Enter the AI Search Platform

An AI Search Platform operationalizes AI by making retrieval smarter, faster, and more scalable. It combines classical search with modern AI techniques like vector search, machine-learned ranking, and real-time inference. These platforms serve as the foundation for applications that demand context-aware, highly relevant results at speed and scale.

Unlike legacy search stacks that focus on sparse term-based retrieval, AI Search Platforms are designed to support advanced use cases like RAG, semantic search, and personalization. They natively handle complex data structures such as tensors, integrate with machine learning workflows, and scale to meet enterprise demands for performance, accuracy, and flexibility.

Vespa by Design: Core Architectural Pillars

Performance: Low Latency, Lower Cost

• The challenge: AI systems often suffer from slow queries, system bottlenecks, and expensive ranking stages.
• Vespa’s advantage: Vespa co-locates data and computation, minimizing network overhead. Its multi-phase ranking pipeline evaluates results in stages, ensuring fast, resource-efficient performance across massive datasets.

Accuracy: Relevant, Contextual Results

• The challenge: Failing to retrieve the most relevant information for a particular situation leads to low quality AI responses and user experiences.
• Vespa’s advantage: Vespa supports hybrid retrieval—keyword, structured, vector, and tensor, and applies distributed machine-learned ranking combining hundreds of signals needed to deliver state-of-the art relevance in typical real-world applications.

Elastic Scalability: Built for Growth

• The challenge: Many systems cannot deliver both high quality and scalability, as that requires co-locating all data modalities and computation on the same distributed set of nodes.
• Vespa’s advantage: Vespa is proven to deliver low latency and reliability at scales up to hundreds of billions of documents and hundreds of thousands of queries per second. You can change the machine resources powering clusters both up and down, simply by changing a configuration value, while serving queries and writes in real time.

Flexibility: Fit for Complex Requirements

• The challenge: Off-the-shelf tools can’t adapt to domain-specific data or evolving AI needs and are locked into specific use cases..
• Vespa’s advantage: Vespa enables deep customization, supporting custom document schemas, external ML models (ONNX, GBDT), and fully configurable query pipelines. It handles both structured and unstructured data, so teams can adapt Vespa to their use case—not the other way around.

Offered as open source and a fully managed cloud service, Vespa gives teams full control over their stack—supporting custom data models, real-time updates, and horizontal scaling without compromising on latency or cost.