Hey folks, hoping to tap into the collective wisdom here. My team is deep into a major Java monolith migration (Spring Boot, Maven, the works) and we're evaluating AI coding assistants to help standardize across the whole dev group. We've got a 50+ developer squad, all on IntelliJ IDEA, mostly macOS with some Linux.
The big debate right now is GitHub Copilot vs Tabnine. Both have their fans internally. We ran a two-week pilot with a small group, and the initial feedback is... mixed on performance. A few devs reported IntelliJ feeling sluggish, especially on startup and during heavy indexing phases. One person's IDE memory usage ballooned to 4GB with what seemed like a normal workload.
I'm particularly worried about plugin conflicts. We already have a hefty plugin stack: SonarLint, Checkstyle, the AWS Toolkit, and of course the essential Lombok. The last thing we need is an AI assistant plugin fighting with the Java language server or causing random completion freezes.
Has anyone run both in a large, mature Java environment? Which one played nicer with a complex existing setup? I'm less interested in raw suggestion quality right now (both seem decent) and more in stability and overhead. Did you have to tweak JVM settings for IntelliJ? Any specific interactions that broke your flow?
Here's the kind of memory spike I'm talking about, from one of our pilot users' `idea.log`:
```
2024-05-15 10:23:45,987 [ 12345] INFO - #c.i.c.i.CopilotCompletionContributor - Copilot indexing started for module: core-api
2024-05-15 10:24:12,345 [ 45678] WARN - com.intellij.util.containers.ContainerUtil - High memory usage detected: 3872MB of 4096MB (94%)
```
Would love to hear your war stories and config wins.
cost first, then scale
I'm a senior dev at a fintech firm with about 120 engineers, and our primary backend is a large Java Spring monolith. We've been running both Copilot and Tabnine in different teams for over a year now to gauge their real-world fit.
Here are the concrete points based on our deployment with a similar IntelliJ/Spring/Maven stack:
1. **IDE Performance and Memory Impact:** In our tests, Copilot's local agent added 350-500MB of sustained memory overhead per IntelliJ instance, with spikes during indexing. Tabnine's lighter local model consistently held around 150-250MB. With 50+ developers, that aggregate resource tax adds up. The reported 4GB usage matches a pattern we saw with Copilot when the plugin interacted with other language server plugins, especially in early 2024 versions.
2. **Plugin Conflict History:** We've had three distinct incidents where Copilot's IntelliJ plugin clashed with SonarLint's inline annotations, causing completion freezes until one was disabled. Tabnine had one reproducible issue with a specific version of the AWS Toolkit, but their support provided a beta build within 36 hours that resolved it. With a hefty plugin stack, Tabnine's simpler completion approach proved less invasive.
3. **Enterprise Pricing and Control:** Tabnine's per-seat Enterprise plan starts around $12/user/month for the full codebase-aware model. GitHub Copilot Enterprise is priced per user but requires a GitHub Enterprise Cloud commitment, which effectively bundles it into a larger $20-30/user/month platform cost. For a 50-developer Java team not already all-in on GitHub's ecosystem, Tabnine's standalone cost is more straightforward.
4. **Network and Offline Reliability:** Our office in Asia sometimes experiences latency to GitHub's endpoints. Copilot suggestions would occasionally time out, falling back to a local cache. Tabnine's hybrid model defaults to a local model first, only calling its cloud for deeper context if enabled and if the network is reliable. This resulted in more consistent, low-latency completions in our hybrid work environment.
Given your priority on stability and integration with a complex existing setup, I'd recommend Tabnine for this specific scenario. Its architecture is simply less prone to interfering with a dense IntelliJ plugin environment. If raw suggestion quality for cutting-edge frameworks was the absolute top priority, I'd lean Copilot, but you've correctly identified that stability matters more at scale.
BenchMark
That 4GB spike isn't normal and points to a deeper integration conflict. You can't ignore it at scale.
Your plugin stack is the problem, especially SonarLint and the Java language server. Copilot's local agent will fight them for AST access. Tabnine's architecture tends to be more passive, causing fewer lock-ups.
Forget about suggestion quality for now. Run your pilot again but mandate disabling every other plugin first. Then reintroduce them one by one. You'll find the culprit in an hour. Standardizing on a broken setup for 50 devs is a productivity killer.
Least privilege is not a suggestion.
Your memory overhead numbers line up with what I've tracked across 200+ AWS EC2 dev instances. The delta isn't trivial at scale.
>Tabnine's lighter local model consistently held around 150-250MB.
That's the advertised baseline, but it assumes default config. If your devs enable the larger enterprise model for better Java context, expect 400-500MB per instance, closing the gap with Copilot.
The real cost is in the aggregated idle overhead. 50 developers * 250MB extra memory allocation = over 12GB of unproductive reserved RAM. If those are cloud workstations, that's a monthly line item with zero code output.
cost per transaction is the only metric
That's a really good point about the idle overhead adding up fast. I've only managed small teams before, so thinking about 50+ seats makes my spreadsheet brain kick in.
Can I ask how you track that "unproductive reserved RAM"? Is it just the baseline memory reported by the IDE, or are you measuring something more specific? I'm wondering if a lower-spec cloud instance might just shift the cost instead of removing it.