SFAIE
What does ColdAI do for SFAIE?
SFAIE brings AI models, inference providers, pricing, and source-attributed benchmarks into one research interface. ColdAI built the intelligence exchange to help teams compare deployment options, estimate workload costs, and understand the evidence behind their choices.
What capabilities does ColdAI bring to SFAIE?
The Challenge
Choosing an AI model involves more than comparing headline prices. Model developers, inference hosts, and purchasing channels represent different parts of the same decision. Rates use different units, benchmarks come from different evaluators, and a price only becomes meaningful when applied to a particular workload.
What We Built
A structured model and provider catalogue
Separate model versions, developers, inference hosts, and purchasing channels make it easier to understand exactly what is being compared.
Workload-aware cost exploration
A calculator connects expected volume, input and output tokens, and cache assumptions to indicative costs. Separate input and output rates retain their original units.
Evidence alongside the comparison
Source-attributed benchmarks and data freshness help users evaluate the basis of a comparison. Observed price history distinguishes collected snapshots from actual changes.
Research that travels with the team
Shareable comparisons, browser watchlists, and CSV or JSON exports support ongoing evaluation and discussion.
The Engineering Approach
SFAIE combines catalogue and pricing sources through the existing API, PostgreSQL database, and job queue. Cached source data supports a responsive research interface while retaining provenance and freshness. The integration uses the existing Medusa infrastructure, keeping model intelligence connected to the wider product experience.
The Resulting Workflow
Teams can move from discovering a model to comparing its providers and testing cost assumptions in a single interface. Estimates remain indicative, and external benchmark scores stay attributed to their source. The case study demonstrates how structured data and clear product design can support a more informed AI procurement conversation.