Vespa is an open-source AI + Data platform designed for organizing, searching, and inferring on diverse data types (vectors, tensors, text, structured data) at scale. It targets developers building applications requiring low-latency responses (under 100ms) for complex operations like search, recommendation, and personalization, even with continuously changing data.

How It Works

Vespa addresses the challenge of performing complex data operations and model inferences on large, dynamic datasets within strict latency requirements. It achieves this through a distributed, high-availability architecture that can process and serve data from multiple nodes in parallel. The platform is optimized for real-time data ingestion and querying, enabling sophisticated AI-driven applications.

Quick Start & Requirements

• Install: Deploy to Vespa Cloud (cloud.vespa.ai) or run a self-hosted instance (docs.vespa.ai/en/getting-started.html).
• Development Environment: AlmaLinux 8 for C++/Java builds. Java 17 and Maven 3.8+ required for Java modules. Docker is recommended for a full development setup.
• Resources: Building Vespa requires a complete development environment setup.

Highlighted Details

• Handles vectors, tensors, text, and structured data.
• Designed for sub-100ms query latency.
• Supports continuous data updates.
• Used in large-scale internet services serving high query volumes.

Maintenance & Community

• Daily releases (Mon-Thu CET) from the master branch.
• Contribution guidelines available in CONTRIBUTING.md.
• Documentation repository: github.com/vespa-engine/documentation.

Licensing & Compatibility

• Licensed under the Apache 2.0 license.
• Permissive license suitable for commercial use and integration with closed-source applications.

Limitations & Caveats

Building Vespa from source requires a specific Linux environment (AlmaLinux 8) or careful setup of Java 17 and Maven on other platforms. While cloud deployment is available, self-hosting requires adherence to build environment specifications.