Are you paying attention to your token use?

I used the Free Tier whenever. For example, I used Google Gemini without the need of the account, so I don't worry about cost. Another case is downloading it Locally like Ollama. If I used Locally, then I don't need to worry.

In other words, no credit card no problem lmao xd

I’m on Gemini Ultra for my day-to-day and it’s been a breath of fresh air to tap into as much token use as I need.

token costs hit different when you're running multi-agent setups. single model calls are manageable but once you have 3-4 agents passing context back and forth the bill compounds fast. biggest lever we found wasn't model choice — it was compressing context before it enters the pipeline. a lot of what gets stuffed into prompts is redundant or low-signal, and stripping that out before inference saved us way more than switching to cheaper models. been open-sourcing some of our compression tooling at github.com/jidonglab/contextzip if anyone's dealing with similar token budget headach**

I use OpenRouter so I don't care much about token use or rates from the tools (and am even fairly agnostic about the models themselves, which are mostly selected via orchestration anyway). But OpenRouter does give me specific tools to monitor and budget token consumption, and I find myself (for side projects) using those budgets to constrain the scope and volume of what I'm working on per day. I could probably make the dollars stretch (and should), but in the meantime I'm focusing on routing more codegen tasks to locally-hosted models (primarily via OpenCode/OMO configurations), which reduces my remote inference volume by 30-40%.

I have a pro subscription to Claude but find myself hitting the barrier and in startup mode can't afford to upgrade.

I decided to go off and solve the problem the geek way. Re-PC here in Seattle area has cast off enterprise servers. Found an old Dell R630 with solid amount of memory for reasonable. Got two older generation Tesla P4 and threw it in there. Hung it in the barn. For roughly $1500 up and running. ROI is about 7 months with me hitting it. Shorter as I add second dev. Working out the bugs.

There is definitely a trade-off. Opus 4.6 is much better than any model out there IMO. However we are getting good results with Qwen3. We are doing embedded device dev and react mobile dev. Our results are good. Saving Claude tokens for stickler problems or as adversarial QA.

LightLLM is amazing as a proxy to make switching and tracking easier. The whole setup was 2 days and now just runs in the background.

We also are getting good advantages for the farm businesses using it and backups of github and our other services.

If we had regular revenue (start selling April), Claude may become more enticing. It is also nice having a big blade for other things as well to remove dependence on services. All tradeoffs though like anything else.

This question hits differently after you've watched an agentic workflow silently burn through tokens in a retry loop.

I used to not think about token usage at all until I started building with agents. A single misconfigured workflow can trigger cascading retries where each step costs multiple LLM calls. What looked like a $0.10 task becomes a $5 surprise by the time you check your dashboard.

Now I treat token budgeting the same way I treat error handling you don't think about it until something breaks, and then you think about nothing else.

The most underrated optimization isn't model choice it's context hygiene. Keeping prompts lean and not stuffing unnecessary history into every call saves more than switching to a cheaper model ever will.

I do not monitor token usage - Yes my company provides unlimited token usage, we are in process of aligning our tools to use those tokens..

I have several accounts, so tokens... Don't tell anyone!

token costs sneak up on you fast once you start chaining agents. single LLM call? manageable. but when you have 3-4 agents passing context back and forth, the token bill multiplies in ways you don't expect until you check the dashboard.

biggest thing that helped us was compressing context between agent handoffs — you don't need the full conversation history for every downstream agent, just the relevant pieces. we ended up building a tool specifically for this: github.com/jidonglab/contextzip

but honestly even just being intentional about what goes into each prompt makes a huge difference. most people stuff everything into the context window "just in case" and that's where the waste happens.

We track token spend per agent task — the surprise was that retries on failed tool calls burn 3-4x more tokens than the actual generation. Monitoring per-request broke our assumptions about where the cost sits.