# Exasol

> In-memory MPP analytics database with SaaS and self-managed modes

Exasol is an in-memory, columnar, massively parallel analytics database available as a managed SaaS on S3-backed storage, deployed into a customer's own cloud account, or run on-premises.

Website: https://www.exasol.com

## Use it when

- Query speed on a fixed, known data volume is the goal; the in-memory columnar MPP engine tunes itself, creating and dropping its own indexes.
- Data residency governs the choice; the same engine runs as SaaS, in your own AWS, Azure, or GCP account, or on-premises.
- Predictable pricing by raw data volume beats metering compute hours.
- You want honest evaluation terms; Exasol Personal is free, unlimited in time and data, single-user.

## Think twice when

- Data growth is fast or unbounded; a volume-based license prices growth at the contract line.
- You want elastic scale-to-zero economics; SaaS clusters help, but the model is closer to a sized system than a serverless one.
- Self-tuning cuts both ways: there are no hints and manual indexing is discouraged, so an optimizer miss leaves you fewer levers than Snowflake or ClickHouse offer.

## How it runs

SaaS runs multiple compute clusters over shared S3-backed data; self-hosted and BYOC deployments couple in-memory columnar storage to the nodes. Indexes and statistics maintain themselves during execution. Consumer groups weight CPU and cap sessions. Iceberg and Parquet are readable natively, and virtual schemas federate external sources.

## Details

### General

| Attribute | Value |
| --- | --- |
| License | Commercial |
| Deployment | Managed, Self-hosted |
| Operational complexity | Medium |
| Pricing | Free, Subscription |
| Documentation | https://docs.exasol.com |

### Data Warehouses

| Attribute | Value |
| --- | --- |
| Where it runs | SaaS on AWS only; self-hosted or BYOC on any cloud, or on-premises |
| Compute and storage | In-memory MPP: separated storage on SaaS, coupled self-hosted |
| Storage format | Proprietary in-memory columnar; native Iceberg and Parquet reads, plus virtual schemas |
| SQL dialect | Exasol SQL (ANSI-based) |
| Streaming ingest | Via connector: Kafka |
| Concurrent users | Consumer groups weight CPU and cap sessions; SaaS adds clusters over one copy of the data |
| Acceleration | Self-tuning: indexes created, maintained and dropped automatically; statistics after every DML |
| Billing unit | Raw data volume licence self-hosted; cluster-hour on SaaS |
| Time travel | None: backup restore instead |
| Access control | Roles and object privileges; row-level security via a virtual-schema extension; LDAP, Kerberos or OpenID |

## Capabilities

- [Data Warehouses](https://matca.io/capabilities/data-warehouse)

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Source: https://matca.io/exasol
Last updated: 2026-08-31T11:40:48.551Z