Neon vs DynamoDB for Solo Developers

Comparing Neon and DynamoDB for solo developers. Serverless Postgres vs AWS NoSQL. Features, pricing, pros and cons, and which one to pick.

Quick Comparison

Feature	Neon	DynamoDB
Type	Serverless PostgreSQL (14, 15, 16, 17 stable; 18 preview)	Fully managed NoSQL (key-value/document)
Pricing	Free $0/mo, then usage-based Launch and Scale (storage from $0.35/GB-month)	Always-free 25 GB storage tier, then pay-per-request ($0.625/M writes, $0.125/M reads)
Free tier	0.5 GB storage, 100 CU-hours, 10 branches, 100 projects	25 GB storage always free; 25 WCU + 25 RCU free in provisioned mode only
Learning Curve	Easy	Hard
Best For	Serverless Postgres for side projects and startups	AWS-native apps needing key-value storage at scale
GitHub stars	22,075 (neondatabase/neon, Rust)	Closed source (AWS managed service)
Solo Dev Rating	9/10	5/10

Neon Overview

Neon is serverless PostgreSQL that scales to zero. You get a full Postgres database with branching, autoscaling, and a generous free tier. Every Postgres tool, ORM, and framework works with it because it is Postgres. No compatibility layers, no surprises.

For solo developers, Neon hits the sweet spot. The free tier handles side projects and early-stage products, giving you 0.5 GB of storage, 100 compute-hours, and up to 10 branches per project across 100 projects. When you outgrow free, the paid Launch and Scale plans are usage-based rather than a flat monthly fee, so the bill tracks what you actually run. Database branching means you can test migrations against a copy of production data in seconds. And because it's just Postgres, you carry zero learning overhead.

I keep coming back to Neon for new TypeScript and Python projects. The cold start times are fast enough that scale-to-zero doesn't cause issues. The connection pooling works well with serverless functions. It's the kind of tool where you set it up once and forget about it.

DynamoDB Overview

DynamoDB is AWS's flagship NoSQL database. It promises single-digit millisecond performance at any scale, and it delivers. The tradeoff is that you have to completely rethink how you model data. No joins, no flexible queries. You design your table structure around your access patterns, and you better get those patterns right upfront.

The always-free tier gives you 25 GB of storage with no expiration, plus 25 write capacity units and 25 read capacity units each month that together can handle roughly 200 million requests. One thing worth knowing upfront: that 25 WCU and 25 RCU allowance only applies to provisioned-capacity tables. If you run on-demand mode, you are billed per request from the very first read or write, with no free request offset, though the 25 GB storage allowance still applies. Pay-per-request pricing means you only pay for actual database operations, which can be cost-effective for low-traffic apps or spiky workloads.

I've built projects on DynamoDB and the performance is real. Reads are instant. Writes are instant. But the mental overhead of data modeling is significant. Every new feature requires asking "how does this fit my partition key strategy?" That's not a question you want to deal with when you're trying to ship fast as a solo developer.

Key Differences

SQL vs NoSQL changes everything about how you build. With Neon, you write SQL. You join tables. You filter with WHERE clauses. You aggregate with GROUP BY. Your ORM handles the mapping. With DynamoDB, you design access patterns, define partition keys, create secondary indexes, and build your queries around those constraints. SQL is flexible. DynamoDB is performant but rigid.

Data modeling effort is wildly different. In Neon/Postgres, you normalize your data into tables and query however you need. Later, you add a new query? Write a new SQL statement. In DynamoDB, adding a new query pattern might require a Global Secondary Index (default quota of 20 per table, with a separate cap of 5 local secondary indexes) or restructuring your data. There is also a hard 400 KB ceiling on any single item, so large blobs need to live somewhere else. For solo developers iterating on features quickly, Postgres gives you room to experiment. DynamoDB punishes changing your mind.

Cost at low scale favors Neon's simplicity. Both have free tiers. But Neon's pricing is predictable: free or $19/mo. DynamoDB's pricing depends on read/write units, storage, data transfer, and whether you use on-demand or provisioned capacity. I've seen DynamoDB costs surprise people who didn't model their capacity correctly.

Vendor lock-in is a DynamoDB problem. Neon uses standard Postgres. You can take your schema and data to any Postgres host. DynamoDB uses a proprietary API that only exists on AWS. Your code, your data models, your query patterns are all AWS-specific. Migrating off DynamoDB means rewriting your data layer from scratch.

Local development experience matters. With Neon, you can run a local Postgres instance for development. Same queries, same behavior. With DynamoDB, you need DynamoDB Local (a Java application that simulates DynamoDB). It's not identical to production and adds friction to your dev setup.

By the Numbers (2026)

Here is the verifiable state of both tools as of late May 2026, pulled from vendor pricing pages, official docs, and the package registries.

Neon

Engine support: PostgreSQL 14, 15, 16, and 17 are stable, with PostgreSQL 18 available as a preview release per Neon's version policy.
Free plan: $0/month with 0.5 GB storage per project, 100 CU-hours of compute per project, 10 branches per project, and up to 100 projects.
Launch plan: usage-based at $0.35 per GB-month of storage and $0.106 per CU-hour of compute, with 10 branches included per project and extra branches at $1.50 per branch-month.
Scale plan: same $0.35 per GB-month storage, compute at $0.222 per CU-hour, 25 branches included per project, and up to 1,000 projects.
Open source core: the neondatabase/neon repository sits at 22,075 GitHub stars, written primarily in Rust, with the most recent push on 2026-05-25.
Driver adoption: the @neondatabase/serverless npm package is at version 1.1.0 with 1,993,558 downloads in the week of 2026-05-21 to 2026-05-27.

DynamoDB

Always-free tier: 25 GB of standard table-class storage with no expiration, plus 25 provisioned WCU and 25 provisioned RCU per month (provisioned mode only) covering roughly 200 million requests.
On-demand pricing: $0.625 per million write request units and $0.125 per million read request units, with standard storage at $0.25 per GB-month (Standard-IA at $0.10 per GB-month) in US East (N. Virginia).
Hard limits: a default quota of 20 global secondary indexes per table, up to 5 local secondary indexes per table, a 400 KB maximum item size, and an initial 2,500 tables per Region.
Throughput ceiling: 40,000 read request units and 40,000 write request units per table in on-demand mode by default, adjustable via a service quota increase.
SDK adoption: the @aws-sdk/client-dynamodb npm package is at version 3.1056.0 with 7,094,587 downloads in the week of 2026-05-21 to 2026-05-27.

Real Cost at Solo-Dev Scale

Pricing tables are abstract, so let me run a concrete workload through both. Take a realistic solo-dev side project that is past the toy stage but nowhere near viral.

Assumptions

5 GB of stored data.
2 million writes per month.
8 million reads per month.
A single small compute environment that ends up running about 100 CU-hours in the month on Neon (a back-of-envelope figure, since real compute depends entirely on your autoscaling settings and traffic shape).

DynamoDB (on-demand, US East N. Virginia)

Writes: 2,000,000 x $0.625 per million = $1.25.
Reads: 8,000,000 x $0.125 per million = $1.00.
Storage: 5 GB is fully inside the 25 GB always-free allowance, so $0.00.
Estimated total: about $2.25/month, assuming each operation maps to roughly one request unit. Real bills climb if items are large, if you add global secondary indexes (each GSI is metered separately for writes and storage), or if you enable streams and global tables.

Neon (Launch plan)

Storage: 5 GB x $0.35 per GB-month = $1.75.
Compute: 100 CU-hours x $0.106 per CU-hour = $10.60.
Estimated total: about $12.35/month, dominated by compute. If your app scales to zero between bursts of traffic, real compute-hours and therefore the bill drop sharply, which is the entire point of Neon's autoscaling architecture.

The honest read: at this scale DynamoDB is the cheaper line item on paper, mostly because storage sits under the free tier and request volume is modest. Neon's cost is front-loaded by compute hours, not storage. But the dollar gap here is a few coffees per month, and it does not price in the engineering hours DynamoDB's data modeling demands or the GSI and streams charges that surprise people once access patterns multiply. For a solo developer, ten dollars of predictable compute often beats two dollars plus a rigid schema you cannot easily change your mind about. Plug your own numbers into the per-unit rates in the Sources below before committing either way.

When to Choose Neon

You want a relational database that's flexible and easy to query
You're building with any standard web framework
You value the ability to iterate quickly on data models
You want to avoid AWS vendor lock-in
You need features like JSONB, full-text search, or complex joins

When to Choose DynamoDB

You're building exclusively on AWS with Lambda and API Gateway
Your access patterns are simple, well-known, and unlikely to change
You need guaranteed single-digit millisecond reads at massive scale
Your data is naturally key-value (sessions, user profiles, event streams)
You're already experienced with DynamoDB data modeling

The Verdict

Neon wins this comparison decisively for solo developers. The 9/10 vs 5/10 ratings reflect real experience. Neon gives you the full power of PostgreSQL with serverless convenience, and you can query your data however you need to. DynamoDB gives you raw performance at the cost of rigid data modeling that slows down feature development.

Unless you're building a serverless AWS application where DynamoDB's integration advantages are critical, go with Neon. You'll spend less time fighting your database and more time building features. And when your project grows, Postgres scales further than most solo developers will ever need.