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AI Context Cost & Waste Estimator

Context Window Token Cost & Savings Calculator

Calculate how much your engineering team spends stuffing raw codebase files into AI context windows versus using active Graph RAG memory.

Team & Model Parameters

15 devs
20 queries
6 repos
💡 Did you know? Raw prompt file stuffing averages ~45k tokens per prompt. Memora’s AST sub-graph extraction isolates the exact 2 functions and 1 PR diff needed (~900 tokens), preventing 98% token waste.
Estimated Annual Savings with Graph RAG
$10,478 / year

That is $873 saved each month in LLM API tokens alone.

Token Waste Cut
98%
45k down to 900 tokens
Dev Latency Saved
16 hrs
Per month across team
❌ Raw Context Stuffing:$891/mo
✅ Memora Graph RAG Memory:$18/mo
Deploy Memora Free Starter

Why Raw Context Window Stuffing Destroys Engineering ROI

1. The "Lost in the Middle" Effect

When you feed 100k tokens of raw files into an LLM context window, attention degradation kicks in. Research shows models routinely hallucinate or ignore subtle invariants buried in the middle of long prompts.

2. Slower Developer Feedback Loops

Processing 45,000 input tokens takes 8 to 15 seconds before the first token is generated. With Memora sub-graphs, time-to-first-token drops under 1 second, keeping engineers in flow state.

3. Sub-Graph AST Extraction

Memora parses your codebases into Abstract Syntax Trees (AST). Instead of sending an entire 3,000-line file, it sends only the exact function and its direct dependency edges.