Cloud inference is the cheapest way to start and one of the more expensive ways to run at steady, high volume. On-premise or edge inference tends to win when volume is high and predictable, latency must be tight, or data can't leave your network. The decision is rarely about the model — it's about your volume curve, your latency budget, and your data.
Almost every AI project starts on a cloud API, and it should. What changes the calculus is success: once inference runs continuously at volume, the per-call pricing that made the cloud easy to adopt becomes the line item nobody budgeted for.
This is not an argument against the cloud. It's an argument for deciding deliberately, because the honest answer depends on numbers specific to your workload — and moving after you've built everything around one assumption is expensive.
Why the cloud bill grows with success
Cloud inference is priced per unit of work — per token, per image, per second of video, per API call. That model is a gift when you're validating an idea: you pay almost nothing to run a pilot, and you never buy hardware you might not need. But per-unit pricing scales linearly with usage, and it never stops. A workload that processes a steady, heavy stream — every frame from a bank of cameras, every transaction through a scoring model — pays that per-unit price on every single inference, forever.
Owned hardware inverts the shape of the cost. You pay a larger amount up front (or amortised as a lease), and then the marginal cost of one more inference is close to zero until you saturate the box. For a spiky, low-volume workload that shape is bad — you've bought capacity that sits idle. For a high, steady workload it's good — the fixed cost is spread across enormous volume and the per-inference cost falls well below the cloud's.
The crossover point is real but workload-specific. There's no universal threshold we'd quote you, because it moves with your volume, the model size, the hardware, and the cloud rates on the day. The useful discipline is to model your own crossover before you commit an architecture to one side of it.
The three costs people forget
Sticker prices for cloud inference undersell the total in three ways that matter most for vision and real-time workloads.
1. Data movement
Sending data to the model has its own cost and its own latency. Streaming video to a cloud endpoint means paying for bandwidth continuously and depending on a link that has to stay up. For a workload where the data is generated on-site — cameras on a floor, sensors in a plant — the cheapest and most reliable place to run the model is often next to where the data already is.
2. Latency as a requirement, not a nicety
If a detection has to trigger an action in under a second — stop a line, flag an intrusion — a round trip to a distant data centre may simply miss the deadline, regardless of cost. Latency can move the decision on its own, before economics enter the picture.
3. Data residency and control
Some data can't leave the building for legal, contractual, or trust reasons. In regulated and IP-sensitive settings, "we don't upload your footage" isn't a cost line — it's a requirement that rules the cloud out for that workload entirely. We see this constantly in on-prem computer vision on factory floors, where footage never leaving the site is the whole point.
When on-prem or edge inference wins
- High, steady volume. Continuous streams that keep hardware busy are where owned capacity amortises well.
- Tight latency. Real-time actions that can't wait for a network round trip.
- Data that can't move. Residency, privacy, or IP constraints that make uploading a non-starter.
- Hardware you already have. If there's capacity on-site, running inference on it can be close to free at the margin.
When the cloud is still the right call
- Spiky or low volume. Bursty, unpredictable, or light workloads that would leave owned hardware idle.
- No operations capacity. On-prem means someone owns uptime, drivers, and hardware failure. If you don't have or want that, the cloud rents it.
- Frontier models you don't run yourself. The largest hosted models exist behind APIs for a reason; some capability is genuinely easier to rent.
- Early validation. Before you know your volume curve, the cloud is the cheapest way to find it.
A framework you can actually use
Before choosing a side, get honest answers to three questions, in order:
- Can the data leave? If not, the decision is made — build for on-prem and design around it. Don't fight it later.
- What's the latency budget? If actions must fire faster than a network round trip allows, edge inference is a requirement, not a preference.
- What does the volume curve look like? Only now does cost dominate. Model your own crossover between per-unit cloud pricing and amortised owned hardware, using your real numbers — not a blog's.
A common and sensible outcome is a hybrid: run the steady, sensitive, latency-critical workload on-prem, and burst to the cloud for the spiky or experimental parts. The mistake isn't picking one — it's picking by default and discovering the bill, or the latency, or the compliance gap, after everything's built.
We build inference that runs where it makes sense — on edge hardware inside a client's network for computer vision, in the cloud where the workload suits it, and hybrids of the two. If you're weighing this decision for a specific workload, the fastest way to a real answer is to model it against your actual volume and constraints rather than a rule of thumb.