After running MongoDB, DynamoDB, and Cosmos DB in production for the last three years, I can tell you the marketing bullshit ends when you're debugging a 3am outage. Here's what actually happens when you pick each one.
NoSQL Database Architecture Reality: These three databases represent fundamentally different approaches to data storage and scaling. MongoDB uses document collections with replica sets for high availability. DynamoDB uses hash-partitioned key-value storage across multiple partitions. Cosmos DB abstracts everything behind Request Units and global distribution. The architecture choice determines your failure modes.
MongoDB: Great Until You See the Bill
MongoDB Atlas Pricing Reality: A basic M10 cluster starts at $57/month, but production-ready M40 clusters cost $5,000+/month across multiple regions.
MongoDB started as the darling of the NoSQL world because it's actually easy to learn. You can throw JSON at it and it works. No schema migrations, no complex joins - just store your data and query it later.
Then you hit production and realize MongoDB Atlas charges you like you're buying a luxury car. A decent cluster starts at $500/month minimum and scales up faster than your AWS bill after leaving an EC2 instance running.
The worst part? MongoDB 8.0 had critical issues SERVER-95067 and SERVER-94559 affecting time-series collections that weren't fully resolved until 8.0.4. Spent 6 hours debugging time-series bucket insertion problems when upgrading from 7.0. These bugs caused measurements to generate multiple batches referencing the same bucket, corrupting our IoT data pipeline. The MongoDB team barely mentioned these in the release notes—had to dig through Jira tickets to understand what broke.
Real MongoDB disasters from the trenches:
The Index Performance Cliff: Compound index queries suddenly became slow after hitting 10M documents. Query that ran in 50ms suddenly took 30 seconds. MongoDB's query planner decided to use the wrong index, and the
hint()operator became our best friend.Connection Pool Death Spiral: Atlas connection pooling maxed out during Black Friday traffic spikes.
MongoServerSelectionError: Server selection timed out after 30000 mskilled our API. Connection pool exhaustion is MongoDB's favorite way to ruin your high-traffic day.Cross-Region Bill Shock: Cross-region replication costs tripled our bill when we went global. $800/month became $2,400/month overnight because Atlas charges for data transfer between regions. Nobody told us replica set reads from secondary nodes in other regions aren't free.
The Memory Mystery: Memory usage spiked randomly causing OOM kills on perfectly sized instances. WiredTiger cache would bloat to 80% of system RAM during routine operations, killing our Node.js processes. Memory patterns in MongoDB make about as much sense as JavaScript equality comparisons.
DynamoDB: AWS Heroin - Easy to Start, Impossible to Quit
DynamoDB's Physical Partition Model: Data is automatically distributed across multiple physical partitions based on your partition key. Each partition is a 10GB allocation of SSD storage replicated across multiple Availability Zones.
DynamoDB Partition Distribution: Each physical partition can handle up to 3,000 read capacity units or 1,000 write capacity units per second, automatically distributed across Availability Zones.
DynamoDB's Partition-Based Architecture: DynamoDB uses a partition key to distribute your data across multiple physical partitions. Each partition can handle up to 3,000 read capacity units or 1,000 write capacity units per second. This design enables massive scale but creates hot partition problems when your data access patterns aren't evenly distributed.
DynamoDB feels like magic when you start. Single-digit millisecond latency! Serverless scaling! No database admin bullshit! Then you try to do anything beyond key-value lookups and realize you've sold your soul to AWS.
The 400KB item size limit will bite you in the ass eventually. That product catalog with high-res images? You're fucked. User profiles with extensive activity logs? Start splitting data across multiple items and enjoy the complexity.
DynamoDB landmines that exploded in production:
The Black Friday Throttling Massacre: `ProvisionedThroughputExceededException` errors destroyed our checkout flow during peak traffic. Auto-scaling couldn't keep up with demand spikes, and customers got error pages instead of purchase confirmations. DynamoDB's scaling isn't as instant as they claim.
Hot Partition Hell: Hot partition issues because some genius used
user_idas the partition key for a social media app. Power users with millions of followers throttled the entire partition. Fixed by switching touser_id#timestampcompound keys, but lost two weeks redesigning the data model.Global Tables Lag Nightmare: Global Tables replication lag hit 45+ seconds during peak hours in Asia-Pacific regions. Users saw stale data, duplicate posts, and inconsistent state across regions. "Eventually consistent" became "eventually working" in our status updates.
The Scan That Killed Production: Scan operations destroyed performance when marketing wanted a quick user export. One full table scan consumed our entire read capacity and throttled legitimate user traffic for 3 hours. DynamoDB scans are basically denial-of-service attacks against your own application.
Cosmos DB: Microsoft's Expensive Science Experiment
Cosmos DB's Global Multi-Master Architecture: Unlike MongoDB replica sets or DynamoDB Global Tables, Cosmos DB supports true multi-master writes across regions simultaneously. All regions can accept writes and resolve conflicts automatically using configurable conflict resolution policies.
Azure Cosmos DB's Global Multi-Model Design: Cosmos DB provides multiple database APIs (SQL, MongoDB, Cassandra, Gremlin, Table) all backed by the same underlying storage engine. Data is automatically replicated across Azure regions with five consistency levels to choose from. This flexibility comes at the cost of complexity in understanding Request Unit consumption patterns.
Cosmos DB is what happens when Microsoft tries to out-engineer AWS and ends up with something that works great but costs more than hiring a dedicated database engineer. The Request Unit model is impossible to predict without a PhD in distributed systems and a crystal ball for usage patterns.
Cosmos DB "WTF just happened to my bill" moments:
The Invisible Index Rebuild Catastrophe: RU consumption spiked 10x during an index rebuild that Azure triggered automatically. No warning, no notification—just a $3,000 bill spike when background indexing consumed 50,000 RUs we didn't provision. Azure's "helpful" automatic optimizations nearly killed our budget.
Multi-Region Write Chaos: Multi-region write conflicts corrupted user data until we implemented proper conflict resolution. Same user updated their profile from different regions simultaneously, creating zombie records that couldn't be resolved. Cosmos DB's "last writer wins" default policy is about as sophisticated as a coinflip.
Consistency Level Surprise Surgery: Consistency level changes required emergency application code changes we didn't plan for. Switching from Strong to Session consistency broke our real-time features because we assumed immediate read-after-write consistency. Three days of scrambling to add retry logic everywhere.
The Most Unhelpful Error Messages: Throttling errors with cryptic messages like "Request rate is large." No details on which operation, no guidance on optimization—just Microsoft's version of "something went wrong, figure it out yourself."
The brutal reality check: MongoDB is great for startups with VC money to burn on developer happiness. DynamoDB works if you're all-in on AWS and can design around its bizarre limitations. Cosmos DB is perfect if you love Azure's enterprise features and have a CFO who doesn't ask questions about database bills.
Each of these databases excels at different things, but they all share one trait: they'll find creative ways to surprise you when you least expect it. But the real pain comes from the costs you never see coming. Let me break down exactly how these databases will destroy your budget in ways the pricing calculators conveniently forget to mention.