Technology

Wasabi AiR

Wasabi Technologies' AI-augmented object storage tier — facial recognition, speech-to-text, OCR, and logo detection run inline as objects ingest, generating per-second searchable JSON metadata. Flat $6.99/TB/month, zero egress, AI compute absorbed into the storage rate.

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Summary

What it is

Wasabi Technologies' AI-augmented object storage tier — facial recognition, speech-to-text, OCR, and logo detection run inline as objects ingest, generating per-second searchable JSON metadata. Flat $6.99/TB/month, zero egress, AI compute absorbed into the storage rate.

Where it fits

The first non-hyperscaler "intelligent storage" tier. Distinct from AWS Rekognition / S3 Vectors (separate service, separate billing) by collapsing AI tagging into the bucket itself — no egress to a tagging API, no metadata-write-back pipeline.

Misconceptions / Traps
  • AiR is a tier of Wasabi, not a separate product — bucket-level opt-in, not account-level.
  • Inference quality is service-grade (facial / OCR / speech), not custom-model territory. For domain-specific tagging, the standard out-of-bucket pipeline still applies.
  • Free egress applies only when downloading source media; metadata index queries follow standard pricing.
Key Connections
  • is_a Wasabi — packaged as a storage tier on the Wasabi platform
  • enables RAG over Structured Data — searchable per-second metadata supports agent retrieval
  • solves High Cloud Inference Cost — bundled inference, not per-call billed
  • scoped_to Multimodal Object Storage

Definition

What it is

An AI-augmented object storage tier from Wasabi Technologies, launched 2025–2026, that runs facial recognition, speech-to-text, OCR, and logo detection inline against media as it ingests into Wasabi buckets — bundling what was historically a separate AI-tagging service into the storage subscription. Generates a per-second searchable JSON metadata index attached to each object. Priced at a flat **$6.99/TB/month** with zero egress and the AI compute absorbed into the storage rate.

Why it exists

Multimodal AI workflows traditionally require pulling petabytes out of cold storage, running them through a third-party tagging API, and writing the metadata back — paying egress twice and stitching the pipeline manually. Wasabi AiR collapses that into the storage tier itself, eliminating egress, the per-API charge, and the pipeline glue. The result is the first **"intelligent storage"** offering at non-hyperscaler pricing.

Primary use cases

Sports/media/entertainment archives needing scene-level search, multimodal RAG pipelines over video and audio corpora, training data preparation for vision and speech models, compliance archives that must support second-by-second discovery for litigation hold.

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