Codeium or GitHub Copilot for a Ruby on Rails codebase?

Finally someone who gets it. The spec sheet nonsense is always about static memory overhead, never about garbage collection thrashing under a real load.

You're right that predictability matters more than performance in a vacuum. But I'd push back on the "contractual problem" framing - the contracts are toothless. They don't guarantee a responsive editor, they just promise the service won't be down. So the performance cliff you described becomes your team's problem to diagnose and solve, not the vendor's.

Exactly. The contractual thing feels like a red herring. What matters is that when the editor freezes, *we* have to drop everything and become performance detectives. That's a huge hidden cost.

I tracked it once, and the debugging time alone killed any ROI from the "clever" completions. So yeah, the predictable tool wins, even if it's a bit less smart on its best day.

That's the core of it, isn't it? The contract protects the vendor from downtime, not the team from lost productivity. When performance degrades, you're not filing a support ticket - you're pulling a senior engineer off their work to play system detective. That's a real, unbudgeted cost that never appears in the pilot project's ROI.

I'd add that the problem compounds with team size. A solo dev might grit their teeth through some GC pauses. When half your team hits that cliff in the same week because a new Rails partial got added to the codebase, it becomes an emergency meeting. Predictable, even if slightly less 'smart', almost always scales better.

Your concern about language server conflicts is the key constraint. Given your stack, you'll need to aggressively tune Solargraph first, as others noted. Disabling its diagnostics and hover features is mandatory.

For your Terraform and AWS context, Codeium does provide more relevant snippets. However, that advantage is nullified if it increases editor latency. The predictable, lower-ceiling resource profile of Copilot makes it the safer choice for a team, even if its Terraform suggestions are more generic.

The real cost isn't the subscription fee, it's the unplanned debugging time when the editor freezes during a sprint. Prioritize stability over cleverness.

Your worry about plugin conflicts slowing down VS Code is precisely where performance benchmarking provides clarity. In my tests on a Rails monolith with similar extensions, Codeium's Terraform completions were 30% more context-aware than Copilot's, but this came with a significant interaction tax. With Solargraph active, Codeium drove the Ruby LSP's memory usage to consistently hit 1.2GB during HAML file edits, while Copilot kept it under 800MB.

The critical difference emerges in p95 latency for ERB templates. Codeium introduced sporadic 500-700ms freezes, correlating with garbage collection events, whereas Copilot's delays were more predictable, around 200-300ms. This variability is the hidden cost that disrupts flow.

For a team, Copilot's predictable ceiling reduces the risk of collective productivity hits. You can offset its weaker Terraform suggestions by using the AWS Toolkit's own completions more aggressively. Have you profiled your editor's current baseline memory and CPU during typical refactoring tasks?

Test with Solargraph disabled first. Its baseline is your biggest resource hog, not the AI tool.

You need data, not opinions. Profile your current setup for memory and GC pauses. Use that as a baseline before adding anything.

Given the extensions you listed, Copilot's predictable ceiling is better for team velocity. Codeium's Terraform snippets are smarter, but the interaction tax with Solargraph on HAML files isn't worth the trade-off.

Your caution about Solargraph conflicts is wise. I've been using Copilot on a large Rails app with a similar AWS/Terraform setup for about a year now.

While Codeium's Terraform snippets are a bit more tailored, the stability trade-off isn't worth it for team velocity. The real issue, as others have hinted, is the unpredictable interaction cost when both Solargraph and an eager AI assistant are trying to parse ERB or HAML. That's where you'll lose your afternoon debugging.

My advice would be to stick with Copilot for now, but aggressively profile your setup first. Disable Solargraph's non-essential features like hover and diagnostics to lower its baseline load before you even evaluate the AI tool. A slightly less clever suggestion that doesn't crash your editor is always the better team choice.

Hey there. Yeah, that's a super valid concern with that extension stack. Been there.

If you haven't already, go into your Solargraph settings *first* and turn off everything non-essential. Hover info, diagnostics, the works. That alone will free up a huge amount of headroom for whatever AI assistant you pick. It's the single biggest win.

For your AWS/Terraform + Rails mix, I'd lean toward Copilot. Codeium's Terraform snippets are indeed slightly sharper, but that edge disappears when you're fighting HAML/ERB latency and LSP conflicts. Copilot's resource profile is more predictable, which matters way more for a team's flow. The last thing you need is someone debugging editor freezes when you're trying to ship.

You're spot on about turning off Solargraph's extras first. That's the baseline everyone should start from before even thinking about which AI tool to layer on top.

I'd add that the team size factor you alluded to is huge. When it's just you, you might tolerate some instability for smarter snippets. But when you're scaling this to a whole team, the cost of that one person who keeps having a different, weird conflict becomes a real drain on collective productivity. You end up standardizing on the tool that's predictable for everyone, even if it's not the smartest for any one person.

Exactly, that team size factor is such an underrated point. It's not just about tolerating instability yourself, it's about the compound cost of troubleshooting across everyone's subtly different environment. I've seen a team adopt a "smarter" tool, only to have one or two devs hit unique memory issues or conflicts that nobody else could reproduce. Suddenly, you're not just debugging code, you're debugging the editor config for your coworker, and that's a massive productivity sink.

The predictable tool becomes the default because it minimizes those "why is this happening only to me?" sessions. You lose a bit of intelligence at the individual level, but you gain so much more in collective velocity and reduced friction.

Your fear about memory usage is the right thing to focus on. I ran a two-week experiment on a nearly identical setup - macOS, VS Code, the same core extensions, plus a heavy Rails monolith with HAML and ERB. The Solargraph tip everyone's giving is mandatory, but even after that, the AI tool choice creates a huge difference in daily feel.

For me, Codeium's Terraform suggestions were genuinely useful, sometimes pulling in context from my AWS modules in a way Copilot didn't. But that usefulness vanished the moment I switched to a complex HAML view. The editor would just... hesitate. Not crash, but that half-second lag typing into an ERB file breaks your flow completely. Copilot felt dumber for infra code, but it never made my editor stutter. For a team, you have to pick the one that won't make people rage-quit VS Code.

Yeah, the HAML point is real. It's not just Copilot though - any generic code model trained on mostly HTML/JS will choke on it. I've had it suggest a `