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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.
Compare AI models across providers by cost, performance, and capabilities.
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.
View tenant-scoped AI credit usage data for admin users.
Count tokens, estimate costs, optimize prompts, and compare LLM pricing.