Vespa
What is Vespa?
Vespa is an open-source AI search platform for serving applications that need low-latency computation over large datasets. It combines data storage, indexing, structured queries, text search, vector search, ranking, and machine-learned model inference in one serving platform. Developers can use it for AI search, retrieval-augmented generation, recommendations, personalization, and AI agents. Vespa supports custom application components and models, including TensorFlow, ONNX, XGBoost, and LightGBM workflows. Vespa Cloud adds managed infrastructure, autoscaling, operational automation, security controls, and support for production deployments.
How to use Vespa?
1. Create a Vespa application from the documentation or start a Vespa Cloud account with trial credits. 2. Define schemas, deploy your application, and feed structured, text, or vector data into Vespa. 3. Send queries through the query API and configure ranking, retrieval, or model inference to deliver a first search or AI-generated result.
Vespa's Core Features
Hybrid Search: Combine lexical text matching, structured filters, and vector retrieval in a single query workflow.
Vector Search: Find semantically similar documents or items using nearest-neighbor and approximate nearest-neighbor search.
Real-Time Ranking: Apply ranking expressions, tensors, and machine-learned models at serving time to improve result relevance.
RAG Support: Retrieve and enrich relevant content for retrieval-augmented generation applications.
Model Inference: Run supported TensorFlow, ONNX, XGBoost, and LightGBM models close to the served data.
Data Serving: Store, index, update, and query structured, textual, and vector data at scale.
Application Components: Extend search workflows with custom searchers, request handlers, document processors, and other components.
Managed Cloud Deployment: Run Vespa applications with autoscaling, automated operations, monitoring, and production support.
Security Controls: Protect cloud applications with access control, service identity, isolation, encryption, and security operations.
Developer APIs: Manage documents, queries, deployments, and application interactions through documented APIs and client tools.
Vespa's Use Cases
- #1
Building hybrid keyword and vector search for ecommerce product catalogs
- #2
Creating retrieval-augmented generation applications over enterprise documents
- #3
Ranking personalized content or recommendations in real time
- #4
Serving AI agent retrieval and reasoning workflows with current business data
- #5
Implementing semantic search across large technical or scientific collections
- #6
Combining structured filters, text relevance, and nearest-neighbor retrieval
- #7
Deploying low-latency search services for media, advertising, travel, or financial applications
Frequently Asked Questions
Analytics of Vespa
Monthly Visits Trend: Jun 2025 - Sep 2026
Traffic Sources
AI Channel Traffic Trends
Top Regions
| Region | Traffic Share |
|---|---|
| United States | 30.28% |
| India | 9.50% |
| Norway | 8.85% |
| Germany | 8.47% |
| Vietnam | 5.97% |
Top Keywords
| Keyword | Traffic | CPC |
|---|---|---|
| vespa | 173.6K | $0.19 |
| vespa ai | 770 | $0.87 |
| jina ai | 10.3K | $3.20 |
| vespa ai search | 130 | -- |
| vespa filesystem | 120 | -- |
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