- Products
- SolutionsUse Cases
Real-Time Analytics
Sub-second dashboards and data products on petabytes of data at any concurrency
Data Warehousing
Sub-second analytics on open lakehouse formats with no vendor lock-in
Observability in the AI Era
The most cost-effective alternative to Elasticsearch observability
Context Engineering
Hybrid search and fresh context for RAG, agents, and LLMs
IndustriesFinancial Services
Real-time analytics and fraud detection for banking, capital markets, and on-chain data
Ad & Media tech
Advertiser-facing reporting, content search, and game telemetry on one engine
Automobile & Transportation
Fleet telemetry, real-time recommendations, and multimodal autonomous driving search
- Docs
- Resources
- Pricing
- Contact us
Batch and real-time in one serving layer — Lambda architecture, rebuilt for the AI era.
Tower orchestrates the full data lifecycle: batch pipelines from Snowflake and Databricks into an open Apache Iceberg lakehouse, plus real-time streams and CDC into VeloDB. VeloDB serves it all as one high-speed layer — sub-second SQL, full-text search, and vector retrieval at thousands of concurrent queries — collapsing the fragmented Lambda stack into a single governed architecture for BI, observability, and AI agents.
What Tower does
Tower is a fully-managed, Python-native orchestration platform from a team of ex-Snowflake engineers, built on the principle of “ship product, not infrastructure.” Deploy any Python — ELT jobs, dbt Core models, notebooks, or agents — with a simple Towerfile, and Tower handles packaging, scheduling, compute, and governed data movement across hybrid stacks like Snowflake, Databricks, and Iceberg. It works with the modern Python data stack: dbt, dlt, Polars, DuckDB, and Iceberg catalogs such as Apache Polaris and Lakekeeper.
Why VeloDB + Tower
One serving layer for batch and real-time
VeloDB unifies the governed batch archive in Iceberg with real-time streams and CDC, ending the dual-path complexity of classic Lambda architecture — one system for dashboards, logs, and RAG instead of three.
Governed pipelines across hybrid stacks
Tower moves data from Snowflake, Databricks, and streaming sources into Iceberg and VeloDB — and a feedback loop merges fresh data from VeloDB back into Iceberg, keeping the historical archive in sync.
SQL, search, and vector in one engine
VeloDB handles analytical queries, full-text search, and vector-based AI retrieval together over open Iceberg (V3), so you skip the separate databases each of those workloads usually demands.
Built for agentic AI
Sub-second latency at thousands of concurrent queries — even under heavy updates — lets AI agents and human users share the same infrastructure without resource contention.
What you can build
Unified BI and observability
Serve sub-second dashboards and log analytics from the same governed data, without standing up separate stores for each workload.
RAG and AI context engineering
Tower pipelines keep data fresh while VeloDB serves vector retrieval and low-latency context directly to RAG pipelines and agents.
Lakehouse plus real-time, together
Query batch history in Iceberg alongside live data in VeloDB — multi-table joins at scale across both, in one architecture.
How the data flows
Ready to get started?
Tell us about your stack and use case. Our team will help you stand up VeloDB with Tower for your environment.
