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Head-to-head · Vector Databases

Elasticsearch vs Vertex AI Vector Search

  • Updated Oct 2026
  • Both researched from official sources
  • 4 checks side by side
Higher score Elasticsearch #1 in Vector Databases 9.3/10 Free plan · 14-day trial Free planFree trial✓ 0 of 3 features Visit Elasticsearch
Vertex AI Vector Search #2 in Vector Databases 9.2/10 Paid ✓ 3 of 3 features Visit Google Cloud

Elasticsearch leads on 0 checks, Vertex AI Vector Search on 0, and 4 are even. Who comes out ahead on the 4 yes/no, price and count checks where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreElasticsearch · 9.3/10
  • Free planonly Elasticsearch
  • Free trialonly Elasticsearch
  • Most featuresVertex AI Vector Search · 3 of 3

Elasticsearch scores higher on our rubric for vector databases: 9.3 against 9.2 out of 10; our editors rank them #1 and #2.

On deployment, Elasticsearch gives you Both where Vertex AI Vector Search offers Cloud.

Elasticsearch is the better fit for search teams adding vectors to mature analytics stacks. Vertex AI Vector Search is the better fit for google Cloud teams needing managed hybrid search.

  • Elasticsearch fits best

    Search teams adding vectors to mature analytics stacks

  • Vertex AI Vector Search fits best

    Google Cloud teams needing managed hybrid search

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Side by side

Feature Elasticsearch 9.3/10 Visit ↗ Vertex AI Vector Search 9.2/10 Visit ↗
At a glance
Editor score 9.3 9.2
Ranking #1 in Vector Databases #2 in Vector Databases
Best for Search teams adding vectors to mature analytics stacks Google Cloud teams needing managed hybrid search
Pricing model Free plan + paid Paid
Starting price — Not checked yet
Free plan ✓ (best) Not checked yet
Free trial 14 days —
Deployment Cloud, Self-hosted Cloud
Platforms Web Web
Support Phone, Live chat · 24/7 Community, Docs
Built for Small business, Mid-market, Enterprise Enterprise
Features Elasticsearch 0/3 · Vertex AI Vector Search 3/3
Hybrid search Not checked yet ✓ (best)
Metadata filtering Not checked yet ✓ (best)
Sparse vectors Not checked yet ✓ (best)
Specs
Deployment Both Cloud
Maximum dimensions Not checked yet Not checked yet
Index types Not checked yet Tree-AH, brute force
SDK languages Not checked yet Python, Java
Our review
Pros
  • Combines lexical, semantic, vector, hybrid, filtered, and geospatial search
  • Supports distributed storage, replication, scaling, aggregations, and analytics
  • Offers managed, self-hosted, API, Kubernetes, cloud, and on-premises deployment
  • Supports dense, sparse, hybrid, and filtered vector search
  • Offers batch and streaming index updates
  • Provides public and private endpoints with SDK, REST, and gRPC access
Cons
  • Elastic Cloud pricing varies by provider, region, instance type, and usage
  • The Basic plan is self-managed rather than a managed Elastic Cloud deployment
  • Its broad search and analytics scope may exceed dedicated vector database needs
  • Usage-based costs span serving, building, and updating indexes
  • Cloud deployment limits portability across infrastructure providers
  • Requires Google Cloud services and client libraries for its ecosystem fit
Our verdict

Elasticsearch is a distributed search and analytics engine for structured, unstructured, and vector data. It suits search teams that want to add vector retrieval to an established analytics stack rather than adopt a narrowly focused vector…

Read the review →

Vertex AI Vector Search is a managed cloud vector search engine from Google Cloud for semantic search, recommendations, and generative AI applications. It is aimed at teams that want to create, deploy, update, and query indexes through…

Read the review →
  1. ElasticsearchVector Databases 9.3Free plan · 14-day trial
  2. Vertex AI Vector SearchVector Databases 9.2Paid

Strengths and trade-offs

  • Elasticsearch — where it wins

    • Combines lexical, semantic, vector, hybrid, filtered, and geospatial search
    • Supports distributed storage, replication, scaling, aggregations, and analytics
    • Offers managed, self-hosted, API, Kubernetes, cloud, and on-premises deployment

    Where it doesn't

    • Elastic Cloud pricing varies by provider, region, instance type, and usage
    • The Basic plan is self-managed rather than a managed Elastic Cloud deployment
    • Its broad search and analytics scope may exceed dedicated vector database needs

  • Vertex AI Vector Search — where it wins

    • Supports dense, sparse, hybrid, and filtered vector search
    • Offers batch and streaming index updates
    • Provides public and private endpoints with SDK, REST, and gRPC access

    Where it doesn't

    • Usage-based costs span serving, building, and updating indexes
    • Cloud deployment limits portability across infrastructure providers
    • Requires Google Cloud services and client libraries for its ecosystem fit

More comparisons

All vector databases comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update