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.