DynamoDB vs MongoDB: Which to Use? (2026)

The Night I Compared Two Databases I Didn’t Need

In 2023, building Clickly — the Bitly-style URL shortener I turned into an accidental database degree — I had a very specific type of insomnia. It wasn’t “will anyone use this.” It was “which NoSQL store handles ten million clicks a day for the cheapest,” a problem I did not have, for a scale I never reached.

Two names kept circling. DynamoDB, because every AWS talk on YouTube treated it like the final boss of scale. And MongoDB, because every AI side-project on Twitter was built on it, JSON thrown around like confetti. Both are “NoSQL.” Both promised to scale past anything a relational database could. So one night I opened two dozen tabs and tried to figure out which one my zero users deserved.

Here’s what took me embarrassingly long to realize: calling them both “NoSQL” is like calling a motorcycle and a cargo ship both “vehicles.” Technically true, wildly unhelpful. They solve different problems, bill you in different shapes, and one of them quietly handcuffs you to AWS forever. This is the comparison I wish someone had handed me at 1 AM.

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Two NoSQL Databases, Two Completely Different Shapes

Start with what each thing actually is, because the marketing blurs it on purpose.

DynamoDB is a managed key-value store that also does documents. You hand it a key, it hands you back an item — a single-digit-millisecond lookup at basically any scale, no matter how much data you throw at it. It’s fully serverless: there’s no box to rent, no CPU to size, no version to patch. You don’t run DynamoDB, you rent access to it. It stores schemaless items (which can be nested, document-ish JSON), but its whole soul is that key-value lookup. It is genuinely brilliant at exactly one thing: “give me this record, right now, insanely fast, forever.”

MongoDB is a document database. Instead of items keyed by a single value, you have collections of documents — fat JSON (BSON, technically) objects that can nest orders inside a user, comments inside a post, whatever your app already thinks about. No enforced schema by default. Two documents in the same collection can look nothing alike. The pitch is that your data comes back shaped exactly like your code expects, no assembly required. And crucially, Mongo is software you run — on your laptop, on a droplet, on any cloud, or on their managed hosting, MongoDB Atlas.

That last distinction is the one that matters more than any benchmark. DynamoDB is a service you can only get from AWS. MongoDB is a product you can run anywhere. Hold that thought — it comes back to bite at the end.

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Query Flexibility: The Straitjacket vs the Swiss Army Knife

This is where the two diverge hardest, and where founders get hurt.

DynamoDB makes you design your database around your queries before you write a line of app code. You pick a partition key and a sort key, and those decide what you can look up efficiently. Want to fetch data a different way later — “all orders for this customer” when your key is order ID? You bolt on a Global Secondary Index, and now every write to your table also writes to that index, and you pay for each one. This is the famous “single-table design” that AWS experts write whole books about. It works, and it’s fast, but it demands you know your access patterns on day one — the exact day you know the least about your product.

Mongo is the opposite temperament. You throw documents in, and you can query almost any field, ad hoc, whenever you feel like it. Its query language and aggregation pipeline are rich — filtering, grouping, joins across collections with $lookup, geospatial, text search. You add an index when a query gets slow, and Mongo just includes it; there’s no per-index billing meter running like there is with Dynamo’s GSIs. For a product still finding its shape — which is every product you’re building right now — that difference is enormous. Mongo lets you change your mind. Dynamo makes you commit before you have the information to commit with.

I lived this. With Dynamo I’d sketch a table, realize I needed a second query field, then a third, and end up staring at four indexes and a bill that made no sense. With Mongo I just… wrote the query. That flexibility is the single best reason to pick Mongo over Dynamo if you’ve already decided you want NoSQL.

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Scaling and Operations: Who Does the Work

Both scale further than you will ever need. The question is who sweats to get there.

DynamoDB does the sweating for you. This is its genuine superpower. No servers, no replicas to configure, no failover to test, no “database at 90% CPU” page at 2 AM. It absorbs traffic spikes and shrinks back down, and at millions of requests per second it doesn’t flinch. If your workload is a firehose with a predictable shape — an ad-tech pipeline, a gaming leaderboard, IoT ingestion — this is home turf, and the operational silence is worth real money.

MongoDB gives you more control and more responsibility. It scales horizontally through sharding — spreading data across many machines — and MongoDB 8.0, the current major line in 2026, made sharding meaningfully faster and cheaper to start. But sharding is a decision you make and manage. On Atlas, their managed cloud, a lot of that pain is smoothed over — backups, scaling, failover — but you’re still picking cluster tiers and thinking about it in a way you never do with Dynamo.

The honest summary: Dynamo trades your design freedom for zero ops. Mongo trades some ops for the freedom to not plan your whole data model in advance.

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The Pricing Models Are Not Even the Same Species

I won’t rebuild the full spreadsheets here — I’ve already broken each one down in its own post — but you have to understand that these two bill you in fundamentally different ways, and both have traps.

DynamoDB bills by throughput, not by server. You pay per read and per write, measured in “capacity units,” plus storage. There’s no monthly minimum for a box, which sounds cheap — a million writes for the price of a coffee. The trap is that it’s illegible: your bill depends on item sizes, how many indexes you added, how consistent your reads are, and whether your access pattern creates a “hot partition.” Those Global Secondary Indexes I mentioned can quietly double or triple your write bill. The exact 2026 numbers, and the gotchas that wreck people, are in the DynamoDB pricing breakdown.

MongoDB Atlas bills by cluster. There’s a genuinely free tier (M0, 512 MB, fine for a prototype), a capped mid-tier, and then a cliff — the jump to a real dedicated production cluster starts near $57/month before you add backups and data transfer. It’s more predictable than Dynamo’s throughput math, but that cliff catches every founder off guard. The full tier-by-tier map is in the MongoDB Atlas pricing post.

The thing neither landing page tells you: at $0 MRR, both are asking you to reason about infrastructure you don’t have the traffic to justify. One makes you a capacity-planning accountant; the other makes you leap a $57 cliff to escape a shared box. Neither is where a broke founder should spend brain cells.

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Lock-In: The Part That Actually Decides It

Here’s the one that reorganized my whole thinking, and it has nothing to do with speed or price.

DynamoDB only exists inside AWS. Its query model is nothing like SQL and nothing like anything else. Once your application is built around partition keys, sort keys, and single-table design, leaving DynamoDB isn’t a migration — it’s a rewrite. You are, permanently, AWS’s tenant. If AWS raises prices, changes terms, or you simply want to move to a cheaper cloud, your data model doesn’t come with you. That’s not a bug in DynamoDB; it’s the deal. Deep AWS integration is exactly what makes it so good and exactly what makes it a cage.

MongoDB runs everywhere. Host it yourself on a $5 droplet, run it on Atlas across AWS, Google Cloud, or Azure, and move between them. The asterisk: Mongo is a single commercial company steering the product, and it changed its license to the controversial SSPL back in 2018 — so “open” comes with a footnote. But compared to Dynamo, where the exit door is bricked over, Mongo is a rented apartment you can actually leave.

For a solopreneur, lock-in isn’t an abstract philosophy problem. It’s the difference between “I switched hosts and saved $40/month” and “I can’t switch without three weeks of rewriting.” I’ve been the second guy. It’s a bad guy to be.

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When Each One Genuinely Earns Its Place

I’m not here to trash either. They’re both phenomenal engineering, used by serious companies for serious reasons. So, honestly:

Reach for DynamoDB when you’re already all-in on AWS — your Lambda functions, queues, everything lives there — and you have a known, simple access pattern at genuinely large scale. Key-value lookups, millions per second, zero-ops, no same-region egress. If that’s your product, Dynamo is close to perfect and its price is fair.

Reach for MongoDB when you want document flexibility without committing to a cloud, your data is legitimately tree-shaped and variable (CMS content, sprawling product catalogs), or you have a write-heavy ingestion workload — logs, events, sensor data — where its update model shines. And if your team already knows Mongo cold, that alone can be reason enough.

Between the two, for a founder who’s decided on NoSQL but isn’t married to AWS, Mongo is the safer pick — more flexible, more portable, less likely to trap you. Dynamo is the specialist you call when you already live in AWS and know your query shape by heart.

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The Broken-Engineer Verdict

Now the part I actually believe, after pricing both databases I never shipped: for roughly 99% of the SaaS products you’ll build, you need neither of these.

You need PostgreSQL on Neon. Both DynamoDB and MongoDB exist to solve “my data is unstructured” and “I need to scale past relational.” But Postgres has a JSONB column type that stores schemaless documents right inside a relational table — you get the throw-JSON-at-it flexibility that draws people to Mongo, plus real joins, transactions, and correctness for the day your data grows up. And “scale past relational”? You’ll run out of runway and ambition before you outscale a well-run Postgres. I dug into that whole comparison in MongoDB vs PostgreSQL, and the short version is: the great SQL-vs-NoSQL war basically ended when Postgres learned to do documents.

On Neon (now a Databricks company) I keep seven databases on the free tier for $0, pausing on idle so a quiet project costs almost nothing. No capacity units to price. No $57 cliff. No AWS cage. No cluster tiers. Just a WHERE clause and a product to ship.

So my honest routing, the one I’d give my 1 AM self staring at two dozen tabs:

• Default: Postgres on Neon, with a jsonb column for the genuinely messy parts. This is where you should be.
• If you’ve proven you need NoSQL and you’re not welded to AWS: MongoDB, for the flexibility and the portability.
• Only if you live deep in AWS serverless with a known, huge-scale access pattern: DynamoDB.

The whole point of the broke solopreneur’s survival guide is that every hour you spend picking between two databases you don’t need is an hour you’re not spending finding a single customer. I know, because I spent that night, and Clickly still never launched. I got a whole over-engineered database curriculum out of it. You can just get the answer.

Don’t compare two databases you’ll never ship. I already did that for both of us.

This is the Broken Engineer Guide — I over-engineer everything, fail at business, and hand you the receipts so you don’t have to. Now close the tabs and go build something.