Sold by: Vespa.ai
Deployed on AWS
Vespa is an AI search platform for real-time retrieval, ranking, and inference on AWS, powering customer-facing applications such as RAG, search, recommendations, and personalization. It unifies structured, unstructured, vector, and tensor data in a single system to deliver low-latency, high-throughput performance. With hybrid search and machine-learned ranking executed directly in the engine, Vespa enables accurate, scalable AI applications that respond instantly to user interactions.
4.6
Overview
Developers building customer-facing, large-scale search, Retrieval-Augmented Generation (RAG), and recommendation systems face a core challenge: retrieving and operationalizing data in real time. Data is fragmented across formats, including PDFs, free text, and semi-structured sources. This makes it difficult to unify, index, and serve data efficiently to applications and end users. Without the right infrastructure, applications become slow, brittle, and costly to scale. Vespa addresses this by unifying structured, unstructured, vector, and tensor data in a single system, enabling efficient, real-time retrieval and ranking at scale.
The Vespa AI search platform is built for real-time retrieval, ranking, and inference on AWS, powering customer-facing applications including search, RAG, recommendations, and personalization. It unifies structured, unstructured, vector, and tensor data to deliver fast, accurate, and highly relevant results at millisecond latency. Vespa is purpose-built for customer-facing experiences where latency, relevance, and scale directly impact engagement, conversion, and revenue.
By combining full-text search, vector search, and machine-learned ranking within a single query pipeline, Vespa delivers consistent, high-quality results across every user interaction. Its tensor-based ranking architecture enables applications to evaluate multiple signals simultaneously, including semantic meaning, behavioral data, and real-time context, enabling results to continuously adapt to user intent and business priorities. Ranking and inference run directly within the engine, eliminating external pipelines and enabling real-time updates to content, models, and business signals.
Running on AWS, Vespa delivers elastic scalability, high availability, and fully managed infrastructure through Vespa Cloud. Automated provisioning, scaling, monitoring, and upgrades reduce operational overhead while supporting high-throughput, low-latency workloads. Vespa is trusted in production by organizations including Perplexity, Spotify, and Yahoo to power large-scale, real-time search, recommendation, and AI applications. Developers use Vespa to build responsive, intelligent applications that enhance the customer experience, improve conversion rates, and drive measurable business outcomes.
Highlights
- Real-time performance and efficiency: Reduce latency and network overhead with co-located data and computation, enabling fast, resource-efficient retrieval at any scale.
- Relevance with hybrid search and ML ranking: Deliver accurate, contextual results using hybrid search and distributed machine-learned ranking across structured, unstructured, and vector data.
- Elastic scalability on AWS: Scale clusters up or down in real time while maintaining low latency, high throughput, and consistent uptime for production workloads.
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Pricing
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Dimension | Description | Cost/unit |
|---|---|---|
Vespa Units | Vespa Units consumed | $0.01 |
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See the Vespa Cloud Terms of Service.
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Software as a Service (SaaS)
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Support
Vendor support
See https://cloud.vespa.ai/support for support details.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Vespa.ai
By Serverless, Inc.
By CelerData Inc.
Top
10
In Ranking
Top
10
In Serverless Workloads, Monitoring and Observability
Top
50
In Data Warehouses
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Unified Data Indexing
Unifies structured, unstructured, vector, and tensor data in a single system for efficient real-time retrieval and ranking.
Hybrid Search and Machine-Learned Ranking
Combines full-text search, vector search, and machine-learned ranking within a single query pipeline with tensor-based ranking architecture for evaluating multiple signals simultaneously.
Real-Time Inference and Ranking
Executes ranking and inference directly within the engine, eliminating external pipelines and enabling real-time updates to content, models, and business signals.
Low-Latency Query Performance
Delivers millisecond-level latency through co-located data and computation architecture optimized for high-throughput, low-latency workloads.
Elastic Scalability with Managed Infrastructure
Provides automated provisioning, scaling, monitoring, and upgrades on AWS with real-time cluster scaling while maintaining low latency and high availability.
Infrastructure as Code Deployment
Enables definition of serverless applications as functions and events with single-command deployment of infrastructure and code to AWS Lambda
Debugging and Monitoring Tools
Includes built-in debugging capabilities with metrics, alerts for performance and error tracking, and request inspection for detailed issue analysis
Plugin and Extension Architecture
Supports extensibility through plugins and extensions to enhance serverless development capabilities
Team Collaboration Management
Provides centralized management of serverless applications with secure sharing of secrets, outputs, AWS accounts, and Lambda function tests across team members
AWS Lambda Integration
Integrates with AWS Lambda for simplified serverless application development and deployment
Deployment Model
Managed cloud offering that deploys in customer-owned VPC
Database Architecture
Extremely fast analytics MPP (Massively Parallel Processing) database
Infrastructure Management
Eliminates requirement for manual cloud resource management
Database Engine
StarRocks database engine
Operational Model
Customer-controlled cloud infrastructure with vendor-managed operations
Standard contract
No
No
Customer reviews
Automotive
Powerful backend for vector and hybrid search with many bells and whistles.
Reviewed on Dec 18, 2024
Review provided by G2
What do you like best about the product?
We purchased the Enclave product which was really well-suited for us because it let us run the hosts in our own Google cloud account (at our pricing with Google), and thus didn't require us to transfer any data out which was well-aligned with our security stance. It provided light-touch deployment and observability services that we lacked and helped us bootstrap quickly and with minimal investment.
The Vespa search backend itself provided a good match to our requirements of near-real time hybrid search, combining nearest neighbor embedding search with attribute filters, in a distributed and highly scalable way. Our target installation comprised >12TB of memory across 24 hosts and held O(1B) vector embeddings.
The Vespa search backend itself provided a good match to our requirements of near-real time hybrid search, combining nearest neighbor embedding search with attribute filters, in a distributed and highly scalable way. Our target installation comprised >12TB of memory across 24 hosts and held O(1B) vector embeddings.
What do you dislike about the product?
Vespa, in a scalable deployment, presents a fairly complex architecture with a lot of tuning knobs and bells and whistles. It took several months to get familiar with them. The Vespa consultant was very instrumental in this. Feeding Vespa from BigQuery was harder than expected.
Native extensions can only be written in Java which, without a native Java toolchain at our company, proved too challenging to pursue. The documentation is vast but could be better organized and have more contextual examples in places.
Native extensions can only be written in Java which, without a native Java toolchain at our company, proved too challenging to pursue. The documentation is vast but could be better organized and have more contextual examples in places.
What problems is the product solving and how is that benefiting you?
We used the Vespa search backend for hybrid search, consisting of nearest neighbor search of indexed embeddings vectors and attribute filters. This powered a natural-language image search product for our internal users.
Satwik L.
My go-to-tool for my research on my e-commerce data
Reviewed on Sep 11, 2024
Review provided by G2
What do you like best about the product?
I like the open-source and free 300 dollar cloud credits for hosting the live applications.
What do you dislike about the product?
I feel there should be more documentation work is in pending and needed as I am still exploring the AI and vector database part.
Anyway I am happy to contribute for open source as a contributor.
Anyway I am happy to contribute for open source as a contributor.
What problems is the product solving and how is that benefiting you?
I worked for my e-commerce client to highlight the product which are giving more sales by ranking and recommendations for efficiency in stock.
Michele S.
Connect data to AI capabilities
Reviewed on Aug 13, 2024
Review provided by G2
What do you like best about the product?
I can create recommendation applications and deploy real-time machine learning inference using this stack. Such a level of functionality is what we need for our large scale search applications.
What do you dislike about the product?
Vespa initialization and subsequent functioning, in fact, require a significant level of system configuration. It may be a little obscure sometimes and for troubleshooting issues one has to really appreciate the underlying environment.
What problems is the product solving and how is that benefiting you?
Vespa solves the problem of managing and processing large amounts of data and its integration with Artificial Intelligence for Web applications. It enables me to build outstanding search capabilities and I use real-time data processing.
Vignesh H.
Best Gen AI software to build your own infrastructure
Reviewed on Jul 30, 2024
Review provided by G2
What do you like best about the product?
The most helpful thing is the open source big data engine, heps to process and serve large scale data in real time with very low latency time.Its content recommendations are very useful for the modern day real-time analysis. Also, it is more flexible and scalable with advanced query techniques which makes it more easy to use.
What do you dislike about the product?
Integrating vespa with existing systems and workflows can be challenging, particulary if systems were based on different technologies. Documentation and customer support for an open source is not at the top notch when compared to the real time products. since it is highly specialised it may overkill for simpler applications w or less demanding requirements.
What problems is the product solving and how is that benefiting you?
Vespa helps in solving real time updates by using as a search engine which gives lot of recommendations based on our search results. it has the scalability and flexibility to process large volume of data in real time analyses and in turn produces intelligent responses based on the latest data.
Marketing and Advertising
Vepsa decreased costs, latency, and management for billions of searches per month
Reviewed on Jun 12, 2024
Review provided by G2
What do you like best about the product?
For our use case in advertising, Vespa leaves Apache Lucene-based products in the dust:
- High indexing throughput while searching
- Very, very technical team
- Best of the best technical support and guidance
- Multiple times, discussions were had and the next day the idea was implemented
- High indexing throughput while searching
- Very, very technical team
- Best of the best technical support and guidance
- Multiple times, discussions were had and the next day the idea was implemented
What do you dislike about the product?
- Search is still costly
- Improving ANN capabilities with ideas like DiskANN
- Simplify schema configuration and testing
- Lean in on more cloud native technologies
- Improving ANN capabilities with ideas like DiskANN
- Simplify schema configuration and testing
- Lean in on more cloud native technologies
What problems is the product solving and how is that benefiting you?
We do web-scale advertising. This means we process billions of queries a month concurrently with hundreds of million of feed requests. Vespa Cloud and their team provided us great technical guidance, saving us hundreds of thousands of dollars by optimizing and implementing fixes for our deployment. Although the road to utilizing Vespa took a long, hard journey, we are in a much better place then our previous solution with a Lucene-based product.