Amazon DynamoDB vs MongoDB
Amazon DynamoDB vs MongoDB compared on data modeling, query flexibility, 2026 pricing, free tiers, search and vector support, and cloud lock-in for production apps.
Quick Answer
Choose DynamoDB if you are all-in on AWS, know your access patterns up front, and want a hands-off database that scales to any traffic. Choose MongoDB if your queries evolve, you need ad-hoc filtering, aggregation, search or vector search, or you want to run the same database on any cloud or on-prem.
Reviewed by TechLogHub Engineering Team. Last updated October 15, 2026. Reflects DynamoDB's reduced on-demand pricing, MongoDB Atlas Free/Flex/Dedicated tiers and the June 2026 GA of search and vector search in MongoDB Community Edition.
| Feature | Amazon DynamoDB | MongoDB |
|---|---|---|
| Data Model | Key-value / wide-column items | JSON/BSON documents |
| Query Flexibility | Key and range queries; PartiQL subset | Rich queries, aggregation pipeline, joins |
| Pricing Model | On-demand ($0.125/M reads, $0.625/M writes) or provisioned | Atlas: Free, Flex ($8-$30/mo), Dedicated from ~$57/mo |
| Free Tier | 25 GB storage (Standard class) | Atlas M0 (512 MB) |
| Max Item / Document Size | 400 KB | 16 MB |
| ACID Transactions | ||
| Secondary Indexes | GSIs and LSIs (limited count) | Many index types (compound, text, geo, wildcard) |
| Full-Text & Vector Search | No (via OpenSearch integration) | Yes (Atlas and Community Edition) |
| Multi-Region Replication | Global tables (active-active) | Multi-region replica sets, global clusters |
| Deployment Options | AWS managed only (local emulator for dev) | Atlas on AWS/Azure/GCP, self-managed anywhere |
| Licence | Proprietary service | SSPL (Community), commercial (Enterprise/Atlas) |
Amazon DynamoDB
AWS's fully managed, serverless key-value and wide-column NoSQL database with single-digit-millisecond latency at virtually any scale.
Pros
- Zero servers, patches or versions to manage; scales throughput automatically in on-demand mode
- On-demand throughput prices were cut 50% in November 2024, making pay-per-request viable for most workloads
- Consistent low latency regardless of table size when keys are designed well
- Global tables provide active-active multi-region replication
- Deep AWS integration: IAM, Lambda triggers via Streams, Kinesis, S3 export
- Permanent free tier includes 25 GB of storage
Cons
- Queries are limited to key lookups and range scans; ad-hoc filters require scans or extra indexes
- Single-table design has a steep learning curve and is hard to change later
- Each global secondary index adds its own write cost and storage
- AWS-only, with no self-hosted production equivalent
- Small item size limit compared with MongoDB documents
Best For
AWS-native serverless apps with well-known access patterns that need predictable latency at large scale.
MongoDB
A document database storing JSON-like BSON documents, available self-managed or as MongoDB Atlas, a managed service on AWS, Azure and Google Cloud.
Pros
- Rich query language and aggregation pipeline handle ad-hoc filters, joins ($lookup) and analytics
- Flexible schema lets data models evolve without reworking access patterns
- Atlas Search and Vector Search built in; now also GA for self-managed Community Edition
- Runs on any major cloud or on-prem, which reduces vendor lock-in
- Atlas free M0 cluster plus a usage-based Flex tier capped at $30/month
- Large ecosystem of drivers, ODMs and developer tooling
Cons
- Dedicated Atlas clusters bill hourly whether busy or idle
- Performance depends on index design and working set fitting in RAM
- Sharding requires choosing a good shard key and adds operational complexity when self-hosted
- Server is licensed under SSPL, which is not OSI-approved open source
- Flexible schema can lead to inconsistent data without validation rules
Best For
Teams that need flexible queries, search or vector features, and portability across clouds.
Data modeling: access patterns first vs documents first
DynamoDB asks you to design around queries before you write any data. Every efficient read goes through a partition key, optionally with a sort key range, so experienced teams use single-table design: many entity types share one table with overloaded keys and carefully chosen global secondary indexes. Done well, every request is a predictable, fast key lookup. Done poorly, you end up with expensive scans or a painful migration when a new query appears. MongoDB lets you model data as it naturally looks, embedding related data in documents up to 16 MB and adding indexes later as queries emerge. Its aggregation pipeline handles grouping, filtering, $lookup joins and reshaping server-side, so product teams can ship new features without rethinking the storage layout. The trade-off is predictability. DynamoDB performance is almost independent of table size because every operation is bounded. MongoDB performance depends on indexes and on your working set fitting in memory, so a missing index can quietly turn a fast query into a collection scan. If your requirements are still moving, MongoDB's flexibility usually wins; if they are settled and scale is extreme, DynamoDB's discipline pays off.
Cost at scale after the 2024 DynamoDB price cut
In November 2024 AWS halved DynamoDB on-demand throughput prices and cut global table replication costs by up to 67%. In us-east-1, on-demand now costs $0.125 per million read request units and $0.625 per million write request units, with Standard storage at $0.25 per GB-month. The change made on-demand mode the sensible default for most tables; provisioned capacity with auto-scaling is now mainly worth it for steady, high-throughput workloads. The hidden multiplier is indexes. Every global secondary index that projects an item consumes its own write units, so a table with three GSIs can cost several times its base write price. Large items also cost more, because writes are billed per 1 KB and reads per 4 KB. MongoDB Atlas pricing is instance-based for production. The free M0 tier offers 512 MB, Flex is usage-based between $8 and $30 per month, and dedicated clusters start at the M10 tier at roughly $57 per month, billed hourly. That makes MongoDB costs predictable but means you pay for idle capacity. Low-traffic or spiky apps often run cheaper on DynamoDB, while steady, query-heavy workloads can be cheaper on a right-sized Atlas cluster.
Lock-in and portability
DynamoDB exists only on AWS. There is a local version for development and testing, and several databases offer DynamoDB-compatible APIs, but moving a production DynamoDB workload elsewhere usually means rewriting your data access layer. Its tight coupling to IAM, Lambda, Streams and Kinesis is a strength inside AWS and a cost if you ever leave. MongoDB runs as Atlas on AWS, Azure and Google Cloud, including multi-cloud clusters, and as self-managed Community or Enterprise Edition on your own servers. You can start on Atlas and later self-host, or the reverse, using the same drivers and query language. Several other databases also implement MongoDB wire-protocol compatibility, though feature coverage varies. Licensing is the caveat. MongoDB's server has been under the Server Side Public License since 2018, which restricts offering MongoDB as a service to third parties and is not OSI-approved. For ordinary application use this rarely matters, but it does matter if you are building a hosting platform. For teams whose main concern is leaving one cloud provider, MongoDB is clearly the more portable option.
Search, vectors and analytics beyond key lookups
Modern apps often need more than CRUD: full-text search, faceting, semantic or vector search for AI features, and some reporting. MongoDB covers much of this in one platform. Atlas Search and Atlas Vector Search run alongside the database and stay synced automatically, and as of mid-2026 search and vector search are generally available for self-managed Community Edition via the mongot process. The aggregation pipeline handles a fair amount of operational analytics. DynamoDB deliberately does none of this. Full-text and vector search typically mean a zero-ETL integration or a Streams-based pipeline into Amazon OpenSearch Service, and analytics means exporting to S3 for Athena or a warehouse. That architecture scales well, but it is more moving parts, more services to pay for and more consistency lag to reason about. If search or retrieval-augmented generation is core to your product, MongoDB lets a small team ship faster with a single datastore. If those needs are peripheral, or you already run OpenSearch and a lake on AWS, DynamoDB plus purpose-built services remains a clean, scalable design.
Verdict
Choose DynamoDB if your team is committed to AWS, your access patterns are stable and known, and you value zero operations and limitless scale above query flexibility. Choose MongoDB if your product is still evolving, you need ad-hoc queries, aggregations, full-text or vector search, or you want to avoid tying your data layer to one cloud. For most early-stage products MongoDB is the more forgiving choice; for high-scale, well-understood workloads on AWS, DynamoDB is very hard to beat on operational simplicity.