IMHO, it doesn't, but I have changed the title to avoid any confusion.
302 karma · joined October 26, 2019
I discovered the hard way that GKE's default behavior is to ingest 100% of this into Cloud Logging with no rate limiting. My bill jumped nearly 1000% before alerts caught it.
Infrastructure (Compute): ~$140 (R$821 BRL) Cloud Logging: ~$1,300 (R$7,554 BRL)
Ratio: Logging cost 9.2x the actual servers.
I fixed the loop and paused the `_Default` sink immediately.
I opened a billing ticket requesting a "one-time courtesy adjustment" for a runaway resource—standard practice for first-time anomalies on AWS/Azure.
I have been rejected twice.
The latest response: "The team has declined the adjustment request due to our internal policies."
If you run GKE, the `_Default` sink in Log Router captures all container stdout/stderr.
There is NO DEFAULT CAP on ingestion volume which is an absurd!
A simple while(true); do echo "error"; done can bankrupt a small project.
Go to Logging -> Log Router. Edit _Default sink.
Add an exclusion filter: resource.type="k8s_container" severity=INFO (or exclude specific namespaces).
Has anyone successfully escalated a billing dispute past Tier 1 support recently?
It seems their policy is now to enforce full payment even on obvious runaway/accidental usage.
> What bugs me most is that many of those 340M tokens feel wasteful? Like the LLM will use 50k tokens exploring dead ends before finding a solution that could have been expressed in 5k tokens. The productivity gain is real, but it feels like I'm paying 10x more than what should be "fair" for the actual value delivered.
Key Findings from Your Cursor Usage Here are the most interesting insights from your usage data:
Cost Summary
Total Cost: $928.45 over 70 days
Average per Request: $0.06
Cost per Task (Request): Ranges from $0.00 to $2.78, with 65.7% costing under $0.05
Projected Monthly Cost: ~$416 (based on average daily spend of $13.86)
Request Patterns
Requests per 5 Hours: Average 70.7, ranging from 1 to 451
Average Time Between Requests: 6 minutes 33 seconds
Median Time Between Requests: Just 13 seconds (shows bursts of activity)
Peak Activity: 1-2 PM (10.4% of all requests at 1 PM)
Busiest Day: Saturday with 21.7% of requests
Token Efficiency
Average Tokens per Request: 83,371 tokens
Median Tokens per Request: 38,342 tokens
Average Output per Request: 876 tokens
Cache Hit Rate: 88.8% (excellent! saves money)
Cost per 1,000 Tokens: $0.0009 (very efficient due to caching)
Cost per 1,000 Output Tokens: $0.14
Notable Stats
Most Expensive Request: $2.78 using 6.8M tokens (mostly cached)
Total Hours of Active Usage: 1,692 hours (~9 requests/hour)
Most Used Models: claude-4.5-sonnet-thinking, claude-3.5-sonnet, and others
Your cache hit rate of 88.8% is excellent and is saving you significant costs! Without caching, your costs would be much higher.
$638/6 weeks won't make me broke, but here's my main issue: for me it's about the value-to-token ratio feeling off.
What bugs me most is that many of those 340M tokens feel wasteful? Like the LLM will use 50k tokens exploring dead ends before finding a solution that could have been expressed in 5k tokens. The productivity gain is real, but it feels like I'm paying 10x more than what should be "fair" for the actual value delivered.
Maybe this is just the current state of AI coding - the models need that exploration space to get to the answer. Or maybe I need to get better at constraining the context and being more surgical with my prompts.
For me as a founder, it's less "can I afford this" and more "does this pricing model make sense long-term?" If AI coding becomes a $5-6k/year baseline expense per developer, that changes a lot of unit economics, especially for early-stage companies.
Are you finding Claude Code Max more token-efficient for similar tasks, or is it just easier to stomach because the billing is flat?
I'm excited to introduce codeplot, a tool I've been working on that's designed to revolutionize the way we interact with data visualizations in Python.
What is codeplot?
codeplot is an interactive spatial canvas that allows for dynamic data exploration. It's built to move beyond static images and fixed layouts, giving your data the interactive, engaging platform it deserves. With codeplot, you can easily integrate live data visualizations directly from your Python code or REPL into a flexible, interactive canvas hosted at codeplot.co.
Key Features:
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Install codeplot with pip, connect to a room, and start plotting right away. We even support usage in Jupyter Notebooks for an integrated development experience.
Docker Support:
For those who prefer self-hosting, codeplot is Docker-ready, allowing you to run your own server and client locally with ease.
Join Our Community:
We're building a community of data enthusiasts and professionals on Discord. It's a place to share insights, ask questions, and collaborate on data visualization projects.
I'd love to get your feedback, suggestions, and hear about the visualizations you create with codeplot. Let's make data exploration more interactive and engaging together!
Thanks for checking out codeplot!
– @antl3x
I'm excited to introduce codeplot, a tool I've been working on that's designed to revolutionize the way we interact with data visualizations in Python.
What is codeplot?
codeplot is an interactive spatial canvas that allows for dynamic data exploration. It's built to move beyond static images and fixed layouts, giving your data the interactive, engaging platform it deserves. With codeplot, you can easily integrate live data visualizations directly from your Python code or REPL into a flexible, interactive canvas hosted at codeplot.co.
Key Features:
Dynamic Visualization: Say goodbye to static charts. Visualize your data in real-time on an interactive canvas. Easy Integration: Seamlessly plot from Python with just a few lines of code. Varied Visualizations: Support for a wide range of data representations, from basic charts to complex widgets. Flexible Layouts: Customize your data exploration space with draggable and resizable plots. Open Community: Whether you're a data scientist or a hobbyist, codeplot is designed for anyone passionate about data. Getting Started is Simple:
Install codeplot with pip, connect to a room, and start plotting right away. We even support usage in Jupyter Notebooks for an integrated development experience.
Docker Support:
For those who prefer self-hosting, codeplot is Docker-ready, allowing you to run your own server and client locally with ease.
Join Our Community:
We're building a community of data enthusiasts and professionals on Discord. It's a place to share insights, ask questions, and collaborate on data visualization projects.
I'd love to get your feedback, suggestions, and hear about the visualizations you create with codeplot. Let's make data exploration more interactive and engaging together!
Thanks for checking out codeplot!
– @antl3x (Creator of codeplot)
If you is tired of spending endless hours wrestling with CSV files, trying to make sense of your data maybe UING can save you ton of hours. No complicated setup or installations required. Just select your CSV file, and you're good to go.
With UING, you can ask questions about your data in plain English.
Wondering about the "total sales amount for each product category"? Just ask, and UING's will show the results.
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Would love some community feedback.
Thanks