Let's clear up a fundamental misconception right from the start: the idea that any modern, revenue-focused software tool would lock itself to a single editor is naive. Of course GitHub Copilot works with JetBrains IDEs; Microsoft and GitHub would be leaving a massive pile of money on the table if they didn't. The plugin is available for IntelliJ IDEA, PyCharm, WebStorm, and the rest of the JetBrains stable. The real discussion, which everyone seems to gloss over in their rush to embrace this "magical" autocomplete, isn't about compatibility—it's about what you're signing up for.
The integration is technically functional, but that's the bare minimum. The more pertinent line of questioning should be about the quality of that integration compared to the VS Code experience, and the long-term implications of weaving a cloud-based, proprietary suggestion engine directly into your development flow. In JetBrains, you're layering a black-box AI model from one corporate entity onto an IDE from another. You become the point where their potential incompatibilities, update schedules, and philosophical differences meet. When the Copilot plugin has a quirky behavior or a performance hiccup, who do you think will be doing the debugging? You will. And good luck getting a straight answer on responsibility from either support team.
Furthermore, while everyone is dazzled by the occasional clever code snippet, I rarely see a thorough audit of what this means for a JetBrains user in practical terms. Your IDE is now, by default, sending fragments of your code—including potentially proprietary or sensitive logic—to Microsoft's servers for processing. Have you scrutinized the data handling terms? Have you compared the latency of suggestions in a heavy JetBrains project with a large index versus the lighter-weight VS Code? The experience is not identical, and the performance hit can be nontrivial on older machines.
This leads to my core contention: the obsession with "does it work" misses the forest for the trees. It works today. The question is, what are the costs beyond the monthly subscription fee? You are investing time into learning to prompt and trust an opaque tool, you are accepting a specific vendor's ecosystem into your core workflow, and you are potentially compromising the legendary self-contained, local intelligence of the JetBrains environment for a cloud dependency. Before you install that plugin, ask yourself if you've evaluated the open-source, locally-run alternatives that don't require a monthly vig and don't ship your code context to a third party. Or if you're even aware they exist.
Just my two cents
Skeptic by default
I'm a senior data engineer at a 300-person fintech, building and maintaining the ingestion layer that feeds our dbt + BigQuery stack. We've had both VS Code and JetBrains (PyCharm/DataGrip) users on the team for the last two years, all using GitHub Copilot, so I've seen the integration play out daily.
Here is a concrete comparison based on our production usage:
1. **Suggestion Latency & Responsiveness**: In PyCharm, I observed a consistent 300-500ms delay from trigger to suggestion appearing. In VS Code, that delay is typically 150-300ms. This is most noticeable during rapid, sequential tabbing through multiple suggestions; VS Code feels more immediate and less likely to "drop" a suggestion thread mid-flow.
2. **Context Window & Project Awareness**: The JetBrains plugin can struggle with broader project context. For instance, when writing a dbt model referencing another model defined elsewhere in the project, Copilot in PyCharm often fails to suggest the correct `ref()` function call with the model name. The VS Code integration seems to have a slightly better grasp of the open workspace, leading to more relevant, multi-line suggestions for data pipeline patterns.
3. **IDE Performance Impact**: Adding Copilot to a JetBrains IDE adds a measurable memory overhead. My PyCharm instance memory usage increases by 400-600 MB with the plugin active. For VS Code, the increase is closer to 200-300 MB. On our standard 16GB dev laptops, this is a tangible difference when also running Docker containers for local testing.
4. **Pricing & Access Control**: The hard cost is the same ($10/user/month), but the management overhead differs. Our platform team found configuring seat management and enforcing policies (like blocking suggestions matching public code) simpler through GitHub's portal for all users. The JetBrains plugin requires individual user authentication per IDE, which created minor friction during onboarding and license audits.
My pick is VS Code for any data engineering work focused on analytics pipelines (SQL, Python, YAML), due to its tighter latency and lower resource footprint. If your team is heavily invested in JetBrains-specific features for, say, complex Java services, then the Copilot integration is functional but you should tell us your team's average RAM and whether you primarily need line-complete or whole-function generation.
Extract, transform, trust