See your whole LLM bill in one place
Right now your generative AI spend is probably split between the AWS bill (Bedrock), separate invoices from OpenAI or Anthropic, and a vector database buried in compute charges. MLCostIntel pulls it into one view, broken down by model and by application, and watches token usage for anything unusual.
Why GenAI spend is the hardest line item to explain
Spend fragmented across providers
Bedrock lands on the AWS bill. OpenAI and Anthropic invoice separately. Vector databases and embedding pipelines hide in compute. Nobody sees the total cost of a single AI feature.
Token costs have no ceiling
Instances have a maximum hourly rate. Token spend doesn't. A feature that takes off, a retry loop, or a prompt template that got longer in a refactor can double the bill in a week, and with usage-based billing you find out after it happened.
No per-application attribution
Provider dashboards show spend per API key at best. When three products share keys and environments, "which feature is driving this bill?" has no answer.
Unknown unit economics
Pricing an AI feature means knowing what a request, a conversation, or a processed document costs you. Most teams are estimating this from a monthly total divided by a guess at volume.
How MLCostIntel tracks generative AI spend
Bedrock spend is ingested from your Cost and Usage Report; OpenAI and Anthropic spend comes from their usage APIs. Everything is normalized into one cost model and attributed to models, applications, and teams.
- ✓ Cross-provider unification — Bedrock, OpenAI, and Anthropic spend in one dashboard with a common cost model
- ✓ Per-model and per-application attribution — know what each model and each product feature costs, by environment
- ✓ Token-level anomaly detection — alerts when consumption deviates from historical patterns, before the invoice arrives
- ✓ Prompt caching efficiency — see how much caching is saving you and where cache hit rates are leaving money on the table
- ✓ Full-stack GenAI coverage — vector databases, embedding pipelines, and supporting infrastructure attributed to the applications they serve
- ✓ Unit economics — cost per request and per workload so you can price features and defend margins
What teams get out of it
Dashboard for all providers
Bedrock + OpenAI + Anthropic unified instead of three separate invoices
Earlier spike detection
Token anomalies caught by daily monitoring, not month-end billing
Typical GenAI savings
From model right-matching, caching improvements, and prompt optimization
Cost attribution
Every AI feature's true cost, including embeddings and vector storage