I’ve been tempted to buy one and do “real dev work” on it just to show people it’s not this handicapped little machine.

I built multiple iOS apps and went through two start up acquisitions with my M1 MBA as my primary computer, as a developer. And the neo is better than the M1 MBA. I edited my 30-45 min long 4k race videos in FCP on that air just fine.

This is as much an indictment of AWS compute as it is anything else.

When I teach, I use "big data" for data that won't fit in a single machine. "Small data" fits on a single machine in memory and medium data on disk.

Having said that duckDB is awesome. I recently ported a 20 year old Python app to modern Python. I made the backend swappable, polars or duckdb. Got a 40-80x speed improvement. Took 2 days.

as a broke ecologist, this little computer can do everything I need in R and word and is a phenomenal build for the price. I'm really enjoying it thus far.

I adore DuckDB.

Did a PoC on a AWS Lambda for data that was GZ'ed in a s3 bucket.

It was able to replace about 400 C# LoC with about 10 lines.

Amazing little bit of kit.

This is awesome.

I wish more companies would do showcases like this of what kind of load you can expect from commodity-ish hardware.

> The cloud instances have network-attached disks

Props for identifying the issue immediately, but armed with that knowledge, why not redo the benchmark on a different instance type that has local storage? E.g. why not try a `c8id.2xlarge` or `c8id.4xlarge` (which bracket the `c6a.4xlarge`'s cost)?

I would have benchmarked with an instance that has local nvme, like c8gd.4xlarge.

I think it’s relevant to first read [1] to see why they’re doing this. It’s basically done as a meme.

[1] https://motherduck.com/blog/big-data-is-dead/

That's not Big Data. If you "need to process Big Data on the move" - what you need is a network.

The DuckDB team benchmarked with an r7i.16xlarge which uses EBS - that's the expected bottleneck. A fairer comparison would be an i4i or c8gd with local NVMe, where you'd likely see the laptop and cloud instance much closer in practice.

Funny just yesterday I almost bought one but got cold feet and opted for a low range MacBook with M5 chip. The Apple sales rep was not convinced it would be enough when i described using it for vibecoding and deploying so kind of talked me out of getting the Neo. I normally use a mix of LLMs, then connect to Github and do a one-click deploy on CreateOS. Do you think I over-reacted? The price of the Neo is SO attractive, a clean half price compared to what I got.

Would it not also work on a raspberry.

With I/O streaming and efficient transformation I do big data on my consumer PC and good old cheap HDDs just fine.

For the TPC-DS results it would also have been nice to show how the macbook neo compares to the AWS instances.

Or am I missing something?

> compared to 3–5 GB/s

Their numbers are a bit outdated. M5 Macbook pro SSDs are literally 5x this speed. It's wild.

> TL;DR: How does the latest entry-level MacBook perform on database workloads? We benchmarked it to find out.

That's not tldr, that's just subheader.

That c8g.metal-48xl instance costs $7.63008 on demand[1], so for the price of the laptop, you could run queries on it for about ~90 hours.

:shrug: as to whether that makes the laptop or the giant instance the better place to do one's work…

[1] https://aws.amazon.com/ec2/pricing/on-demand/

If you can fit it on a thumb drive, it's not Big Data.

“Big data” doesn’t have a 5gb memory cap.

I’m guessing so many devs started out on 32gb MacBooks that the NEO seems underpowered. but it wasn’t too long ago that 8gb, 1500mb/sec IO & so many cores was an elite machine.

I did a lot of dev work on a glorified eePC Chromebook when my laptop was damaged. You don’t need a lot of ram to run a terminal.

I’m hoping NEO resets the baseline testing environment so developers get back to shipping software that doesn’t monopolize resources. “Plays nice with others” should be part of the software developer’s creed.

I'm interested by one (not for big data) but only 8 GB or RAM is kinda really sad.

My good old LG Gram (from 2017? 2015? don't even remember) already had 24 GB of RAM. That was 10 years ago.

A decade later I cannot see myself being a laptop with 1/3rd the mem.

Queue the endless blog posts about running tech on the potato macbook and being stunned it’s functional with massive trade-offs. Groundbreaking stuff.

this has a phone CPU/memory

other test:

2025-09-08 : "Big Data on the Move: DuckDB on the Framework Laptop 13"

"TL;DR: We put DuckDB through its paces on a 12-core ultrabook with 128 GB RAM, running TPC-H queries up to SF10,000."

https://duckdb.org/2025/09/08/duckdb-on-the-framework-laptop...

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You could get a laptop with an Nvidia GPU, 16gb ram, 512 ssd... or a 'cheap' Macbook.

I totally understand if you need to compile for iphones. We need to make apps for the lower and middle class people that think a $40/mo cellphone is a status symbol. I get it.

But if you are not... why? I hate windows, but we have Fedora... and you get an Nvidia. Is it just a status symbol? And I have a hard time believing people who tell me stories about low power consumption, because no one had cared about that until Apple pretended people cared about it.

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Mind blown, if you need to handle "big" data on the move - the macbook neo is not the right choice. - Who would have guessed that outcome?

That's an awesome idea to get a bricked MacBook Neo really fast because those idiots soldered the SSD inside

Seems completely unnecessary, there is probably 0 overlap between people who buy a cheap MacBook and people running DuckDB locally

Oh great, the term "big data" is back.

>Can I expect good performance from the MacBook Neo with Slack, Microsoft Office, and Google Chrome signed into Atlassian and a CRM, all running simultaneously?

No.

>Do I reject a world where all of the above is necessary to realize value from an entry-level MacBook?

In theory, yes.