The Large-Scale Vector Database

The challenge isn't finding similar vectors. It's delivering fast, relevant retrieval as data, ranking, and AI workloads become increasingly complex.

Watch the video Read the GigaOm Radar for Vector Databases

↗ AI Search Platform ↗ Tensors

Why Are Vector Databases Becoming Difficult to Scale

Vector search is only the beginning. Most vector databases perform well when retrieval is the primary requirement. The real challenges emerge as data volumes, query traffic, ranking complexity, and update rates increase.

In customer-facing applications, these issues directly impact user experience. Search results become less responsive, recommendations become less timely, and AI applications struggle to keep pace with constantly changing data. The challenge is no longer finding similar vectors. It's maintaining relevance, freshness, and performance as AI retrieval systems become more complex.

Many teams respond by adding more infrastructure: separate systems for vector retrieval, keyword search, filtering, reranking, or even multiple vector databases to balance performance and functionality. Initially, this works well. A vector database handles semantic retrieval, another engine manages keyword search, a reranker improves relevance, and additional services provide filtering or machine learning inference.

Over time, however, every new capability introduces another moving part. Latency increases, operational complexity grows, and tuning becomes harder because retrieval performance now depends on multiple systems working together. The challenge is no longer vector search. It is operating a large-scale AI retrieval architecture.

The challenge is no longer vector search. It is operating a large-scale AI retrieval architecture

Why Retrieval is an Architecture Problem

These challenges are becoming even more pronounced with the rise of agentic AI. Unlike traditional search applications, agents continuously retrieve, evaluate, and refine information as they reason and act. This dramatically increases retrieval volume while placing greater demands on latency, freshness, ranking quality, and system scalability.

Organizations that once viewed vector search as a standalone capability are increasingly discovering that retrieval has become a core platform requirement. Most vector databases are optimized for semantic similarity search. Large-scale AI retrieval demands much more, combining semantic retrieval with keyword search, filtering, ranking, personalization, business rules, and machine learning inference.

As these capabilities are added, many organizations assemble multiple specialized systems. What begins as a vector search deployment gradually becomes a fragmented retrieval architecture that is increasingly difficult to operate, optimize, and scale.

Vespa was built for large-scale AI retrieval, not just vector search.

Instead of adding more infrastructure, Vespa brings retrieval, ranking, and machine learning together into a single distributed serving engine, allowing AI applications to remain fast, relevant, and operationally simple at scale.

Choose Vespa When:

Query latency is increasingly impacting service
Infrastructure costs are growing faster than usage
Stitching together search engines, vector databases, and rerankers.
Ranking has become increasingly sophisticated
Freshness matters as much as relevance
AI agents have dramatically increased retrieval traffic

Built for Large-Scale AI Retrieval

Most retrieval systems become more complex as they become more capable. Every new requirement—vector search, keyword retrieval, reranking, machine learning inference, or orchestration—often introduces another component into the serving path. As data volumes and query complexity increase, latency, infrastructure costs, and operational overhead rise accordingly.

Vespa takes a different approach.

Instead of distributing retrieval across multiple systems, Vespa brings vector search, keyword search, filtering, ranking, and machine-learning inference together in a single distributed serving engine. The result is a simpler architecture designed to deliver fast, relevant retrieval at large scale.

That's why organizations including Spotify, Yahoo, Perplexity, and AlphaSense use Vespa to power production AI applications at scale.

Explore customer stories

One Engine

Stop moving data around!

Many retrieval architectures move data between multiple services before returning a result. Every network hop adds latency, infrastructure, and operational complexity. Vespa performs retrieval, filtering, ranking, and machine learning inference where the data resides, delivering high throughput, fewer moving parts, and <100 ms latency for large-scale AI retrieval.

Always Fresh

AI retrieval depends on continuously changing information. New documents, embeddings, and user signals need to become searchable immediately, not after an index rebuild or background refresh.
Vespa supports continuous indexing and real-time updates while serving live traffic, keeping RAG, recommendations, personalization, and agentic AI applications up to date without sacrificing performance.

Linear Scale

Adding more data shouldn't require adding more systems.

Vespa's distributed architecture scales to billions of vectors and documents through intelligent partitioning, replication, and autoscaling. As workloads grow, retrieval, ranking, and inference scale together, allowing applications to expand without rearchitecting the retrieval pipeline. Engineering benchmarks demonstrate that Vespa maintains throughput and latency as datasets and query volumes grow.

Beyond Vectors: Why Tensors Matter

Vectors are an excellent representation for semantic similarity, but AI retrieval often requires multiple signals, multiple embeddings, and richer mathematical representations. Vespa is vector- and tensor-native by design, allowing dense, sparse, and structured data to work together inside a single retrieval engine.

Learn why tensors matter

Everything Needed for AI Retrieval

Unified Vector, Text, and Structured Retrieval

Vespa brings all retrieval methods into a single, cohesive engine. You can combine dense embeddings, keyword signals, and metadata filters in one query, eliminating the need for multiple systems or external orchestration.
Multimodal and Multi-Vector Support

Vespa supports multiple embeddings per document, enabling use cases such as ColBERT or ColPali-style retrieval, image–text matching, and cross-modal search all within the same schema.
Ranking Built In

Vespa lets you deploy ranking models directly inside the serving layer using ONNX, XGBoost, or custom functions. You can perform first-phase recall with embeddings, then re-rank using machine learning models for maximum accuracy and explainability.
Advanced Filtering and Aggregation

Beyond similarity search, Vespa supports structured filters, geospatial queries, and aggregations directly on vector fields. This allows you to combine semantic and business logic seamlessly.
Tensor-Native Architecture

Vespa stores and computes on tensors, not just vectors. This enables multi-dimensional representations that capture relationships between modalities such as text, images, and numeric data, supporting both dense and sparse features for hybrid search.

Explore tensors
Real-Time Indexing and Updates

Unlike immutable-segment vector systems, Vespa supports continuous data ingestion and updates without costly index rebuilds. Applications stay fresh and responsive even under high write throughput.

Why Teams Choose Vespa

Vespa was recognized by GigaOm as a Leader and Outperformer among vector databases for its ability to store, retrieve, and process complex data structures at scale and in real time. This independent analyst recognition complements our continuous engineering benchmarks and large-scale customer deployments.

Read the GigaOm report

How Does Vespa Compare?

Most platforms excel at one aspect of AI retrieval. Traditional search engines provide strong keyword search but were not designed for vector-native retrieval and machine learning. Standalone vector databases deliver semantic similarity but often require additional systems for ranking, filtering, and large-scale retrieval.

Vespa takes a different approach by bringing vector search, keyword search, ranking, machine-learning inference, and tensor-native data processing together into a single distributed engine. The result is a simpler architecture with lower operational complexity and better outcomes as AI retrieval requirements evolve.

Compare retrieval platforms

Explore the AI Search Platform

Learn how the Vespa AI Search Platform combines retrieval, ranking, machine learning, and distributed serving in a single architecture to execute the end-to-end AI retrieval workflow for large-scale AI applications.

Explore the AI Search Platform

Why would I choose Vespa over a vector database?

Choose Vespa when vector search is no longer your only retrieval requirement. As AI applications grow, they often need to combine semantic search with keyword search, filtering, ranking, machine learning inference, and real-time updates. Many teams meet these requirements by stitching together multiple systems, increasing latency, operational complexity, and infrastructure costs.
Vespa takes a different approach. It brings vector search, hybrid retrieval, ranking, and machine learning together into a single distributed serving engine, allowing you to build and scale AI retrieval applications without adding additional infrastructure.

When does a vector database become difficult to scale?

Vector search scales well when similarity search is the primary requirement. As applications add hybrid retrieval, ranking, machine-learning inference, real-time updates, and agentic AI, retrieval architectures become increasingly complex. Vespa was designed for these environments, bringing retrieval, ranking, and inference together into a single distributed engine.

How is a vector database different from a traditional search engine?

Traditional search engines rely on keyword matching and text-based ranking, whereas vector databases use embeddings to capture meaning and context, returning semantically similar results even when the exact words differ. Vespa does both in a single engine, combining precise keyword and structured filtering with semantic vector retrieval so results are not only contextually relevant but also accurate and explainable.

Why do AI applications need hybrid retrieval?

Vector search is excellent for finding semantically similar content, but similarity alone rarely produces the best results. Modern AI applications combine vector search with keyword search, structured filtering, metadata, and business rules to improve precision and explainability. Hybrid retrieval allows these signals to work together in a single query, producing more accurate and relevant results.

Is ranking as important as retrieval?

Retrieval identifies candidate documents. Ranking determines which of those documents are most relevant to the user or AI application. As retrieval systems grow, ranking increasingly combines semantic similarity, keyword relevance, freshness, personalization, business rules, and machine learning models. For many production AI applications, ranking has become the primary factor in retrieval quality.

What makes Vespa different from other vector databases?

Vespa goes beyond simple vector search by supporting tensors, multi-phase ranking, and hybrid retrieval. It can blend semantic, textual, and structured signals at scale, run machine learning models directly in the query path, and provide explainable, real-time results even for massive datasets.

Can Vespa replace both a search engine and a vector database?

Yes. Vespa combines keyword search, vector retrieval, filtering, ranking, and machine-learning inference into a single distributed engine. Many organizations use Vespa to replace multiple retrieval systems, simplifying architecture while improving performance and reducing operational complexity.

Can Vespa run machine learning models during retrieval?

Yes. Vespa executes ONNX and XGBoost models, as well as custom ranking functions, directly within the serving layer. Running inference where retrieval occurs reduces latency and eliminates the need for separate model-serving infrastructure.

What is multimodal retrieval?

Multimodal retrieval combines information from different data types such as text, images, audio, and structured data within a single search. Vespa supports multimodal retrieval through its tensor-native architecture, enabling applications to retrieve and rank across multiple modalities with a single engine.

What are tensors?

Tensors are multi-dimensional data structures that generalize vectors and matrices, allowing AI systems to represent complex relationships and context across text, images, and structured data. In Vespa, tensors enable unified retrieval and ranking by combining vector, text, and structured signals within a single, flexible framework.

Ready to move beyond vector databases?

Vector search is a powerful retrieval technique, but modern AI applications need more than vectors alone. If you're building large-scale search, RAG, recommendations, or agentic AI systems, we'd be happy to discuss your architecture and show how Vespa brings dense, sparse, and structured retrieval together in a single platform.

More resources

Explore related guides, videos, case studies, and technical documentation in our resource library to learn more about AI search and retrieval with Vespa.