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Head-to-head · Vector Databases

Elasticsearch vs VikingDB

  • 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
VikingDB #3 in Vector Databases 9.1/10 Paid ✓ 3 of 3 features Visit VikingDB

Elasticsearch leads on 0 checks, VikingDB 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 featuresVikingDB · 3 of 3

Elasticsearch scores higher on our rubric for vector databases: 9.3 against 9.1 out of 10; our editors rank them #1 and #3.

On deployment, Elasticsearch gives you Both where VikingDB offers Cloud.

Elasticsearch is the better fit for search teams adding vectors to mature analytics stacks. VikingDB is the better fit for cloud users needing multimodal vector retrieval.

  • Elasticsearch fits best

    Search teams adding vectors to mature analytics stacks

  • VikingDB fits best

    Cloud users needing multimodal vector retrieval

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Side by side

Feature Elasticsearch 9.3/10 Visit ↗ VikingDB 9.1/10 Visit ↗
At a glance
Editor score 9.3 9.1
Ranking #1 in Vector Databases #3 in Vector Databases
Best for Search teams adding vectors to mature analytics stacks Cloud users needing multimodal vector retrieval
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 Docs
Integrations 3 integrations 1 integrations
Built for Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features Elasticsearch 0/3 · VikingDB 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 4096 dimensions
Index types Not checked yet HNSW, HNSW-Hybrid, FLAT, IVF, DiskANN
SDK languages Not checked yet Python, Java, Go, Node.js
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, filtered, and multimodal search
  • Offers HNSW, IVF, and DiskANN indexing
  • Provides streaming updates plus API and SDK 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
  • Cloud deployment may not suit on-premises requirements
  • Hourly postpaid billing can complicate fixed-budget planning
  • Documented integration coverage centers on LangChain
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 →

VikingDB is a cloud vector database from Volcengine for storing, indexing, and searching machine-learning vectors. It is aimed at teams building RAG systems, semantic search, recommendations, image similarity, video retrieval, data…

Read the review →
  1. ElasticsearchVector Databases 9.3Free plan · 14-day trial
  2. VikingDBVector Databases 9.1Paid

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

  • VikingDB — where it wins

    • Supports dense, sparse, hybrid, filtered, and multimodal search
    • Offers HNSW, IVF, and DiskANN indexing
    • Provides streaming updates plus API and SDK access

    Where it doesn't

    • Cloud deployment may not suit on-premises requirements
    • Hourly postpaid billing can complicate fixed-budget planning
    • Documented integration coverage centers on LangChain

More comparisons

All vector databases comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update