2024-2026
Cloud Benchmark Dashboard
From a manual Excel benchmark and watch (September 2024) to a production pricing-intelligence platform: automated collectors plugged into provider APIs, 7 European cloud providers and hyperscalers, 12 LLM providers. Supported a repricing review of the offer in spring 2026, then handed over to the team.

fig. 01 · The 1:1 matching module: price per physical core, size by size, provider by provider
Cloud Benchmark Dashboard is the industrialization of work that started by hand: in September 2024, competitive intelligence and pricing benchmarks lived in Excel. The production platform is where that ended up. Collectors automatically ingest data through provider APIs (Cloud Mercato only as a complement), across a scope covering 7 European cloud providers and hyperscalers (Cloud Avenue, Scaleway, Outscale, OVH, AWS, Azure, GCP) and 12 LLM-as-a-Service providers. The application features 8 analysis modules: VM Instances, Storage, GPU, Software Licensing, Bare Metal, Dedicated Clusters, LLM-as-a-Service, and OVH comparison. Data is managed via PocketBase with Clerk authentication and Sentry monitoring. In spring 2026, the platform supported a repricing review of the offer (pricing arbitrations). It was taken over by the program management team to continue after my departure: the best proof of its usefulness.
2,156 VM instances compared

fig. 02 · The VM Instances module: 2,156 instances from 7 providers, sortable and filterable column by column
The VM Instances module lines up the offers of 7 European cloud providers and hyperscalers (Cloud Avenue, Scaleway, Outscale, OVH, AWS, Azure, GCP) in one TanStack table: sorting, per-column filters, comparison and CSV export.
1:1 matching and overcommit

fig. 03 · The overcommit map: the vCPU to physical core ratio documented for each offer
The 1:1 matching module compares VM sizes at the price per physical core, and the overcommit map documents each offer's vCPU per physical core ratio.
Methodology in plain sight

fig. 04 · Overcommit sources and methodology: mechanism, ratio, explanation and confidence level per class
Each instance class carries its allocation mechanism, its overcommit ratio, a technical explanation, its sources and a confidence level (official, strong indicator, inferred, rumor). Normalizing data from 7 providers with different formats stays readable and open to challenge.
Challenges
- Industrializing a pricing watch that first lived in a hand-maintained Excel
- Aggregating and normalizing pricing data from 7+ cloud providers with different formats
- Interactive visualization of large datasets with advanced filtering and sorting
- Role-based authentication to control access to sensitive data
- Preparing a clean handover so the tool outlives my departure
Solutions
- Automated ingestion collectors plugged into provider APIs, with Cloud Mercato as a complement
- Next.js 16 architecture with TanStack Table for performant filtering and sorting
- Recharts for interactive and comparative visualizations
- Clerk for authentication and role management
- PocketBase backend for data persistence with Sentry for monitoring
Results
- Supported a repricing review of the offer in spring 2026 (pricing arbitrations)
- Successful handover: taken over by the program management team to continue after my departure
- 8 analysis modules covering all cloud offerings
- Comparison of 7 European cloud providers and hyperscalers, and 12 LLM providers
- PDF and Excel export of benchmark reports, light/dark theme, command palette
Technologies
Next.js 16 · React 19 · TypeScript · PocketBase · Clerk · Recharts · TanStack Table · Tailwind CSS · Sentry