AUEB vs AWS Cost Explorer
AWS Cost Explorer tells you how much you spent on cloud services. AUEB tells you whether your AI features will ever make money. Cloud billing ≠ AI economics.
| Dimension | AUEB | AWS Cost Explorer |
|---|---|---|
| What it tracks | AI feature profitability per interaction | Cloud infrastructure spend by service |
| Output | Margin per AI interaction, break-even analysis | Monthly cloud bills with service breakdown |
| Audience | Product leaders, CTOs, CFOs | Cloud engineers, DevOps, FinOps |
| Question answered | "Will this AI feature ever be profitable?" | "How much are we spending on AWS?" |
| AI-specific? | ✅ Built specifically for AI/LLM economics | ❌ General cloud cost tracking |
| Cost | Free (richardewing.io/tools/aueb) | Free (included with AWS) |
| Granularity | Per-feature, per-interaction cost modeling | Per-service, per-region billing |
| Board-ready? | ✅ Produces investment-grade analysis | ❌ Infrastructure-focused dashboards |
Why AWS Cost Explorer Fails at AI Economics
1. The Token Telemetry Gap
AWS Cost Explorer shows you aggregate billing for AWS Bedrock or EC2 instances. It has absolutely no awareness of token telemetry. It cannot associate individual model request/response payloads with customer IDs, feature names, or pricing tiers. Without AUEB to bridge this gap, you cannot identify which customers or features are driving your variable costs.
2. Shared RAG Infrastructure Bloat
Retrieval-Augmented Generation (RAG) involves memory-resident vector databases (e.g., OpenSearch, Pinecone), document processing pipelines, embedding models, and caching layers. AWS groups these costs under broad categories like compute, storage, and networking. Only AUEB breaks down the multi-layered cost of RAG pipelines to compute the true marginal cost of an AI-generated answer.
3. Prompt Cache Blending
Modern LLM APIs offer steep discounts (up to 80%) for prompt cache hits. AWS Cost Explorer reports the blended monthly total, making it impossible to determine if caching is working effectively across different feature releases. AUEB models dynamic cache allocation, letting you isolate cache efficiency from baseline query costs.
4. Failure to Track Retry Loops
When an LLM call fails or returns malformed JSON, modern agentic systems automatically initiate retry loops. These loops consume tokens rapidly, compounding costs invisibly. AWS Cost Explorer sees only the final bill. AUEB explicitly factors in retry inflation, identifying code loops that are draining your margins.
The Verdict
They answer completely different questions. AWS Cost Explorer is essential for managing cloud bills. AUEB is essential for knowing whether your AI product strategy is viable.
You need AWS Cost Explorer to know your inputs. You need AUEB to know your outputs. The gap between them is where AI products die.
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Richard Ewing — AI Economist & Capital Auditor