Vespa

Overview

Vespa is an open-source big data serving engine originally developed at Yahoo and battle-tested at massive scale. While not exclusively a vector database, Vespa excels at combining vector search with traditional search, filtering, and machine learning model inference in a single platform. It has been used in production at Yahoo, Verizon Media, and other major companies serving billions of queries per day.

Vespa's architecture is designed for low-latency serving of complex queries over large datasets, making it ideal for scenarios requiring more than simple vector similarity search. It can handle real-time updates, complex ranking expressions, and personalization at scale that few other systems can match.

Key Features

**Hybrid Search**: Combines vector, text, and structured search

**ML Model Serving**: Built-in inference for TensorFlow, PyTorch, ONNX

**Advanced Ranking**: Custom ranking expressions with multiple phases

**Real-Time Updates**: Sub-second document updates

**Auto-Scaling**: Horizontal scaling with data redistribution

**Query Language**: Powerful YQL (Vespa Query Language)

**Personalization**: Real-time personalized search and recommendations

**Battle-Tested**: Proven at Yahoo-scale deployments

When to Use Vespa

Vespa is ideal for:

Large-scale search and recommendation systems

Applications needing hybrid search (vector + text + filters)

Real-time personalization at scale

Complex ranking and ML model serving

Big data applications with billions of documents

Organizations requiring proven enterprise reliability

Pros

Battle-tested at massive scale (Yahoo, Verizon)

Combines vector search with full search engine capabilities

Excellent performance and scalability

Real-time updates without reindexing

Advanced ranking and personalization features

Strong consistency and reliability

Open-source with commercial support available

Comprehensive documentation

Cons

Steep learning curve and complexity

Overkill for simple vector search use cases

Requires significant resources to run

Java-based which may not fit all tech stacks

Setup and configuration can be complex

Smaller community focused on specific use cases

Less integrated with modern LLM frameworks

Pricing

**Open Source**: Free, Apache 2.0 license

**Vespa Cloud**: Managed service with free tier

**Production**: Pay-as-you-go based on resources

**Enterprise**: Custom support and SLAs