Predibase Finops
FinOps view of Predibase spend. Predibase bills three usage meters: serverless inference per token (scaled by model size), batch inference at a flat per-million-token rate, fine-tuning (training) per token of training data scaled by base-model size, and dedicated deployments per GPU-hour by accelerator. LoRAX multi-LoRA serving packs many adapters onto one GPU, reducing dedicated serving cost versus one deployment per fine-tuned model.
Predibase Finops is the FinOps profile for Predibase on the APIs.io network, aligned with the FinOps Foundation Framework.
It defines 4 billable meters, billed in USD, on a monthly cycle, and pricing category usage-based.
The profile maps 8 FOCUS columns for cost-allocation reporting.
Tagged areas include AI, LLM, Fine-Tuning, Inference, and LoRA.
Framework Alignment
Charge Categories
FOCUS Columns
Meters
Sources
- https://predibase.com/pricing
- https://docs.predibase.com
- https://focus.finops.org/focus-specification/v1-3/
Work with this as data
Every finops artifact here is available over the APIs.io API and to AI agents over MCP.
MCP server
One button, every client — Claude, Cursor, VS Code and the rest.
https://apis.io/mcp
Tools for finops
4 MCP tools reach this
find_finopsBrowse and filter every finops artifact in the catalog.apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.resolveTurn a domain, URL or GitHub org into the provider it belongs to.find_cohortsEvery scored population of providers in the catalog.
Call it yourself
curl for this page
curl "https://apis.io/api/v1/finops/predibase-finops"curl "https://apis.io/api/v1/finops?limit=25"Discovery needs no key. Ratings and market analysis are Pro.