We're a small startup (6 devs) and everyone's using a different AI coding assistant. It's getting chaotic, and I'm not convinced any of them are actually saving net time once you account for setup, context switching, and false confidence in generated code.
I've spent the last two weeks trying to standardize on one tool. My shortlist is Copilot (GitHub), Continue, and Cursor. The goal is simple: which one, in practice, helps a small, fast-moving team ship correct code faster? Not which has the most features.
Here’s what I’m weighing:
* **Context & “Knowing” Our Codebase:** This is huge. We need the assistant to understand our specific patterns and internal libraries. Continue’s open-source model and local setup seem strong here, but does it work reliably day-to-day?
* **Workflow Integration:** We live in VS Code. A smooth, non-disruptive experience is critical. Copilot is frictionless, but sometimes feels like a black box. Cursor rethinks the editor itself—is that a productivity boost or a distraction?
* **Cost vs. Value:** With a tight budget, the pricing models matter. Copilot is a per-user seat. Continue is essentially free (self-hosted). Cursor has a team plan. The cheapest isn't the best value if it slows us down.
I’m especially curious about real-world pitfalls. For example:
- Does Copilot’s lack of deep project context lead to more generic, useless suggestions?
- Does Continue require constant tinkering with configs and models to stay effective?
- Does Cursor’s agent-like approach (e.g., “plan” mode) actually solve complex tasks, or just create more steps?
If you’ve made a choice for your team, what was the deciding factor? Concrete examples of time saved (or wasted) would be incredibly helpful.
—K
Keep it real
I'm a finops lead at a 30-person SaaS startup, and we standardized on Copilot for our Python/React/Node stack about 18 months ago, after a similar internal debate.
* **Knowing your codebase**: None of them truly "know" it out of the box. Continue lets you attach a local model (like Llama 3.3 70B via Ollama), which gives the best *potential* for deep, specific context, but you're now managing a model server and dealing with variable inference speed. Copilot and Cursor rely on cloud models with indexed context from open files. In practice, Copilot's context window felt about 70% accurate for our internal patterns; Cursor's chat-with-repo is slightly better but adds latency.
* **Workflow integration**: Copilot wins on pure frictionlessness. It's a VS Code extension that just works. Cursor is a fork of VS Code, so you're committing to a whole new editor. It offers powerful agent-like features (e.g., "write a test for this"), but the mental shift cost for our team was about 2-3 days of lost productivity. Continue sits in the middle as an extension but requires more manual context management.
* **Real cost**: Copilot is $10/user/month flat via GitHub Teams. Cursor's Pro plan is $20/user/month for the team tier. Continue is "free" if you self-host models, but factor in the engineering time to set up and the GPU/compute cost for a local model. For a 6-person team, that's $60/mo for Copilot, $120/mo for Cursor, or ~$40-80/mo in extra cloud bills (plus 5-10 hours of setup) for a decent self-hosted Continue setup.
* **Where it breaks**: Copilot can hallucinate library APIs when your context isn't loaded. Cursor's agent can get stuck in long, unproductive loops requiring manual interruption. Continue's performance is entirely dependent on the local model you choose; the default smaller models often produce trivial or outdated code suggestions.
My pick is GitHub Copilot for your scenario. It's the lowest friction path to net-positive time savings for a small, fast-moving team living in VS Code. If deep, offline codebase understanding is non-negotiable, tell us your backend language and if you have a spare machine with 16GB+ RAM to dedicate to a model server.
- elle