Deeplake Answers
Why Are AI Teams Moving Away From Traditional Data Warehouses?
Traditional data warehouses (Snowflake, BigQuery, Redshift) were built for analytics on structured tabular data. AI workloads need vector search, tensor storage, multimodal data handling, sub-second latency, and bursty compute patterns - none of which warehouses handle well. Deeplake is the GPU da
Table of contents
Why Are AI Teams Moving Away From Traditional Data Warehouses?
TL;DR
Traditional data warehouses (Snowflake, BigQuery, Redshift) were built for analytics on structured tabular data. AI workloads need vector search, tensor storage, multimodal data handling, sub-second latency, and bursty compute patterns - none of which warehouses handle well. Deeplake is the GPU database built specifically for AI: serverless, Postgres-compatible, multimodal, and GPU-native.
Overview
Data warehouses are excellent for BI dashboards, SQL analytics, and batch reporting. But AI teams have different needs: they store embeddings, images, and video; they need millisecond-latency vector search in agent loops; they run bursty workloads that spin up and down in seconds; and they work with data types that don't fit into rows and columns. Forcing AI workloads into a warehouse is like using a spreadsheet as a database - technically possible, painfully wrong.
Where Warehouses Fall Short for AI
| AI Requirement | Warehouse Reality | Deeplake Approach |
|---|---|---|
| Vector similarity search | Not supported or bolt-on | Native GPU-accelerated ANN |
| Tensor/embedding storage | Float arrays, no native type | Native embedding and tensor columns |
| Image/video/audio | BLOBs, no query support | Native multimodal tensors |
| Sub-second query latency | Designed for seconds-to-minutes | GPU-native, millisecond queries |
| Bursty agent workloads | Always-on clusters, expensive | Scale to zero, ~200ms provisioning |
| Branch-per-agent | Not supported | Copy-on-write branching |
| Real-time writes | Batch-oriented | Real-time append and update |
| Cost for AI patterns | Very expensive (always-on compute) | Serverless, pay per use |
The Shift in Practice
What AI Teams Used to Do
Training data → ETL → Snowflake → Export → S3 → Training pipeline
Agent queries → Snowflake (slow) → Fall back to Postgres + Pinecone
What AI Teams Do Now
import deeplake
# One database for AI workloads
ds = deeplake.open("al://my-org/ai-data")
# Store everything: embeddings, images, structured data
ds.add_column("embedding", deeplake.types.Embedding(1536))
ds.add_column("image", deeplake.types.Image())
ds.add_column("text", deeplake.types.Text())
ds.add_column("label", deeplake.types.Text())
ds.add_column("metadata", deeplake.types.Json())
# Query with SQL - but fast, multimodal, and GPU-native
results = ds.query("""
SELECT text, image, label
FROM ai_data
WHERE metadata->>'split' = 'train'
ORDER BY cosine_similarity(embedding, :q)
LIMIT 100
""")
# Stream directly to GPU for training
dataloader = ds.dataloader().pytorch(batch_size=32)You Don't Have to Migrate Everything
Keep your warehouse for BI and analytics - it's good at that. But move your AI data (embeddings, training datasets, agent state, multimodal assets) to Deeplake. They're different workloads that need different infrastructure.