Choosing the right NoSQL database is one of the most critical decisions for any modern, cross-platform web application. With titans like Azure Cosmos DB, MongoDB, and Amazon DynamoDB each offering powerful solutions for scalability and performance, how do you decide which is best for your project’s specific needs? This practical, no-nonsense 2025 buyer’s guide cuts through the noise. We’ll dive deep into a head-to-head comparison focusing on what truly matters: global scale, query flexibility, total cost of ownership, and developer experience. Get ready to explore live filters, interactive charts, and a visual decision tree designed to help you confidently land on the right choice for your app. GigXP — Cosmos DB vs MongoDB vs DynamoDB: The 2025 Buyer’s Guide

GX GigXP.com Interactive Buyer’s Guide

Databases · 2025 Edition

Cosmos DB vs MongoDB vs DynamoDB

A practical, no‑nonsense comparison focused on global scale, developer velocity, cost control, and ecosystem fit. Explore live filters, interactive charts, and a visual decision tree to land on the right choice for your app.

Global Scale Query Flexibility TCO & Cost Multi‑Cloud Offline & Sync

Quick Take

Cosmos DB: Turn‑key global distribution + consistency controls. Best if you’re Azure‑first and need planet‑scale with SLAs.
MongoDB: Rich queries + multi‑cloud portability + Realm/Device Sync for offline. Best for flexibility and complex querying.
DynamoDB: Massive scale + low ops + great cost efficiency on AWS. Best for high‑throughput, key‑value workloads.

GigXP Quick Pick

Azure stack & global writes? Cosmos DB.
Multi‑cloud or offline‑first? MongoDB.
AWS serverless & spiky scale? DynamoDB.

Feature Comparison

Capability	Cosmos DB	MongoDB	DynamoDB
Global distribution (multi‑region)	✓ Active‑active; tunable consistency	◇ Global clusters via sharding/replicas	✓ Global Tables (multi‑active)
Latency target (local, p99)	Single‑digit ms (regional)	Low ms (near primary/replica)	Low ms (regional)
Query flexibility	SQL‑like + auto‑index; multi‑model	Aggregation pipeline, rich indexes	Key/partition queries + GSIs; PartiQL
Multi‑document ACID	Within partition (batches/SPs)	✓ Across documents & shards	Transactions (bounded size)
Offline/mobile sync	Custom (change feed)	✓ Realm/Device Sync	AppSync + Amplify DataStore
Pricing model	RU/s (provisioned/autoscale) or serverless	Instance tier (Atlas) or self‑hosted	Provisioned or On‑Demand (per request)
Ecosystem fit	Azure‑native (Functions, Synapse Link)	Multi‑cloud + broad tooling	AWS‑native (Lambda, AppSync, IAM)
Lock‑in / portability	Azure‑only service	✓ Runs anywhere / Atlas multi‑cloud	AWS‑only service
Best for	Azure teams needing turnkey global	Flexible queries, multi‑cloud, offline	High‑throughput key‑value on AWS

Filter by What You Care About

Interactive Charts

Capability Profile (Radar)

Feature Heatmap (1–10)

GlobalQueryCostOpsPortabilityWrites

“Cost” ≈ cost‑efficiency; “Ops” ≈ operational simplicity; “Writes” ≈ sustained write throughput.

Throughput vs. Relative Monthly Cost Index

Reads/sec: 1500

Writes/sec: 500

Avg item size (KB): 1

Regions

Mode

Visual Decision Tree

FAQ

Can I mix them?

Yes. Many teams use polyglot persistence: e.g., DynamoDB for high‑volume events, MongoDB for content & search, Cosmos for Azure‑native microservices with change feed. Keep boundaries clear and document data ownership.

What about migrations later?

Migrations across these systems are possible but non‑trivial. Prefer portable data shapes (JSON), avoid engine‑specific features unless they deliver outsized value, and keep ingestion/egress pipelines handy.

Deep Research Modules

Consistency Spectrum & Multi‑Region Trade‑offs

Strong ⇄ Bounded‑Staleness ⇄ Session ⇄ Consistent‑Prefix ⇄ Eventual. Lower consistency often improves perceived latency and availability in geo deployments, at the cost of potential read staleness and conflict handling.

Cosmos DB: five levels built‑in; per‑request overrides supported; multi‑master conflict resolution (LWW/custom) available.
MongoDB (Atlas): Primaries handle writes; reads from secondaries are eventually consistent. Global clusters use zone‑sharding for regional locality.
DynamoDB: Eventually consistent by default; strongly consistent reads (same region); Global Tables replicate asynchronously with last‑writer‑wins.

Scenario Playbooks

Global SaaS (multi‑region writes)

Pick: Cosmos DB (multi‑master + consistency levels). Alt: DynamoDB Global Tables if AWS‑native.

Offline‑first Mobile

Pick: MongoDB (Realm/Device Sync). Alt: AppSync + DynamoDB.

Serverless with Spiky Traffic

Pick: DynamoDB (On‑Demand). Alt: Cosmos Serverless for bursty workloads.

Content & Search‑heavy

Pick: MongoDB (rich aggregation, text search). Alt: Cosmos (SQL API) with Synapse Link.

IoT Ingestion (high writes)

Pick: DynamoDB (high write throughput). Alt: Cosmos with well‑chosen partition key.

Hybrid / Multi‑cloud

Pick: MongoDB (Atlas or self‑host). Alt: —

Security, Compliance & Ops Matrix

Control	Cosmos DB	MongoDB (Atlas)	DynamoDB
Encryption at rest / in transit	✓ Yes / Yes	✓ Yes / Yes	✓ Yes / Yes
Customer‑managed keys (BYOK)	✓ Yes (AKV)	✓ Yes (KMS/AKV/GCP KMS)	✓ Yes (KMS)
Private networking	✓ Private Link	✓ PrivateLink / VNet Peering	✓ VPC Endpoints
AuthN/AuthZ	✓ Azure AD RBAC	✓ Atlas RBAC, SCIM; SSO	✓ AWS IAM, fine‑grained
Backup & PITR	✓ Periodic & Continuous	✓ Snapshots & PITR	✓ On‑demand & PITR
Auditing / Logs	✓ Azure Monitor / Activity	✓ Atlas Auditing	✓ CloudWatch / CloudTrail

Always verify regional compliance (e.g., data residency) and certifications (SOC, ISO, HIPAA) for your tenant/region.

Limits & Quotas (Quick Reference)

Limit	Cosmos DB	MongoDB	DynamoDB
Max item/document size	≈ 2 MB	≈ 16 MB	≈ 400 KB
Transactions	Within partition key scope	Multi‑document (incl. sharded)	Up to 25 items / 4 MB per txn
Secondary indexes	Auto‑index (configurable)	User‑defined (rich types)	LSI/GSIs (plan ahead; up to ~20 GSIs)
Change stream	Change Feed	Change Streams	DynamoDB Streams

Values are representative; check current docs for your region and API level.

Cost Optimization Cookbook

Cosmos DB

Choose high‑cardinality, well‑distributed partition keys; avoid hot partitions.
Trim indexing policy (exclude rarely queried paths); prefer point reads by id + partition key.
Use Autoscale prudently or Serverless for intermittent loads.
Co‑locate items that transact together under the same partition key.
Leverage TTL for ephemeral data; use Synapse Link for analytics to offload heavy queries.

MongoDB

Design documents for read patterns; avoid super‑large arrays/embedded docs.
Create compound indexes that match query predicates and sort order; use projections.
Use Atlas Performance Advisor; offload full‑text to Atlas Search.
Archive cold data; consider zone sharding to localize regional traffic.
Tune connection pools and timeouts; watch working set vs RAM.

DynamoDB

Favor single‑table design; model access patterns up front.
Use On‑Demand for unpredictable spikes; switch to provisioned + auto‑scaling when steady.
Avoid scans; add GSIs for new query patterns; batch writes/reads.
Consider DAX for hot read paths; compress large attributes or move blobs to object storage.
For Global Tables, write only where needed to limit replication cost.

Migration Cheat Sheets

MongoDB → Cosmos (Mongo API)

Inventory features (aggregation stages, transactions) used; check Cosmos Mongo API compatibility.
Export/import via mongodump/restore or Data Migration Tool; validate index definitions.
Benchmark RU charges for hot queries; refine indexing policy.

SQL → Cosmos (SQL API)

Denormalize data: embed related data into single documents to reduce joins.
Choose a partition key that evenly distributes requests (e.g., `userId`, `tenantId`).
Migrate data using Azure Data Factory or custom scripts.
Rewrite queries from SQL to Cosmos DB's SQL-like syntax.

Any → DynamoDB

Model access patterns FIRST. Design primary keys and GSIs for all queries.
Use AWS DMS or custom scripts for data migration.
Rewrite application logic to use DynamoDB's key-value query model.
Leverage single-table design patterns where possible.

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