Public vs. Private LLMs: Another One Rides the Bus

Weird Al popped into my head at lunch this week (not exactly unusual if we're being honest) while I was talking with a friend about the pros and cons of public vs. private LLMs. We work in wildly different industries, with vastly different AI experience — he's just starting to explore LLMs, and I've been building GPT-powered pipelines since the GPT-2 days — yet our perspectives on the tech, the people, and the problems lined up almost perfectly.

Somewhere in our conversation about data sanitization and between bites (Just Eat It!), the line from Another One Rides The Bus popped into my head:

Ridin' in the bus down the boulevard, and the place was pretty packed… It was smellin' like a locker room // There was junk all over the floor…

It fit our conversation perfectly. What happens when you don't want your proprietary dataset helping your competitors — or worse, seeing traces of their smelly data (my friend's words) in your results?

And then there's reliability. The bus can break down — sometimes for minutes, sometimes for hours — and you're stuck on the curb waiting for service to resume.

Or maybe the bus driver (read: OpenAI, Anthropic, Google, etc.) decides overnight to change the route entirely. One day you're happily riding GPT-4o, the next you're handed GPT-5 — a completely different personality — without so much as a stop-announcement.

That's not hypothetical; it happened this past week with the replacement of GPT-4o with 5, and the uproar from developers and end-users alike was proof that we've all gotten a bit too comfortable trusting someone else's transit map.

To quote a comment (https://www.linkedin.com/in/jimdiroffii/) I read on LI:

"Imagine if when Python 3 was released, the public wasn't allowed to test it, and all access to Python 2 was simultaneously restricted. It would have been calamitous."

When you're dependent on someone else's fleet, you inherit their scheduling, their maintenance priorities, and their budget decisions — even if those decisions wreck your carefully tuned workflows. In other words: you might be paying for a ticket, but you're not steering the wheel.

Weird Al might have been singing about public transportation, but the analogy works frighteningly well for public Large Language Models (LLMs) like GPT, Gemini, Perplexity, Grok, or Claude: easy to hop on, zero maintenance (for us), and they usually get you where you need to go. Usually. But when another one rides the bus — or a few million others do — the limitations of a shared ride become very clear.

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Private LLMs: Owning the Car

If public LLMs are the bus, then private LLMs are your own set of wheels. You choose the route, pick the passengers, and set the speed — no surprise detours (unless something doesn't build, a dependency doesn’t work), no mystery riders peeking into your data, and definitely no smelly data stinking up the back seat.

But car ownership isn't free. Running your own model — whether on-prem, in a private cloud, or on rented GPUs — means you're paying for the car and the garage. The garage is the capital expenditure (or OpEx if you're renting) of the infrastructure: racks of servers, high-end GPUs that can run into the tens of thousands each (or \$2–\$5/hour per A100/H100 on AWS if you're renting source), the network backbone, and the cooling and power to keep it all alive.

Then there's the gas: training your models isn't just costly in dollars — it burns time, patience, and talent. Fine-tuning or pretraining requires huge datasets, long-running compute jobs, and often, iteration after iteration before you get something production-worthy.

Owning the car also means you're both the driver and the mechanic. You handle the monitoring, patching, scaling, and security hardening. You plan the oil changes (retraining schedules) and replace the tires (deprecated libraries and outdated dependencies). You keep a constant eye on performance, guard against drift, and respond fast if something breaks down at 2 a.m.

And don't overlook the opportunity cost. Every hour our engineers spend babysitting infrastructure and wrangling GPUs is an hour they're not building new customer-facing features — the stuff that actually differentiates your brand and makes money.

If public LLMs sometimes feel like a noisy city bus at rush hour, private models are the quiet, climate-controlled road trip where you control the playlist. But that playlist comes with a service manual — and you're the one holding the wrench.

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Choosing Your Ride

Whether you're boarding the bus or grabbing the keys, the choice between public and private LLMs boils down to trade-offs. Public models are fast, cheap to board, and require zero maintenance — but you share the ride, the baggage, and the consequences when the driver changes the route without warning. Private models give you control over every turn of the wheel, every passenger, and every mile per hour — but you pay for the privilege in cost, complexity, and upkeep.

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Public vs. Private LLMs at a Glance

Public LLMs – The Bus
[✓] Fast boarding — API key and go
[✓] Maintenance-free — someone else handles scaling and patches
[✓] Great for quick, low-sensitivity projects
[!] Noisy neighbors can cause slowdowns
[!] Shared baggage — your data may mix into the communal pool
[!] Privacy leaks possible
[!] Routes change without notice (model swaps, price hikes)

Private LLMs – The Car
[✓] Full control over architecture, data, and updates
[✓] No smelly data from competitors
[✓] Fine-tuned for your exact needs
[✓] Predictable privacy boundaries
[!] You’re the driver and the mechanic
[!] Gas isn’t cheap — GPUs, training costs, MLOps overhead
[!] Longer on-ramp to production
[!] Feature velocity can slow if you’re stuck under the hood

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In the end, it's about priorities:

• Need speed, flexibility, and low commitment? Hop on the bus and enjoy the ride.
• Need control, privacy, and consistency? Grab the keys and drive yourself.

Just remember: in both cases, you're not immune to road hazards. Public buses can get rerouted without notice, and private cars can break down if you skip maintenance.

You could be sharing a spot that's "smellin' like a locker room [with] junk all over the floor" or take more time than expected training your model.

The trick is knowing which trade-offs you can live with — and which ones will leave you stranded by the side of the road, humming Weird Al while you wait for a tow.