The Elasticsearch Alternative

Workload Requirements

When should you consider Vespa?

Elasticsearch supports a broad range of search and analytics applications. If your deployment meets your relevance, freshness, latency, and cost requirements, a migration may offer little benefit. Vespa deserves a closer look when your application combines:

Complex ranking: bringing together text, vectors, business signals, and machine-learned models.
Frequently changing data: keeping inventory, prices, preferences, and engagement signals available to search and ranking.
Demanding serving requirements: sustaining query traffic within tight latency targets while data continues to change.

These requirements often come together in product discovery, recommendations, and AI agents. The following customer stories and technical comparisons can help you assess whether Vespa fits your workload.

engineers' PERSPECTIVE

From Elasticsearch to Vespa: customer engineering stories

Explore the requirements behind each migration, the engineering decisions involved, and the results achieved.

Kleinanzeigan

Rebuilding homepage recommendations with Vespa, combining clicks, searches, and preferences to deliver a substantial uplift in listing clicks.

Read the engineering blog
Vinted

Consolidating six Elasticsearch clusters into one Vespa platform and halving server requirements.

Read the engineering blog
Splore

Moving from Elasticsearch to Vespa to introduce hybrid search and reduce reported search latency from over a second to approximately 20 milliseconds.

Read the Searchplex case study

ARCHITECTURAL APPROACHES

Compare Vespa with Elasticsearch

Both platforms support keyword and vector search. The differences below focus on how they retrieve, rank, and update information—and how those approaches affect application design.

Capability	Vespa	Elasticsearch
Retrieval and filtering	Combines keyword, vector, and structured retrieval, with Boolean composition of multiple retrieval paths.	Combines keyword and vector retrieval, structured filters, and retriever pipelines with rank fusion.
Ranking pipeline	Programmable first-phase, second-phase, and global ranking, combining custom expressions, tensor operations, and machine-learned models.	Query scoring, function and script scoring, rescore, learning to rank, and semantic reranking through composable retrievers.
Model execution	Executes supported models within the ranking pipeline, on content nodes or in the global phase, depending on configuration.	Supports model-based rescoring and reranking through Elastic-hosted, in-cluster or external inference, depending on the model and configuration.
Updating live signals	Supports continuous writes alongside queries, with controls for allocating resources between feeding and search. See benchmark results for tested workloads.	Supports continuous indexing alongside queries; refresh and segment merging influence the resource balance. See benchmark results for tested workloads.
Concurrent search and updates	Supports continuous writes alongside queries, with controls for allocating resources between feeding and search. See benchmark results for tested workloads.	Supports continuous indexing alongside queries; refresh and segment merging influence the resource balance. See benchmark results for tested workloads.
Performance and cost	Higher query throughput per CPU core and more efficient updates in Vespa’s published 2024 benchmark. Results depend on workload and configuration.	Results in the same benchmark vary by retrieval workload and index configuration. Evaluate against your own latency, throughput, and update requirements.

Capabilities depend on version, deployment, and configuration. Performance comparisons reference Vespa’s published 2024 benchmark and its specified software versions, hardware, and workloads. Validate suitability using your own data and application requirements.

Explore the benchmark and methodology

RETRIEVAL FOR AGENTS

Building AI agents? Evaluate the retrieval behind each decision.

An agent may search repeatedly as it compares options, checks constraints, and decides what to do next. The relevance and freshness of each result can affect subsequent decisions.

Vespa lets developers combine retrieval paths, select application-defined ranking profiles, and apply machine-learned models across ranking phases. This provides control over both the evidence returned and the computation used to select it.

For agentic applications, evaluate retrieval quality, data freshness, latency, and cost across the complete task, not just a single search.

Explore retrieval in Vespa Explore ranking in Vespa

Resources

Engineering evaluation and migration resources

Elasticsearch vs. Vespa: performance benchmark

Examine a reproducible comparison using an e-commerce search application with one million products. Explore query performance, indexing, and updates across the tested workloads and configurations.

Read the benchmark
Benchmark executive summary

Explore the key performance findings and their implications for infrastructure efficiency and cost in this six-page management summary.

Read executive summary
Elasticsearch and Vespa: architectural differences

Understand how the two platforms handle indexing, document updates, query execution, and scaling—and what those differences mean for your application requirements.

Read the architecture guide
Vespa terminology for Elasticsearch users

Map familiar Elasticsearch concepts to Vespa’s application packages, schemas, content nodes, and query processing. Understand where the architectures overlap and differ.

Explore the terminology guide
Planning your migration to Vespa

Explore a roadmap for moving from Elasticsearch to Vespa, helping you structure your migration planning and identify the decisions ahead.

Read the migration brief
Migrating from Elasticsearch to Vespa with Searchplex

Explore schema mapping, ingestion pipelines, and access control considerations in this technical session on migrating Elasticsearch workloads to Vespa.

Watch the migration webinar

Smarter Product Discovery with Real-Time AI

See how Vespa uses tensors, semantic search, and real-time personalization to power intelligent, business-aware eCommerce experiences.

Watch the Demo

What performance improvements could I see when moving from Elasticsearch to Vespa?

Results depend on your data, queries, ranking models, update rate and configuration. Vespa’s published 2024 benchmark found higher query throughput per CPU core and lower latency across the tested lexical, vector and hybrid workloads. Use the report as a starting point, then test your own workload against your latency, throughput and infrastructure requirements.

How does Vespa handle frequently changing data?

Elasticsearch makes indexed changes searchable through a refresh operation. Vespa supports continuous updates to searchable data, including in-place updates to in-memory attribute fields without reindexing the whole document. This is particularly useful when prices, inventory, preferences or engagement signals change frequently and must be available to filtering and ranking.

How do Vespa and Elasticsearch approach scaling?

Both platforms support horizontal scaling. Elasticsearch distributes data through indices and shards; Vespa distributes documents in fine-grained buckets and automatically redistributes them as cluster capacity changes. When evaluating either platform, test data growth, query traffic, and update volume together, including how the system behaves when capacity is added or removed.

How does Vespa support AI search and agentic applications?

Vespa combines text, vector, and structured retrieval with programmable multi-phase ranking. Developers can combine relevance signals, perform tensor computations, and execute supported machine-learned models within the ranking pipeline. These capabilities support applications such as recommendations, personalized product discovery, and agents that need relevant, current information across multiple searches.

What operational changes should I expect when migrating to Vespa?

Moving to Vespa introduces a different application model, including schemas, ranking profiles, and deployment configuration. Its automatic data distribution and integrated retrieval, ranking, and model execution can reduce the need for separately managed components. The outcome depends on your architecture: Vinted, for example, consolidated six Elasticsearch clusters into one Vespa platform and halved its server count. Those results reflect one deployment, not a guaranteed migration outcome.