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Estimate GPU needs, AI costs, and cloud versus on-prem TCO.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "infra-advisor-mcp" yet — see the docs or source repo.
Estimate the GPU count, training time, and total cost to train a 7B-parameter model with these assumptions: 2 trillion tokens, BF16 precision, A100 80GB GPUs, deployed in the cloud.
Returns the required GPU configuration, estimated training duration, and itemized plus total cost estimates.
Compare the 3-year TCO of running this AI inference workload in the cloud versus on-prem: 500,000 daily requests, peak concurrency of 300, 13B model, target latency under 800 ms, including hardware, operations, power, and scaling costs.
Outputs a 3-year TCO comparison for cloud and on-prem options, with the main cost drivers explained.
Create an inference capacity plan for a customer support LLM application: 34B model, average 1,500 input tokens and 300 output tokens, 100 requests per second, and 99.9% availability. Estimate the required GPU resources and monthly operating cost.
Provides a recommended inference cluster size, required redundancy, and estimated monthly operating costs.
Estimate GPU needs, training costs, inference spend, and AI infrastructure TCO.
Parse multi-cloud IaC and generate real-time cost estimates and comparisons.
Analyze Azure Data Factory costs, detect waste, and recommend optimizations.
Track AI usage, costs, logs, and debug model interactions across apps.
Query normalized usage, cost, and dashboard data across OpenAI and Anthropic.
View tenant-scoped AI credit usage data for admin users.