Explore the performance benchmarks behind Vespa, the AI Search Platform for production AI applications. Compare Vespa with alternative retrieval technologies and discover how every release is continuously validated through comprehensive engineering benchmarks.
When evaluating AI retrieval platforms, architecture matters—but so does evidence. Our benchmarks measure throughput, latency, indexing performance, machine learning inference, and infrastructure efficiency using representative production workloads. Some compare Vespa with alternative technologies, while others demonstrate the engineering performance that underpins every Vespa deployment.
Our benchmarks fall into two categories: comparative benchmarks that evaluate Vespa against alternative technologies, and engineering benchmarks that continuously validate the performance of every Vespa release. Together, they provide transparent evidence of Vespa's performance on representative AI retrieval workloads.
Everything needed to build production AI applications in a single platform, combining retrieval, ranking, machine learning, and serving.
Combine vectors, keywords, tensors, structured data, filters, and business rules in one retrieval engine.
Run ONNX models, embeddings, reranking, and machine learning inference directly inside the serving engine.
Continuously ingest and update data without batch rebuilds or offline indexing.
Deliver predictable low-latency performance for billions of documents and production AI workloads.
Reduce infrastructure costs by minimizing unnecessary data movement through an integrated architecture.
Integrate with existing data sources, operational systems, and AI models using open APIs and SDKs.
Deploy on Vespa Cloud or manage your own clusters on Kubernetes or bare metal.
Benchmarks provide a useful starting point, but every application is different. Contact our team to discuss your workload or evaluate Vespa using your own data.
