gpu-market-analyst
Pricing intelligence for GPU rentals — built for hosts who want to maximize earnings, renters who want fair deals, and spend-governance tools that need to catch agent cost blowups. Ask "what should I price my RTX 4090 at?" or "is $6/hr fair for an H100?" and get answers backed by 16+ days of historical data at 5-minute resolution. 26 GPU models tracked across vast.ai, RunPod, Lambda, io.net, and Thunder Compute. 11 tools: 8 free (live snapshots, deal finder, cross-provider comparison, free benchmark, price-sanity guardrail, GPU comparison, local collector script, feedback) + 3 Pro ($9/mo: pricing recommendations, trend analysis, full market summary). Free tier is generous: 1,000 calls/month with no card required. The first MCP server in the GPU pricing intelligence + spend-governance category.
Overview
The gpu-market-analyst MCP server delivers pricing intelligence for GPU cloud rentals, aggregating real-time and historical data from providers like AWS, GCP, Azure, and specialized GPU marketplaces. It enables querying current spot and on-demand rates, analyzing trends, predicting availability shortages, and suggesting optimal providers based on workload needs.
Key Capabilities
- Real-time pricing queries: Fetch current rental costs for specific GPU models (e.g., A100, H100) across providers.
- Historical data access: Retrieve past pricing trends to identify patterns in spot pricing fluctuations.
- Availability forecasting: Predict GPU stock levels and price spikes using time-series models.
- Pricing recommendations: Compare rates and recommend the lowest-cost provider matching capacity and region requirements.
Use Cases
- Cost optimization for ML training: Query real-time pricing for H100 GPUs, forecast availability for a 48-hour job, and switch to the cheapest provider mid-training.
- Budget planning: Analyze historical data to model quarterly GPU expenses and set alerts for price thresholds.
- Batch job scheduling: Use forecasts to schedule inference jobs during low-availability periods, avoiding premiums.
- Multi-provider arbitrage: Get recommendations to rent from Lambda Labs when AWS spots surge, ensuring uninterrupted workflows.
Who This Is For
AI/ML developers deploying large-scale models, data engineers managing compute fleets, and DevOps teams handling GPU infrastructure. It suits organizations scaling inference or training without overpaying for cloud resources.