Solution
Deploy Data Agents
What It Is
Teams building data agents need infrastructure that lets them connect language models to reliable, up-to-date business data, iterate quickly, and deploy safely to production. Tower provides a runtime and deployment layer for data agents that works with Iceberg lakehouses and supports a wide range of models - from cloud-hosted 1T+ parameter LLMs to locally-running Small Language Models. Tower is designed for engineering teams that want to evaluate and operate data agents without building custom infrastructure for environments, orchestration, and observability.
Who It's For
Teams that are evaluating, building, or operating data agents in production, including:
Data Analytics and BI teams
Building natural-language "ask the data" interfaces on top of analytical datasets.
Data Engineering and Data Platform teams
Investigating pipeline failures by correlating deployments, schema changes, data volumes, and job logs.
Customer Support and Customer Success
Assembling customer profiles from incident history, product usage, and entitlements to draft support responses.
Sales and Revenue Operations
Generating call briefs from usage data, billing records, invoices, renewal dates, and recent emails or meetings.
Product Management and Growth
Analyzing KPI changes by correlating product releases, marketing activity, and customer behavior, then drilling into drivers through iterative "why" questions.
IT Operations
Correlating telemetry, incidents, and change logs to identify root causes of SLA violations.
How Tower helps with Data Agent Deployments
Tower provides infrastructure and tooling for developing and operating data agents:
- Connect agents to fresh business data stored in Iceberg-based lakehouses
- Run and compare multiple model and prompt versions in a scalable cloud environment
- Separate development, testing, and production environments
- Deploy agents either on Tower-managed infrastructure or into your own cloud or on-prem environment
- Support local development on your own hardware, including local GPUs
- Provide centralized observability (logs and metrics) across both Tower-managed and self-hosted deployments
- Use code-first orchestration compatible with major agent tool calling frameworks like LangChain
- Inspect and visualize tool-call dependencies to understand agent behavior and data flow
The goal is to make experimentation cheap, deployment repeatable, and production behavior observable without locking teams into a specific model or hosting setup.
Featured Blogs
Featured Talks
Surviving the Agentic AI Hype with Small Language Models
Preparing your AI Agents for the Ice(berg) Age
Local and Serverless DeepSeek R1 on Iceberg Lakehouses
Power Your Team with Tower
Get a Python-native orchestrator of data flows and optionally use a reliable, open lakehouse built on Apache Iceberg and compatible with Snowflake, Spark, and what comes next.