Your AI is overspending. We'll prove it.
Most teams building with LLMs have one problem they cannot see: they are using the wrong model for the task.
GPT-4o handling customer support queries that need a one-word answer. Claude Opus summarising a document that Haiku would process identically. Flagship models running classification tasks at 30x the necessary cost. Nobody notices because the invoice just shows a total.
GetWeka connects to your OpenAI or Anthropic usage logs and tells you exactly where this is happening, what it is costing you monthly, and the specific change that fixes it. No API keys required. No changes to your stack. Just a usage export and findings back within 24 hours.
What GetWeka detects:
Model overkill — flagship models on economy-level tasks
Prompt cache misses — identical prompts regenerating tokens on every request
Context window inflation — sending 10x more tokens than the task requires
Agent spend anomalies — runaway loops with no spend ceiling
What you get:
Pipeline-level cost breakdown by feature and model
Dollar amount on each finding — not percentages, actual monthly waste
Specific implementation step to fix each issue
Findings calibrated for both engineers and non-technical founders
The average team we analyse has 25 to 35 percent unnecessary LLM spend. Most of it is fixable in an afternoon.
Free audit for teams spending $1K or more per month on AI APIs. Results in 24 hours.
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