What if the best coding assistant for your workflow isn’t the one everyone talks about? GitHub Copilot gets the headlines, but the market around it has grown crowded, and plenty of developers now reach for something else. Some want a free tier. Some need their code to stay on their own machine. Others just want a tool that fits a specific IDE or budget.
This guide walks through the practical side of switching. It covers what each major alternative actually is, how to install it, which settings to touch first, and the mistakes that trip people up during setup. The focus is on getting a working assistant in your editor in under an hour, not on feature-list tourism.
Why Developers Look Beyond Copilot
GitHub Copilot arrived as a technical preview in June 2021 and became generally available in June 2022, built on OpenAI Codex models according to GitHub’s own announcements. That head start made it the default choice for a lot of teams. But “default” and “best fit” are different things.
The reasons people shop around tend to fall into a few buckets:
- Cost structure. Copilot Individual launched at $10 per month or $100 per year, with a Business tier at $19 per user per month. For solo hobbyists or large teams, that math gets re-examined often.
- Privacy and data residency. Regulated industries and open-source maintainers sometimes need code to stay local or on a private cloud.
- IDE and language fit. A JetBrains-first developer may get more value from a JetBrains-native assistant than from a general-purpose one.
- Model preference. Some teams want a specific model family or the ability to swap models per task.
None of these are knockouts against Copilot. They are simply reasons the alternatives exist and keep growing.
The Main Contenders at a Glance
Before touching a keyboard, it helps to see the field. The table below summarizes the tools most commonly evaluated as Copilot replacements, based on vendor announcements and documentation available through 2024 and early 2025. Pricing and features change fast, so confirm current details on each vendor’s site before committing.
| Tool | Vendor | Typical Strength | Notable Setup Note |
|---|---|---|---|
| Amazon Q Developer | AWS | AWS-aware completions, security scanning | Formerly CodeWhisperer; rebranded April 30, 2024 |
| Gemini Code Assist | Large context, Google Cloud integration | Grew out of Duet AI for Developers, announced August 2023 | |
| JetBrains AI Assistant | JetBrains | Deep IDE integration | Bundled with JetBrains IDEs and their licensing |
| Tabnine | Tabnine | Privacy-first, on-prem options | Enterprise plans allow self-hosting |
| Windsurf (formerly Codeium) | Windsurf | Free tier, agentic editing | Rebranded from Codeium in 2025; verify current state |
| Sourcegraph Cody | Sourcegraph | Codebase-wide context | Strongest with a connected repository index |
| Continue | Open source | Full model control | Pairs with local models via Ollama |
If you are weighing a JetBrains-heavy shop against a VS Code one, a closer head-to-head is worth reading: github copilot vs jetbrains ai breaks down where each one pulls ahead in daily use. (GitHub Copilot)
How to Set Up Amazon Q Developer
Amazon’s entry began life as CodeWhisperer, launched in preview in June 2023, and was rebranded as Amazon Q Developer on April 30, 2024. The setup path is straightforward and works well for anyone already inside the AWS ecosystem.
- Install the extension. Search for “Amazon Q” in the VS Code marketplace or the JetBrains plugin repository. The old CodeWhisperer listing now points to the same product.
- Sign in with an AWS Builder ID or IAM Identity Center. The Builder ID route is the fastest for individuals; enterprise users should go through Identity Center so permissions are managed centrally.
- Pick your authentication method and confirm the region. This matters for data handling and for which features light up.
- Open the Q panel and run a first prompt. Ask it to explain a function in your open file. If you get a coherent answer, the connection is live.
- Enable or disable the security scanning feature deliberately. It is useful, but it adds noise if you turn it on before you have tuned your linting.
The common mistake here is signing in with a personal AWS account that has no permissions configured, then wondering why completions stall. Use the Builder ID unless your organization has told you otherwise.
How to Set Up Gemini Code Assist
Google’s offering traces back to Duet AI for Developers, announced at Google Cloud Next in August 2023, and later folded into the Gemini brand. Setup is similar in shape to Amazon’s but leans on a Google Cloud project.
- Install the Gemini Code Assist extension in VS Code or your JetBrains IDE.
- Sign in with a Google account and, for team use, select the correct Cloud project.
- Grant the required API permissions to that project. Skipping this is the number-one cause of a silent failure.
- Set your context preferences. Decide whether the assistant can read your whole workspace or only open files.
- Test with a real task, such as generating unit tests for an existing module, rather than a toy prompt.
For developers who also work across multiple repositories and hosting platforms, it is worth understanding how your source control choice interacts with these tools. A comparison like github vs gitlab 2026 helps clarify which integrations you can expect out of the box.
How to Set Up JetBrains AI Assistant
If your daily driver is IntelliJ, PyCharm, or WebStorm, the native assistant has an obvious advantage: it lives where you already are. Setup is nearly invisible.
- Update your IDE to a recent version. The assistant ships with newer builds.
- Open Settings and find the AI Assistant entry. Log in with your JetBrains account.
- Check your license tier. Some AI features are bundled; others require an add-on.
- Configure the commit message and documentation generators if you want them, since these are the features people either love or immediately disable.
- Set your privacy preferences before you start pasting proprietary code.
The practical tip: turn off any feature you will not use in the first week. Assistants that generate commit messages when you did not ask for them create more friction than they remove.
How to Set Up Tabnine for Privacy-First Teams
Tabnine’s pitch is control. Enterprise plans support self-hosting, which makes it a genuine option for organizations that cannot send code to a third-party cloud.
- Install the plugin for your IDE from the vendor’s marketplace listing.
- Create an account or connect to your organization’s instance.
- Choose your model hosting model: cloud, on-premises, or a hybrid arrangement.
- Configure team-wide settings through the admin console so individual developers do not each reinvent the wheel.
- Run a pilot with five to ten developers before rolling out broadly. Privacy tools are judged on trust, and trust is earned in small batches.
The mistake to avoid is treating a self-hosted deployment as a pure plugin install. It involves infrastructure, and someone on your team needs to own it.
How to Set Up Windsurf and Sourcegraph Cody
Windsurf, which rebranded from Codeium in 2025, leans into agentic editing, where the assistant proposes multi-file changes rather than single-line completions. Setup follows the familiar pattern: install the editor or extension, sign in, and grant workspace access. Verify the current product shape on the vendor’s site, since the rebrand changed packaging.
Sourcegraph Cody rewards teams that already index their code. The setup sequence is install the extension, authenticate against your Sourcegraph instance or the hosted service, connect repositories, and then let the index build. Do not judge Cody on day one; its value shows up once the index is complete and it can answer questions across the whole codebase.
How to Set Up Continue With Local Models
For developers who want full control and zero per-seat cost, Continue is the open-source route. It is also the most hands-on.
- Install the Continue extension in VS Code or JetBrains.
- Install a local model runner, typically Ollama, and pull a code-capable model.
- Edit the configuration file to point Continue at your local endpoint or a hosted API.
- Map models to roles: one for autocomplete, one for chat, one for edits. Using a single model for everything is a common beginner error.
- Test latency before committing. A local model that takes four seconds per completion will lose to a cloud tool every time.
This path suits teams with strict data rules or developers who enjoy tinkering. It is not the fastest route to productivity, and pretending otherwise helps nobody.
Configuration Mistakes That Waste Your First Week
Across every tool above, the same handful of missteps show up. Watching for them saves real time.
- Leaving every feature on. Assistants ship with chat, completion, tests, docs, and commit messages all enabled. Turn off what you will not use.
- Ignoring context limits. Feeding a giant monorepo into a tool with a small context window produces vague answers. Scope the workspace.
- Skipping the privacy review. Know where your code goes before you paste a client’s proprietary logic into a chat pane.
- Judging on day one. Most tools improve as they see your patterns and as indexes build.
- Not telling the team. Mixed configurations across a team create confusing diffs and inconsistent code style.
If you are setting up an editor alongside these assistants, a focused walkthrough helps: VS Code extensions Cursor setup 2026 covers how extension choices interact with an AI-first editor.
Matching the Tool to the Job
There is no universal winner, and the honest answer depends on constraints.
| Your situation | Sensible first pick |
|---|---|
| Solo developer, tight budget | Windsurf free tier or Continue with a local model |
| AWS-heavy backend team | Amazon Q Developer |
| Google Cloud shop | Gemini Code Assist |
| JetBrains-only team | JetBrains AI Assistant |
| Regulated industry, no cloud | Tabnine self-hosted or Continue |
| Large monorepo, many questions | Sourcegraph Cody |
A useful exercise: list your three non-negotiables, whether that is price, privacy, or IDE support, and eliminate anything that fails one. The shortlist is usually two tools, and a week-long trial settles it.
Conclusion
Switching away from GitHub Copilot is less about finding a better tool and more about finding a better fit. The setup steps across Amazon Q Developer, Gemini Code Assist, JetBrains AI Assistant, Tabnine, Windsurf, Cody, and Continue are all variations on the same pattern: install, authenticate, scope the context, and prune the features you will not use. The developers who get value fastest are the ones who configure deliberately instead of accepting defaults and hoping for the best. Pick one alternative, give it a focused week on real work, and let the results decide.
Frequently Asked Questions
Is there a free alternative to GitHub Copilot?
Yes. Windsurf (formerly Codeium) has offered a free tier, and Continue is open source and free to run, though a local model still costs you hardware or compute time. Google and AWS have also offered free usage tiers for individual developers at various points. Confirm current limits on each vendor’s site, because free tiers shift.
Do these alternatives work in JetBrains IDEs?
Most do. Amazon Q Developer, Gemini Code Assist, Tabnine, Continue, and JetBrains AI Assistant all support JetBrains products. Sourcegraph Cody has also supported JetBrains IDEs. Check the specific plugin listing for your IDE version, since support sometimes lags behind the newest release.
Can I keep my code from leaving my machine?
Yes, with the right setup. Continue paired with a local model through Ollama keeps inference on your hardware. Tabnine’s self-hosted enterprise option keeps processing inside your infrastructure. Cloud-based tools generally send code to vendor servers, so read the privacy documentation before assuming otherwise.
How long does setup usually take?
For a cloud-based assistant with an extension and a sign-in, expect fifteen to thirty minutes, including authentication and a first test prompt. Self-hosted or local-model setups take longer, often an afternoon, because you are managing infrastructure and configuration files rather than just installing a plugin.
Will switching break my existing editor setup?
Rarely, but conflicts happen. Two completion extensions running at once can fight over the same keystrokes, producing doubled suggestions or lag. Disable the old assistant before enabling the new one, and restart the IDE after installing anything that hooks into the editor’s completion system.
Which alternative is best for a large team?
It depends on where your code lives and what your compliance team will approve. AWS-centric teams tend to land on Amazon Q Developer, Google Cloud teams on Gemini Code Assist, and regulated organizations on Tabnine or a self-hosted setup. Run a pilot with a small group before buying seats for everyone.
