Exasol

Exasol

by Exasol • • Data Warehouses

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Exasol is an in-memory analytics database and data warehouse for many concurrent users and AI agents, deployable as SaaS, on premises or in hybrid setups.

Exasol answers SQL queries over billions of rows in seconds, for organizations whose dashboards, applications and AI agents hit the same data at the same time. Behind it is Exasol AG, a stock corporation founded in 2000 and headquartered in Nuremberg. The design centers on control: a customer chooses where data runs, how governance is enforced and how the infrastructure changes over time. Automatic self-indexing helps tune query performance. The Query cache answers a repeated identical query from stored results instead of running it again. Workload Management sorts sessions into consumer groups and enforces resource limits, ending queries that break them. Python 3 UDF scripts run inside the database, in parallel on every cluster node, so models work next to the data. Virtual schemas present tables from remote databases to Exasol SQL, and the Kafka Connector Extension imports Kafka topics into tables. The Exasol MCP server lets AI assistants such as Claude Desktop query the database directly. Exasol fits enterprise data teams modernizing a legacy warehouse, with published migration guides for Oracle and Teradata. It also suits organizations that must keep data on infrastructure they own, since the same database runs as Exasol SaaS, on premises or across hybrid environments. Exasol sets out a two-step path for buyers: developers start on Exasol Personal to build and test analytics and agent use cases, then production moves to Exasol Enterprise, arranged with Exasol's experts. The jobs it takes on are near real-time reporting on large tables, self-service analytics for business teams and machine learning inference on warehouse data. A data team loads Kafka topics and files from AWS S3, Azure Blob Storage or Google Cloud Storage, and connects data virtualization tools such as Denodo and Data Virtuality on top. Data loading and reporting can run side by side, each consumer group held to its own resource limits. Exasol suits companies that want fast SQL analytics and AI agents on infrastructure they control.

Pricing

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  • Exasol Personal

    Free

    • Single user
    • Fully functional Exasol database
    • Runs in the cloud or on a local machine
    • For building and testing analytics, AI workloads and agent use cases
  • Exasol Enterprise

    Custom pricing

    • Price on request from Exasol's experts
    • For production analytics and AI workloads

Features

  • Not included: Independent scaling of storage and compute
  • Not included: Serverless query execution
  • Not included: Multi-cluster concurrency scaling
  • Not included: Time travel to prior table states
  • Not included: Zero-copy cloning of databases
  • Not included: Auto-refreshing materialized views
  • Not included: Native semi-structured columns
  • Not included: External tables on data lake files
  • Not included: Open table format interoperability
  • Not included: Streaming row ingestion into tables
  • Not included: Live data sharing across accounts
  • Not included: Column masking policies
  • Not included: Row access policies
  • Included: Automatic query result reuse
  • Included: Workload management queues
  • Not included: Compute spend limits and alerts
  • Not included: Cross-region warehouse replication
  • Included: In-warehouse Python execution

Additional Features

  • Query cache for repeated identical queries
  • Workload Management with consumer groups and resource limits
  • Python 3 UDF scripts run in parallel on every node
  • Automatic self-indexing
  • Virtual schemas over remote databases
  • Kafka Connector Extension for topic imports
  • Exasol MCP server for AI assistants
  • Exasol SaaS managed clusters
  • Auto-stop for idle SaaS clusters
  • Extra SaaS clusters per database
  • Automated database version updates
  • On-premises and hybrid deployment
  • Deployment on AWS, Azure and Google Cloud
  • Database backup and restore
  • SaaS user and role management
  • Oracle and Teradata migration guides

Best for

  • Enterprise data teams replacing a legacy Oracle or Teradata warehouse
  • Organizations where dashboards, applications and AI agents query one database at once
  • Companies that must control where their data runs
  • Teams that need on-premises or hybrid deployment alongside SaaS
  • Data scientists running Python models next to warehouse data
  • Developers building and testing analytics before a production rollout

Use cases

  • Running near real-time reporting on large tables
  • Serving many concurrent BI users from one warehouse
  • Offering self-service analytics to business teams
  • Migrating an Oracle data warehouse
  • Moving a Teradata warehouse to a new database
  • Running machine learning inference with Python UDFs
  • Giving AI assistants SQL access through an MCP server
  • Loading Kafka topics into warehouse tables
  • Querying remote databases through virtual schemas
  • Keeping data loads and reporting within separate resource limits

Screenshots & Videos

Explore Exasol in action

Exasol Homepage With The Agentic Chat Demo
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Exasol Homepage With The Agentic Chat Demo

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