Both engines can power hybrid search, RAG and AI agents. They differ in licensing, how you configure and scale them, and how much control you get over ranking. Here is how they compare, based on each project's official documentation.
| Feature | Elasticsearch | Vespa |
|---|---|---|
| License | Elastic License 2.0, SSPL 1.0 or AGPLv3 (your choice) | Apache 2.0 |
| Managed service | Elastic Cloud Hosted and Serverless | Vespa Cloud |
| Self-managed | Yes | Yes |
| Configuration model | Index mappings and settings via REST APIs | Application package: services.xml plus schemas |
| Query language | Query DSL and ES|QL | YQL |
| Hybrid retrieval | BM25 + kNN, fused with the RRF retriever | BM25 + nearestNeighbor in one query, fused in rank profiles (e.g. RRF) |
| Ranking model | Query scoring, rescoring and learning to rank (GBDT models) | Rank profiles with first-, second- and global-phase ranking |
| Model inference | Inference API with built-in (ELSER, E5) and third-party models | Built-in embedders and ONNX models evaluated during ranking |
| Multi-vector / token embeddings | Not compared here | Mixed tensors and a ColBERT embedder |
| Aggregations | Aggregations | Grouping language |
| Scaling data | Shards and replicas you plan per index | Buckets distributed automatically across content nodes |
| Observability and logs | Core use case with the Elastic Stack (Kibana) | Not a focus |
Relevance starts with BM25 and query-time boosting. Learning to rank adds a second-stage re-ranker using gradient boosted decision tree models, and the inference API produces embeddings with built-in models such as ELSER and E5 or with third-party services.
Ranking is defined in rank profiles inside the schema. A cheap first phase scores every match on the content nodes, a second phase re-ranks the top hits per node, and a global phase re-ranks the merged results in the container, where ONNX models such as cross-encoders run. Built-in embedders run ONNX models too, and a ColBERT embedder stores token-level embeddings for ranking.
Run it on Elastic Cloud Hosted, Elastic Cloud Serverless or your own servers. You plan shards and replicas per index, and the Elastic Stack covers logs, metrics and dashboards alongside search.
Run it on Vespa Cloud or self-host it. An application package declares stateless container clusters and stateful content clusters. Vespa manages data distribution itself: add content nodes and it redistributes data, with no manual sharding. Fields stored as attributes can be partially updated in place and are visible to queries and ranking immediately.
Moving from Elasticsearch to Vespa is more than copying data. We help you:
Elasticsearch is a broad search and analytics engine with a large ecosystem for search, logs and observability. Vespa is focused on serving search, recommendation and RAG applications where ranking is central: rank profiles run in several phases and can evaluate machine-learned models, including ONNX models, per query.
Yes. Vespa is released under the Apache 2.0 license and can be self-hosted, or run on Vespa Cloud, its hosted platform. Elasticsearch source code is available under your choice of Elastic License 2.0, SSPL 1.0 or AGPLv3, with Elastic Cloud as the managed option.
Both handle hybrid lexical and vector retrieval for RAG. Elasticsearch fuses BM25 and kNN results with its RRF retriever. Vespa combines BM25 and nearest neighbor search in one query and fuses or re-ranks them in rank profiles. Vespa gives more control over multi-phase ranking; Elasticsearch is often simpler if you already run it.
Choose Vespa when ranking quality is the main problem: you need several ranking phases, machine-learned or cross-encoder models at query time, multi-vector representations, or frequent in-place updates to ranking signals. Stay with Elasticsearch when you also need log analytics, rely on the Elastic Stack, or your current relevance is good enough.
Moving the data is straightforward: Vespa documents a path that exports an Elasticsearch index, converts mappings to a schema and feeds the documents. The real work is rewriting queries in YQL, rebuilding relevance logic as rank profiles and validating results against your current system before switching traffic.
Talk to us. We will look at your data, queries and ranking needs and recommend the engine that fits.
