Postgres + Deep Lake: JSON + embeddings + files, training-native
See the architecture
Query primitives for semantic + temporal structure, plus zero-copy paths from storage to GPUs.
What You Can Do With PG Deep Lake
PG Deep Lake gives you one place to store, search, query, and stream your AI data, across JSON, files, embeddings, video, and tensors. Use it to power retrieval, analytics, training, and replay without stitching together five different systems.
Query Any Modality, Together
Ask questions that span metadata + raw data in one shot: filter by time, session, user, sensor, or model version, then pull back the exact frames, tensors, files, or traces you need.
Find the Exact Slice Fast
Jump to the precise window or subset that matters: "the 5 seconds before the event," "all sessions with negative sentiment," "all trajectories where reward dropped," or "the examples that triggered this failure." No manual digging, no re-exporting.
Replay and Debug Deterministically
Reproduce what the system "saw" at any point in time. Store versioned datasets and full traces so you can replay runs, compare iterations, and debug failures with confidence.
Stream Directly Into Training
Move from data → batches without re-ETL. Stream the same stored data into PyTorch/JAX efficiently to keep GPUs fed and iteration cycles tight.
Build Dashboards and Monitoring on Top
Track what teaches your models: usage, drift, performance, and data quality. Power internal dashboards and monitoring from the same source of truth your retrieval and training pipelines use.
Benchmarks & Efficiency
Deep Lake PG was built to be fast and cost efficient:
State of the art TPC H cost efficiency vs serverless warehouses
Run standard analytics workloads at a lower cost profile: without paying the serverless "tax" for predictable queries and always-on teams.
GPU streaming that maintains 95% utilization
Feed training jobs directly from storage with high-throughput, low-overhead streaming so GPUs stay busy instead of waiting on I/O and data prep.
Minimal memory footprint while serving billions of rows
Deliver fast queries and high concurrency without needing massive RAM footprints—scale efficiently as data volume and traffic grow.
Zero pipeline maintenance across OLTP and OLAP workloads
Stop stitching systems together. Use one consistent data plane for transactions, analytics, retrieval, and training without fragile ETL glue.
Built for Developers Building Real AI Systems
Modern AI apps aren't chatbots anymore. They're agentic systems with:
- Branching plans
- Rollbacks
- Partial writes
- Scratchpads
- Vector memory
- Multimodal inputs
- GPU fine-tuning on demand
Deep Lake PG supports this natively:
- Multimodal indexing and retrieval
- Branch and merge tables for speculative agent writes
- Billion scale vector + SQL queries
- Horizontal scaling using object storage consistency
- GPU streaming at 95% utilization
If you're building agents for research, enterprise automation, scientific discovery, or large-context reasoning, this dramatically simplifies your architecture.