ElasticsearchThe search database for AI
vs
MongoDB AtlasThe intelligent data platform for the AI era
Comparison · Vector Databases · weekly run Nov 2
Elasticsearch vs MongoDB Atlas
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
Elasticsearch is the stronger AI pick in Vector Databases: it has a visibility score of 14.3 versus 12.7 for MongoDB Atlas, and ranks ahead in 73% of the answers that name both.
Choose Elasticsearch if…
- You want the brand AI engines rank higher (14.3 vs 12.7)
- AI answers describe it more positively (30% vs 28% positive)
- You need SOC 2 Type II or ISO/IEC 27001 or ISO/IEC 27017 or ISO/IEC 27018 or ISO/IEC 27701 or FedRAMP Moderate or FedRAMP High coverage
- Strongest general enterprise choice for vector search alongside full-text search, logs, and analytics
Choose MongoDB Atlas if…
- You need ISO/IEC 27001:2022 or FedRAMP Moderate (Atlas for Government) or TX-RAMP Level 2 or GDPR coverage
- You prefer a larger, established vendor (5,000+ employees)
- stores documents and vector embeddings together in one system
- good fit for teams already using MongoDB
AI visibility side by side
Latest weekly run · Nov 2
| Metric | Elasticsearch | MongoDB Atlas |
|---|---|---|
| Visibility score | 14.3% | 12.7% |
| Change vs last week | New | New |
| Rank in Vector Databases | #7 | #8 |
| Share of voice | 5.1% | 5.5% |
| Mentions this run | 24 | 26 |
| Positive in AI answers | 30% | 28% |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Elasticsearch 0pp · MongoDB Atlas 0pp over 0 weekly runs.
Engine by engine
Elasticsearch leads on 1 of 3 engines
ChatGPT
AI Mode
Perplexity
ElasticsearchMongoDB Atlas
Head to head in AI answers
15 engine answers name both brands
11Elasticsearch ranked higher
4MongoDB Atlas ranked higher
| Prompt | Engine | Elasticsearch | MongoDB Atlas |
|---|---|---|---|
| How do I choose a vector database for a mid-market team? | ChatGPT | #7 | #6 |
| How do companies store and search embeddings without a dedicated tool? | ChatGPT | #3 | #5 |
| How do companies store and search embeddings without a dedicated tool? | AI Mode | #4 | #7 |
| What should a vector database integrate with? | ChatGPT | #3 | #2 |
| What are the best Vector Databases tools in 2026? | ChatGPT | #8 | #10 |
| What are the best Vector Databases tools in 2026? | AI Mode | #7 | #8 |
| Top vector database options for B2B SaaS companies | Perplexity | #6 | #8 |
| Compare the leading Vector Databases platforms | ChatGPT | #7 | #8 |
| Compare the leading Vector Databases platforms | Perplexity | #6 | #9 |
| Alternatives to the most popular vector database | Perplexity | #7 | #8 |
| Which Vector Databases vendors do analysts recommend? | AI Mode | #4 | #6 |
| Which Vector Databases vendors do analysts recommend? | Perplexity | #5 | #6 |
Features, pricing & fit
| Dimension | Elasticsearch | MongoDB Atlas |
|---|---|---|
| Pricing model | — | Usage-based |
| Entry price | — | Free; paid from $0.011 / mo |
| Free plan | — | Yes |
| Deployment | Elastic Cloud Serverless, Elastic Cloud Hosted, or self-managed on premises, in private, hybrid or air-gapped environments. | Fully managed cloud with Atlas Core and Atlas Infinite deployment options, or self-managed through MongoDB Enterprise Advanced |
| Integrations | — | 100+ |
| Compliance | SOC 2 Type II, ISO/IEC 27001, ISO/IEC 27017, ISO/IEC 27018, ISO/IEC 27701, FedRAMP Moderate, FedRAMP High, PCI DSS, HIPAA, CSA STAR | ISO/IEC 27001:2022, FedRAMP Moderate (Atlas for Government), TX-RAMP Level 2, PCI DSS, HIPAA, GDPR, CSA STAR |
| Support | Four support tiers depending on Elastic Cloud subscription tier, 99.95% monthly uptime SLA for Platinum and Enterprise tiers | Basic Support included free with Atlas, Paid plans with faster response times, Named Technical Support Engineers, Extended Lifecycle Support add-on |
| Founded | 2012 | — |
| Headquarters | San Francisco, California | New York, NY, United States |
| Employees | 1,001–5,000 | 5,000+ |
| Funding | IPO (NYSE) | IPO and stock offering · $311M raised |
| Best for | Teams building AI search, RAG and vector applications, and organizations that want search, observability and security on one data platform. | Development teams building AI applications and agents that need operational data, vectors and search in one platform |
Plans side by side
Elasticsearch
Pricing not available.
MongoDB Atlas
Free (M0)$0hour
Flex$0.011hour (up to $30/month)
Dedicated$0.08hour (starts at $56.94/month)
Atlas Infinite (Public Preview)$0.09hour
Enterprise Advanced (Self-managed)Customcontact sales
Pros and cons
Themes from the AI answers that mention each brand
Elasticsearch
- Strongest general enterprise choice for vector search alongside full-text search, logs, and analytics
- Widely deployed, often cited as the most widely deployed vector-capable search engine
- Hybrid keyword-plus-semantic search via dense vectors and inverted indexes
- Suited to consolidated stacks and broader search, logging, analytics, or observability platforms
- Existing infrastructure can be leveraged
- Smaller community and ecosystem than PostgreSQL, MySQL, MongoDB
MongoDB Atlas
- stores documents and vector embeddings together in one system
- good fit for teams already using MongoDB
- built-in vector search without extra infrastructure
- vector search included in Atlas pricing with a free M0 tier
- integration with Databricks Lakebase
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Elasticsearch vs MongoDB Atlas”, https://slateindex.ai/compare/elasticsearch-vs-mongodb-atlas, data of Nov 2, 2026. Licence: CC BY 4.0.