When most devs first spin up an LLM project, the focus is on getting it to work. Generate text, call an API, throw together a demo. Cool, right?
But once your project hits real traffic, the hidden killer appears: token costs.
Whether you’re fine-tuning, streaming completions, or chaining agents together, token usage adds up in ways that can nuke your budget if you’re not paying attention.
At Prosperspot, we’ve been helping students and devs build affordable AI systems, and we’ve learned the hard way that cost engineering isn’t optional. It’s the difference between a viable product and a cool idea that bankrupts you.
- Input vs. Output Tokens Aren’t Equal
APIs often charge differently for input (prompt/context) and output (generated response). Example (fictional numbers):
Input: $0.02 per 1M tokens
Output: $0.06 per 1M tokens
If your system feeds giant prompts but expects small outputs, your costs scale differently than if you use compact prompts with verbose completions.
👉 At Prosperspot, we encourage devs to right-size their prompts: prune unnecessary history, cut fluff, and compress context where possible.
- Context Window Abuse
Bigger isn’t always better. Models brag about 100k+ context windows, but stuffing the entire Wikipedia into every request just means you’re paying for tokens the model never even touches.
Rule of thumb: If the LLM doesn’t need the token, don’t send it.
At Prosperspot, we’ve seen cost drops of 30–40% just by trimming context intelligently.
- Systematic Logging
You can’t optimize what you don’t measure. Every request should log:
Input tokens
Output tokens
Total cost
Latency
A simple CSV log lets you spot which workflows are draining budgets. For example, a single poorly-designed agent loop can burn 100x more tokens than a straightforward query.
Prosperspot’s dev console ships with token logging baked in, so you can see real cost footprints, not just vibes.
- Model Mix-and-Match
Not every task needs a 70B-parameter giant. Smaller, cheaper models often do 80% of the work at 10% of the price. Save the heavy artillery for when it really matters.
Prosperspot lets you swap models in pipelines easily — so a summarization step can run on an 8B model, while critical reasoning goes to a larger one.
- Batch and Cache
Batching: Group multiple queries in a single request where possible.
Caching: If you’re asking the same question 1,000 times, cache the result instead of paying 1,000 times.
Simple tricks, massive savings.
Closing: Cost Is the Real Bottleneck
The hype around LLMs focuses on raw capabilities. But the bottleneck for real-world adoption isn’t intelligence — it’s economics.
The companies and projects that survive will be the ones that engineer costs as carefully as they engineer prompts.
That’s why Prosperspot makes token cost transparency a first-class feature. Because if students, indie hackers, and startups can’t afford to experiment, the future of AI will belong only to corporations with deep pockets.
And that’s not a future worth building.
Top comments (0)