Agent-Lake: Data Warehouse Built for AI Agents
High-performance data warehouse that handles millions of rows with fast queries. No setup, no cluster management, no infrastructure overhead. Designed from the ground up for AI agents to analyze your data.
See Agent-Lake in Action
Query millions of rows with fast response times. Try these examples or write your own SQL.
Query Your Data with SQL
Agent-Lake provides full SQL support for technical users. Write complex queries with joins, window functions, CTEs, and more—all with fast execution.
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For Technical Users
SQL When You Want It
- • Write SQL queries directly
- • Join across multiple datasets
- • Use CTEs, window functions, etc.
- • Export results as needed
AI When You Don't
- • Quick questions in natural language
- • Let AI handle complex analysis
- • Generate charts automatically
- • Share insights with non-technical team
Same data, multiple interfaces. Use what works best for each situation.
Why Agent-Lake Outperforms Traditional Warehouses
Overview
Agent-Lake is our high-performance data warehouse infrastructure designed specifically for AI agents. Unlike traditional data warehouses that require complex setup and management, Agent-Lake provides fast query execution on millions of rows with zero configuration.
Built on DuckDB's columnar storage engine, Agent-Lake delivers analytical query performance comparable to enterprise warehouses like Snowflake or BigQuery—without the infrastructure overhead, maintenance burden, or per-query costs.
Key Benefits:
- Query millions of rows in seconds
- No warehouse setup or cluster management required
- AI agents query via SQL automatically
- Scales automatically with your data
- Concurrent queries from multiple agents
- Zero infrastructure overhead or per-query costs
Benefits
Fast Query Execution
No warehouse setup, no clusters to manage. Upload your data and start querying immediately. Most queries complete in seconds, even on millions of rows.
DuckDB-Powered Performance
Fast analytical queries on massive datasets. Column-oriented storage optimized for aggregate operations, filtering, and complex analytical workloads.
Built for AI Agents
Optimized for ChatGPT, Claude, Gemini, and custom AI agents. Agents query via SQL automatically with schema introspection for context.
Concurrent Access
Multiple AI agents and users can query simultaneously. No performance degradation or locking issues—designed for collaborative analysis.
No Infrastructure Overhead
No Snowflake/BigQuery bills. No cluster management. No ETL pipelines to maintain. Scales automatically with your data, pay only for storage.
SQL + Natural Language
Full SQL support for technical users. AI agents translate natural language to SQL automatically for non-technical team members.
Documentation
1Architecture
3-Layer Design:
- Storage: Parquet columnar format, S3-compatible
- Engine: DuckDB with vectorized execution
- AI Layer: Auto SQL generation, schema introspection
Why Fast:
- Column-oriented for analytical queries
- Predicate pushdown reduces data scanned
- No network overhead (in-process queries)
2Performance
Typical Query Times:
- Simple queries: 1-3 seconds
- Complex joins: 5-10 seconds (multi-table)
- Large datasets: Optimized with column pruning
Scales From KB to TB:
- No warm-up delays
- Linear scaling
- Concurrent queries with no locking
3Best For
✅ Ideal Use Cases:
- Analytical queries (aggregations, grouping)
- Multi-dataset analysis (joins across sources)
- AI-powered insights (natural language)
- Self-serve analytics (no bottlenecks)
❌ Not Recommended:
- Real-time updates (read-optimized)
- Transactional workloads (use PostgreSQL)
- Streaming data (batch/micro-batch only)
4SQL Support
Full SQL:2016 Standard:
Joins, window functions, CTEs, subqueries, aggregates, JSON, regex
Two Ways to Query:
- Technical users: Write SQL in SQL Editor
- Everyone else: Ask in plain English, AI translates to SQL
Frequently Asked Questions
Ready to Query Your Data at Scale?
Start with Agent-Lake today. Upload your data and run your first query in seconds—no setup required.
Agent-Lake vs Traditional Warehouses
Enterprise performance without enterprise complexity
Agent-Lake | Snowflake/BigQuery | PostgreSQL | |
|---|---|---|---|
| Setup Time | Instant | 2-4 weeks | 1-2 weeks |
| Query Performance (1M rows) | 1-3s | 3-5s | 10-30s |
| Infrastructure Management | Zero | Auto-scales (complex pricing) | Server maintenance |
| AI Agent Optimized | Limited | No | |
| Concurrent Queries | Unlimited | Pay per query | Limited by server |
| Storage Included | 10-100 GB | Pay per TB | Limited by disk |