DynamoDB vs MongoDB: Serverless NoSQL Cost & Scale Compared
DynamoDB vs MongoDB Atlas 2026 — serverless NoSQL cost comparison, write capacity units vs Atlas credits, flexible schema, and which to choose at scale.
Quick Answer
DynamoDB wins for predictable AWS-native serverless workloads where access patterns are known upfront; MongoDB Atlas wins for flexible schemas, ad-hoc queries, and teams that cannot pre-define every access pattern. DynamoDB is cheaper at massive scale with consistent patterns; MongoDB Atlas is cheaper for variable/exploratory query loads.
DynamoDB vs MongoDB Atlas: Overview
High-throughput key-value lookups, session storage, IoT, AWS-native architectures
Always-free: 25GB storage, 25 WCU, 25 RCU per month forever
On-demand: $1.25/million write requests, $0.25/million read requests, $0.25/GB/mo storage
Flexible schemas, complex queries, content apps, ML feature stores, global scale
Atlas M0 free: 512MB, shared cluster, no credit card required
Atlas Serverless: $0.10/million reads, $1.00/million writes, $0.10/GB/mo; Dedicated M10 from $57/mo
DynamoDB vs MongoDB Atlas: Feature Comparison
| Feature | DynamoDB | MongoDB Atlas |
|---|---|---|
| Read cost (per million) | $0.25 (on-demand RCUs) | $0.10 (Atlas Serverless) |
| Write cost (per million) | $1.25 (on-demand WCUs) | $1.00 (Atlas Serverless) |
| Ad-hoc query support | Limited — requires GSI pre-definition | Full aggregation pipeline, any field |
| Multi-region active-active | Global Tables, <1s cross-region | Atlas Global Clusters with zone-based routing |
| Vendor lock-in | High — AWS-proprietary API | Low — runs on AWS, GCP, Azure |
| Peak latency guarantee | Single-digit ms p99, SLA-backed | Varies by tier — no serverless p99 SLA |
Pros & Cons
DynamoDB
Pros
- Single-digit millisecond p99 latency at any scale — proven at Amazon.com's 89,000 requests/sec peak
- Serverless on-demand billing: $1.25/million writes and $0.25/million reads — zero idle cost
- DynamoDB Streams + Lambda enables real-time event-driven pipelines without separate message queues
- Global Tables provide multi-region active-active replication with <1s cross-region consistency
- DAX (DynamoDB Accelerator) caching layer adds microsecond read latency on hot items
Cons
- Access patterns must be defined upfront — ad-hoc queries without GSIs require full table scans at high cost
- No multi-item ACID transactions across tables with different partition keys without TransactWrite
- Pricing becomes unpredictable at high write volumes: 1B writes/month = $1,250 in write costs alone
- Vendor lock-in: DynamoDB API is AWS-proprietary with no open-source self-host equivalent
MongoDB Atlas
Pros
- Atlas free M0 tier: 512MB on shared cluster — larger than DynamoDB free tier for prototype data
- Ad-hoc queries via aggregation pipeline without pre-defining indexes or access patterns
- Atlas Search adds full-text and vector search (Atlas Vector Search) without separate Elasticsearch
- Atlas Serverless charges $0.10/million reads vs DynamoDB's $0.25/million — 2.5x cheaper on reads
- MongoDB Atlas runs on AWS, GCP, and Azure — avoids cloud vendor lock-in
Cons
- Atlas write pricing ($1.00/million writes) matches DynamoDB at scale but with less predictable throughput SLAs
- Atlas Serverless has throughput limits of 1,000 RU/sec — dedicated clusters required for sustained bursts
- Multi-document ACID transactions incur 3-5x overhead vs single-document operations
- Atlas M0 free tier has strict limits: 100 max connections, no backup, shared resources
Our Verdict: DynamoDB vs MongoDB Atlas
Use DynamoDB if you are all-in on AWS, your access patterns are predictable key-value or range lookups, and you need AWS-native integrations (Lambda, Streams, DAX). The $1.25/million write cost is affordable when patterns are consistent and GSIs cover your queries. Use MongoDB Atlas if your schema evolves frequently, your team needs ad-hoc aggregation queries, you want to avoid AWS lock-in, or you need full-text/vector search via Atlas Search. Atlas Serverless reads at $0.10/million are 2.5x cheaper than DynamoDB for read-heavy applications.
DynamoDB vs MongoDB Atlas — FAQs
How does DynamoDB pricing compare to MongoDB at 100M requests/day?
At 100M requests/day (3B/month) with a 90% read, 10% write ratio: DynamoDB costs approximately $750/month (2.7B reads × $0.25/million + 300M writes × $1.25/million). MongoDB Atlas Serverless costs approximately $570/month (2.7B reads × $0.10/million + 300M writes × $1.00/million). MongoDB wins on pure API cost at this ratio. However, if your workload is write-heavy (50% writes), DynamoDB becomes competitive. At 1B writes/month, DynamoDB costs $1,250 vs MongoDB $1,000 — both expensive, and dedicated clusters become more economical than serverless pricing.
Can DynamoDB replace MongoDB for a content management system?
DynamoDB can replace MongoDB for a CMS only if you define your access patterns upfront and accept limitations on ad-hoc queries. For a simple blog with patterns like "get post by slug" and "list posts by author", DynamoDB works well with a partition key design. Where it breaks down: reporting queries ("find all posts published in March with >1000 views tagged 'AI'"), schema evolution (adding new metadata fields requires data migration or sparse attributes), and search functionality (requires Elasticsearch/OpenSearch integration). MongoDB handles all these natively. Most CMS projects benefit from MongoDB's flexibility.
What is the DynamoDB free tier and is it genuinely usable for side projects?
DynamoDB's always-free tier includes 25GB storage, 25 Write Capacity Units/sec, and 25 Read Capacity Units/sec. In practice, 25 WCU/25 RCU on provisioned mode supports approximately 25 writes/sec and 50-200 reads/sec (depending on item size) — enough for a low-traffic side project or development environment. The free tier is always-free (not 12-month trial) and does not expire. For comparison, MongoDB Atlas M0 free tier gives 512MB storage on a shared cluster with higher read throughput for mixed workloads, but DynamoDB's always-free 25GB is significantly more storage.
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