate or irrelevant suggestions for projects with unique requirements. This can result in developers needing to revise or even discard AI-generated code that doesn’t align with the broader project goals. |
Key points
AI coding tools can assist with everything from autocompleting code to fixing bugs, and they’re particularly helpful in reducing repetitive tasks.
Using AI coding tools is a double-edged sword: If you know what you are doing, they can greatly enhance your development speed and your time to market, but the debugging process can get cumbersome if you lack experience. No AI tool will build exactly what you need without a good prompt, and it will certainly not generate production-ready, bug-free code from the first few iterations, so you will need to constantly provide new prompts, or fix the issues yourself.
When selecting an AI coding tool, consider factors like your preferred programming languages, the tool’s integration with your development environment, privacy concerns (such as whether local models are used), and whether you’re working solo or with a team. The ideal AI coding assistant should fit seamlessly into your workflow, boost productivity, and meet the specific needs of your project or organization.
Keep infrastructure moving at AI speed
Spacelift Intelligence keeps platform teams ahead. Fuse traditional IaC and GitOps pipelines with an AI deployment model and a powerful Infrastructure Assistant.
