r/costlyinfra

We're nowhere near capacity, so here's a stupid deal, while also telling you what it's all about.

Unlimited AI inference for $6/month.

Model:qwen3.6-35b

\- No token caps

\- No request caps

\- OpenAI-compatible endpoint

\- \~100 tokens/sec average right now

\- 2 active generations at once

\- 128K context (maybe 256 soon)

\- strong growing discord community

\- events with prizes

\- for normal prompts, TTFT is insane

If you're building an AI embedded SaaS, shipping side projects, or just burning through prompts, this is probably the cheapest unlimited inference endpoint you'll find.

So we have about 99% uptime now. Finally figured out how to make qwen3.6-35b-3a not loop, and we've had some pretty big blunders along the way. It's all at yolo-auto.com .

How are you guys are handling all the expense the ai model api costs..... it seemed small in the beginning... but as time goes, it is burning all the money...

I just wanted to know how much it actually costs for OpenAI and Claude to host the LLM models and how much they are subsidizing, and whether it is actually feasible for them to host models at the prices that they are. I just wanted to know how much it actually costs or what sort of calculations should we do to understand the unit economics of generating tokens. I know that most of what OpenAI and Claude might be doing are some optimization techniques that are not known to the public. But can you help me to understand calculations by explaining it for DeepSeek hosted on some cloud platform like AWS? I just want to understand what sort of calculations I must do. When I started to do it, I got confused about where I should start from. And what things should I consider?

Burned through my entire GPT-5.5 quota in under 2 hours.

Good thing DeepSeek V4 has my back.

Turns out the best token optimization strategy is just... being good enough at programming to get it right the first time.

Fewer iterations = fewer tokens = less money burned.

The irony?

The expensive model made me lazy — sending half-baked code and letting AI fix it.

DeepSeek forces me to think before I type. Probably healthier in the long run.

I built a learning tool to see how cost changes based on reasoning, caching, deployment, EU/US compliance etc.

It’s a learning tool, not a quote.

There are other factors that can impact the cost.

If you see any errors, something is missing or factually incorrect, please let me know.

Also it doesn’t mean that the quality will be the same. I tried to pick more or less comparable models but of course DeepSeek Flash is not the same quality as Opus 4.8

It’s rather to understand that if you run classification or summarisation task with Opus and you could do it with DeepSeek, you waste a lot of money

Had Qwen model running with OpenRouter for weeks. Cost maybe 0.20€ a day. Everything fine. Then one day I check my usage and it's 3€. In a single day. Provider silently adjusted their pricing.

I know you would say that 3€ is not much but I don't have money to afford that because I am still going to school and I don't have a stable income.

I spent the next two hours comparing models, rewriting configs, testing DeepSeek alternatives I found. That's when I realized I had zero visibility into what anything cost until I manually check the openrouter website.

So I built a system where Python script runs on a schedule, hits the OpenRouter API with my key, writes a daily .md file straight into my Obsidian vault with cost breakdown. My AI agent watches that file and alerts me if any line item jumps over a threshold I set. However no dashboard or database, just markdown files and one API call per day.

It works but it feels fragile. What do you use to track AI API costs? My setup is too manual and I'd rather use something that catches spikes before they turn into horrible costly surprises.

Kafka → ClickHouse is the default pattern for OTel pipelines, and for org-wide streaming with replay and many consumers it's a great fit. But for a lot of single-sink observability setups, it's a cluster you're babysitting for no reason.

This post compares where the Kafka layer does real work vs. where you can drop it. It also checks what processing the Collector can or can't do alone (stateful dedup, enrichment-conditional filtering, dynamic sampling, etc.)
https://www.glassflow.dev/blog/opentelemetry-to-clickhouse-do-you-need-kafka?utm_source=reddit&utm_medium=socialmedia&utm_campaign=reddit_organic

Curious what others run:

1. Kafka buffer,
2. straight from the collector, or
3. a lighter processor in between

Leave your comments below, I'd like to discuss the options and understand what folks are using these days!