Vespa.ai: an open source platform for building efficient AI search and recommendation systems

Vespa.ai is an open source AI search and recommendation platform that focuses on processing large-scale data to provide efficient search, recommendation and personalized services. It supports vector search, text search, and structured data processing, combined with machine learning models to achieve real-time inference.Vespa is capable of handling billions of dollars of data, with fast response times and latency as low as under 100 milliseconds, making it suitable for enterprise-level applications. The platform provides cloud services and local deployment options, and the open source code is maintained on GitHub, so developers are free to extend the functionality.Vespa is widely used in e-commerce, personalized recommendations and academic research, and is recognized by Spotify, Yahoo, and other companies for its high performance and flexibility.

Function List

• Supports mixed queries of vector, text and structured data to meet complex search needs.
• Provides real-time machine learning model inference to optimize search result rankings.
• Supports billion-data scale and handles thousands of queries per second with less than 100 milliseconds latency.
• Offers a streaming search model that reduces costs by 20x for personal data searches.
• Open source , supports developers to customize Java components to extend functionality .
• Offers Vespa cloud services to simplify deployment and management.
• Support multi-vector representation and hybrid search to improve search relevance.
• Integrated HNSW index to optimize nearest neighbor search performance.

Using Help

Installation and Deployment

Vespa offers two ways to use it: deploying it through the Vespa Cloud Service or running it locally. The cloud service is good for getting started quickly, and local deployment is good for users who need deep customization.

Cloud Services Deployment

1. interviews console.vespa-cloud.com, register for an account.
2. Create a new application, select the region and configuration (e.g. number of nodes).
3. Upload the data model and configuration file and the platform automates the deployment.
4. Use the Vespa API to send queries and get search or recommendation results.

local deployment

1. Make sure Java 17 and Maven 3.8+ are installed on your system, AlmaLinux 8 is recommended.[](https://github.com/vespa-engine/vespa)
2. Cloning GitHub repositories:

   git clone https://github.com/vespa-engine/vespa
   

3. Go to the project directory and run the Maven build:

   mvn install
   

4. To configure the development environment, refer to the https://docs.vespa.ai/en/getting-started.htmlThe
5. Start the Vespa instance:

   vespa deploy
   

6. Add nodes to increase redundancy and ensure high availability.

Main Functions

Vector Search and Hybrid Queries

Vespa supports mixed queries of vector, text and structured data, suitable for complex scenarios such as e-commerce search. Users can send query requests through the API:

{
"yql": "select * from sources * where userQuery() or nearestNeighbor(vector_field, query_vector);",
"query_vector": [0.1, 0.2, ...],
"hits": 10
}

• procedure: Upload data to Vespa, define vector fields and text fields. Write queries using YQL (Vespa Query Language), combining vector similarity and keyword search. Results are automatically sorted based on machine learning models.
• Featured Functions: Support for multi-vector representation allows documents to contain multiple vectors, improving search accuracy. For example, in academic search, both title and content vectors can be matched.

Real-time recommendations

Vespa's recommendation system combines search and machine learning evaluation to quickly return personalized results. Configuration Steps:

1. Define a data model that contains user behavior and content features.
2. Upload machine learning models (e.g., in TensorFlow or ONNX format).
3. Use the API to call the recommendation interface:

   {
   "yql": "select * from sources * where user_id = '123';",
   "ranking": "personalization_model"
   }
   

• procedureVespa uploads user and content data, and sets up a ranking model; Vespa calculates recommendations in real time based on the model, suitable for news recommendations or e-commerce product recommendations.

Streaming Search

Streaming search is low cost and efficient for personal data scenarios. Modus Operandi:

1. Configure the data source, labeled as streaming mode:

   {
   "schema": {
   "document": {
   "mode": "streaming"
   }
   }
   }
   

2. Upload personal data and send a query request. vespa processes only a subset of the relevant data, reducing resource consumption.

• Featured Functions: Streaming search eliminates the need to build a complete index and is suitable for privacy-sensitive scenarios such as personal email search.

Extended functionality

Developers can extend Vespa functionality with Java components:

1. Write a custom Searcher or Ranker, cf. https://docs.vespa.ai/en/developing-applications.htmlThe
2. Compile and deploy to a Vespa instance:

   vespa deploy --application my-custom-app
   

3. Test features to ensure compatibility with existing APIs.

Precautions for use

• Regularly update your Vespa version for the latest features (such as the new June 2025 tier ranking and chunking support).
• Check the API documentation https://docs.vespa.ai, make sure the query syntax is correct.
• Cloud service subscribers need to monitor quotas to avoid service interruptions due to overruns.

application scenario

1. E-commerce Search & Recommendation
   Vespa supports search combining text, images and structured data for e-commerce platforms. Users can search for products and get personalized recommendations at the same time. For example, when typing "sneakers", Vespa returns matching products and suggests relevant styles.
2. academic research
   Vespa handles academic datasets (e.g. COVID-19 research dataset) and supports vector search and keyword query. Researchers can quickly retrieve papers and improve research efficiency.
3. Personalized content recommendations
   Media platforms use Vespa to provide news or video recommendations. The system generates a list of recommendations in real time based on user behavior to enhance the user experience.
4. Privacy Sensitive Search
   Streaming search mode is suitable for handling personal data, such as email or document searches, protecting privacy while remaining efficient.

QA

1. Is Vespa free?
   Vespa is an open source platform, the code is free to use and hosted on GitHub. cloud services are available for a fee, for prices see https://vespa.aiThe
2. What data types does Vespa support?
   Supports vectors, text, structured data and tensors for complex queries and reasoning.
3. How to optimize search performance?
   Optimizing vector search using HNSW indexing, tuning ranking models to improve relevance, and adding nodes to improve throughput.
4. Is Vespa suitable for small projects?
   Yes, Vespa supports small deployments, running on a single node, for startups or personal projects.