Elasticsearch vs MongoDB
No clear leader: MongoDB (75.7) and Elasticsearch (73.0) are within the 5-point margin; treat as a tie. The attribute-by-attribute breakdown below, with a source and date on every value, is the honest way to compare them.
Capabilities
Feature-by-feature on the axes that matter for databases. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
Elasticsearch
A search platform that distributes data across multiple nodes to handle large-scale indexing and retrieval through standard web APIs.
MongoDB
MongoDB is a fully managed cloud database that uses flexible document-based storage and supports complex queries, analytics, and advanced search capabilities including vector and full-text search functionality.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Elasticsearch
From $99/month. Free 14-day trial available.
- Standard$99/month
- Distributed Elasticsearch across AWS, Azure, GCP
- ES|QL query language and APIs
- Default monitoring and AutoOps
- Alerting with detection engine
- Malware prevention
- +6 more
- Gold$114/month
- All Standard features
- Reporting
- Third-party alerting actions
- Watcher
- Multi-stack monitoring
- +3 more
- Platinum$131/month with SLA
- All Gold features
- Machine learning (anomaly detection, supervised learning)
- Vector search with ELSER and e5 models
- Cross-cluster replication
- Log categorization
- +5 more
- Enterprise$184/month
- All Platinum features
- Searchable snapshots in cold/frozen tiers
- Elastic Maps Server
- Cross-cluster search with ES|QL
- ES|QL Data Federation (query S3 directly)
- +13 more
MongoDB
Free tier (512MB), Flex from $8–$30/month, Dedicated from $56.94/month
- Free (M0)Free
- 512MB storage
- 32MB sort memory
- 100 ops/sec
- shared vCPUs
- replica set
- Flex Clusters$8–$30/month based on usage
- hourly billing
- monthly invoices
- Dedicated ClustersFrom $56.94/month
- dedicated resources
- auto-scaling
- horizontal scaling via sharding
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
- Kubernetes
Elasticsearch
- Slack
- Jira
- ServiceNow
- PagerDuty
- Microsoft Teams
- IBM Resilient
- xMatters
- GitHub
- Jina AI
- NVIDIA AI
- Prometheus
- Grafana
- AWS Bedrock
- Sarvam AI
- Google ADK
- LlamaParse
- Mistral
- Elastic Agent
- Beats
- Logstash
- Filebeat
- Metricbeat
- Heartbeat
- Webhooks
- +18 more
MongoDB
- Vertex AI
- BigQuery
- Datastream
- Google Kubernetes Engine
- Looker
- Apigee
- Apache Kafka
- Google Vertex AI
- Google Cloud BigQuery
- Google Cloud Datastream
- Google Cloud Functions
Elasticsearch or MongoDB: which one depends on you
A composite score cannot know your constraints. Describe them and both get re-weighted against what you actually need, with the evidence behind every position.
Free to run, no account needed to start. How the evaluation works
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.