Transparency on AI Costs
We run three production AI systems for enterprise clients. After 12 months, we have real numbers — not estimates, not projections. Actual invoices. Here's the breakdown for a system processing ~50K queries/month.
API Costs (LLM inference): $2,840/month. This is after aggressive prompt optimization, caching, and routing smaller queries to Claude Haiku. Our original architecture would have cost $11,200/month for the same workload.
Vector Database (Pinecone): $420/month for ~2M vectors with pod-based deployment. We considered serverless but the cold-start latency was unacceptable for our SLA.
Infrastructure (AWS): $1,180/month. ECS Fargate for the API layer, ElastiCache for prompt caching, CloudWatch for monitoring. No GPUs — we're fully API-based for inference.
The Hidden Costs Nobody Talks About
Human review pipeline: $3,200/month. This is the big one nobody mentions. Our legal AI system requires human review for edge cases flagged by the confidence scorer. That means paralegals, a review UI, and an escalation workflow. This single line item exceeds our entire technical infrastructure cost.
Evaluation and monitoring: $600/month. Automated eval suites running nightly, LLM-as-judge pipelines for output quality, and custom dashboards. This is the cost of knowing whether your system is actually working.
Total: ~$8,240/month
For a system that replaced a workflow previously costing the client $67,000/month in associate billing hours. That's an 87.7% cost reduction. But the path to that number involved 4 months of optimization from an initial $23K/month architecture.
The takeaway: AI in production is cheap compared to the humans it augments. But getting to "cheap" requires serious engineering investment upfront.