LLM Cost Optimization
See and cut your large language model spend across Amazon Bedrock, OpenAI, and Anthropic in one place. MLCostIntel attributes token costs by model, team, and application, then shows you exactly where to save.
Common LLM Cost Challenges
Spend Scattered Across Providers
Amazon Bedrock lands on your AWS bill while OpenAI and Anthropic arrive as separate invoices. With generative AI spend split across three billing systems, no one has a unified view of what large language models actually cost.
Token Waste
Bloated prompts, oversized context windows, uncontrolled retries, and premium foundation models used where a cheaper one would do. Every wasted token is billed — and at scale, token waste quietly becomes the biggest line item.
Missed Caching & Batch Savings
Cached input tokens and batch APIs are dramatically cheaper than standard requests, but most teams leave them on the table. Repeated system prompts and RAG context get billed at full price when caching would slash the cost.
No Attribution
You can't tie token spend to a team, feature, or experiment. When the generative AI bill jumps, there's no way to know which application or model drove it — so optimization is guesswork.
How MLCostIntel Optimizes LLM Costs
MLCostIntel unifies your large language model spend across Amazon Bedrock, OpenAI, and Anthropic, tracks usage down to the token, and surfaces actionable ways to save — from prompt caching to model right-sizing — each with estimated impact.
- ✓ Unified LLM cost view across Amazon Bedrock, OpenAI, and Anthropic in a single dashboard
- ✓ Token-level tracking of input, output, cached, and batch tokens — plus reasoning tokens for reasoning models
- ✓ Prompt and token optimization insights that flag bloated prompts and oversized context windows
- ✓ Prompt-caching and batch inference savings opportunities you're not yet using
- ✓ Model right-sizing and routing recommendations — move simple tasks to cheaper foundation models
- ✓ Per-team, per-application, and per-experiment attribution, with anomaly detection on token spend
How It Works
Connect Your Providers
Deploy a read-only IAM role via CloudFormation for Amazon Bedrock, and add your OpenAI and Anthropic API keys where you use them. Keys are stored encrypted. Setup takes less than 5 minutes.
We Analyze Your LLM Spend
MLCostIntel ingests Bedrock usage from your Cost and Usage Report and invocation logs, pulls OpenAI and Anthropic usage from their APIs, and normalizes everything to FOCUS 1.2 — broken down by model, provider, and token type.
Get Your Savings Roadmap
Receive prioritized recommendations — prompt caching, batching, and model right-sizing — with estimated savings, per-team attribution, and token-spend anomaly alerts to track progress over time.
Where Teams Save on LLMs
Overall LLM Spend
Unify spend across providers and eliminate token waste with prioritized recommendations
Prompt Caching
Cache stable system prompts and RAG context so repeated input tokens are billed at a fraction of the rate
Batch Inference
Route non-latency-sensitive workloads through batch APIs for roughly half the standard cost
Model Right-Sizing
Route simple tasks to smaller, cheaper foundation models and reserve premium models for hard problems