Hey everyone, I’ve been absolutely loving Cursor Pro for the past couple of months—it’s genuinely changed my coding workflow, especially when building out integrations for our martech stack. The agentic features are incredible for debugging API connections and writing scripts for lead routing.
However, I’ve hit a bit of a snag that I wanted to bring to the community. I feel like my token credits are vanishing *way* faster than I anticipated. I know it uses the Anthropic model behind the scenes, and I expected usage, but my experience has been… intense. I’ve been tracking my sessions in a spreadsheet (you know me 😅), and I’m burning through roughly 2-3 times the credits I had loosely budgeted for.
Here’s a breakdown of my typical usage patterns that might be contributing:
- **Long, conversational debugging sessions:** I’ll often have Cursor open and ask it to help trace an issue through our marketing automation platform’s webhooks. This involves multiple back-and-forths, with me providing error logs, code snippets, and asking for iterative fixes.
- **Codebase-wide queries:** I frequently use "Chat with Workspace" to ask questions about our existing code structure when adding new CRM features. For example, “How is the lead scoring algorithm currently implemented across the repo?” These queries seem to consume a significant chunk.
- **Agent mode for repetitive tasks:** Letting the agent run to refactor a set of similar functions or update configuration files seems to be a major credit drain, though it’s incredibly effective.
My main questions for you all are:
- Has anyone done a formal or informal benchmark comparing token usage for similar tasks in Cursor vs. using the Claude API directly or another AI-powered IDE?
- Are there specific features or usage styles you’ve found to be unexpectedly “expensive”?
- Conversely, have you discovered any settings or approaches that help conserve credits without drastically reducing functionality? For instance, does disabling certain features per-project make a noticeable difference?
- How does your token burn rate compare to mine? I’m mostly working on JavaScript/Node.js for integrations and some Python for analytics pipelines.
I’d love to pool our data and see if there are best practices emerging. Maybe we can figure out the most credit-efficient way to leverage this amazing tool! I’m particularly curious if the cost-per-outcome still feels justified once you track it meticulously. For me, the productivity boost is huge, but I want to make sure I’m using it sustainably.
Test everything, trust nothing
Oh, the classic spreadsheet defense. I love it. Look, I think you've already diagnosed the problem with your own bullet points: "long, conversational debugging sessions" and "codebase-wide queries." You're basically using a high-powered, expensive LLM as a glorified rubber duck, and then you're surprised the meter is running?
The "Chat with Workspace" feature is a notorious token black hole. Every time you invoke it, it's slurping up context from who-knows-how-many files, shoving them into the prompt, and billing you for the privilege. It's incredibly convenient, sure, but you're paying for that convenience directly with credits. The longer your sessions go, the more the conversation history itself balloons, making each subsequent call even more expensive.
Have you considered that maybe the "agentic workflow" is the problem, not the budget? It's encouraging this iterative, chatty style of development that's fantastic for velocity but horrific for token economy. Sometimes the old way - actually reading the code yourself first - is still the cheaper option.
🤷