Analytical Database

25x faster queries. 50x lower cost. Unlimited seats, unlimited queries — flat monthly pricing that scales down as you grow.

A columnar analytical engine with vectorized execution, always-on agents, and region-aware security — sub-second insights at a fraction of warehouse costs, with no per-query billing to think about.

Capabilities

Core properties of a database designed for continuous intelligence

Low Latency & Cost Efficiency

Columnar analytical engine with vectorized execution, adaptive caching, and an efficient planner. Sub-second queries without warehouse-style bills.

Predictable Economics

Fair, transparent pricing with a fixed monthly rate. Run an unmetered number of queries without unpredictable overages. View pricing.

Additional Capabilities

More features that make Altertable a complete analytical database solution

Secure by Design

Encryption in transit and at rest, key management at the region level, fine-grained permissions, and auditability in the query layer.

SQL Native & AI Ready

SQL-first interface designed for AI-native workloads and continuous analysis—not just batch jobs. Read the docs.

Ecosystem Ready

Adopt incrementally. Altertable integrates seamlessly with your existing data tools.

Every language. Every stack.

SDKs and adapters for all major languages, so your team ships without friction.

Lakehouse API

Query and write data directly to your Altertable lakehouse.

from altertable_lakehouse import Client
from altertable_lakehouse.models import QueryRequest

client = Client(
    username="your_username",
    password="your_password",
)

# Run a SQL query
req = QueryRequest(statement="""
    SELECT
        date_trunc('day', timestamp) AS day,
        COUNT(DISTINCT user_id)      AS dau
    FROM   events
    WHERE  event = 'Event Name'
    AND    timestamp >= NOW() - INTERVAL '30 days'
    GROUP  BY 1
    ORDER  BY 1 DESC
""")

# Accumulate all rows in memory
result = client.query_all(req)
for row in result.rows:
    print(row)

# Append data to a table
client.append(
    catalog="my_catalog",
    schema="my_schema",
    table="users",
    data={"user_id": 1, "plan": "pro"}
)