What Does a Computer Vision Project Cost to Build?
This page covers the build phase only. What the system costs once it is live is a separate budget with a different shape, and it is set out in what it costs to run an AI system after you build it rather than repeated here.
It also does not contain a total project cost. Two of the four items below have published vendor rates you can apply to your own numbers. The other two depend on what you are looking at, where it is, and who owns the place it happens. A total would have to invent them, and the invented half would be the larger one.
The Four Build Costs, in the Order They Arrive
They arrive roughly in sequence, and each one is harder to change once the next has started.
Getting the images. Cameras, access, permissions, storage, and time spent waiting for the thing you care about to happen.
Labelling them. Someone marks what is in each image so a model can learn the difference. This is the item with real published pricing.
Training compute. GPU hours, priced by the hour and multiplied by however many attempts it takes.
Where it runs. A cloud instance you rent, or hardware you buy and install somewhere physical.
Most budgets contain the middle two and omit the outer two. The outer two are usually larger.
Collecting the Images: The Cost Nobody Quotes
Annotation appears in every budget because vendors sell it and publish rates. Image collection appears in almost none, because nobody sells it to you.
The work is real regardless. If the images already exist, in a camera system that has been recording for two years, the cost is retrieval, storage, and the permission conversation about using them. If they do not exist, someone has to install cameras, position them so the thing of interest is actually visible, and then wait.
Waiting is the part that surprises people. A model that detects a defect needs examples of the defect. If the defect happens twice a month, collecting a few hundred examples is a schedule problem before it is a budget problem, and no amount of money shortens it. Teams discover this after the annotation quote has been approved and the project plan has been written.
Three things make this cost swing hardest: whether the images exist already, whether the event you care about is common or rare, and whether you control the place the camera has to go. A camera on your own production line is a purchase order. A camera in a customer's warehouse is a negotiation.
Whether the data you have is usable at all is the prior question, and it is worked through in is your data ready for an AI engineer.
Annotation: The One Cost With Published Rates
This is the only item in the four where you can look up a price and multiply.
Label Your Data publishes $0.02 per object for bounding box annotation, $0.015 per object for keypoint annotation, and $6 per annotator hour for flexible workflows and QA assistance (Label Your Data pricing). Those are per object rather than per image, which matters: an image of a busy shelf may contain forty objects.
Three qualifications before you multiply.
Most vendors do not publish. The majority of annotation companies quote on request, so a published rate is a floor from one vendor rather than a market price.
Task type changes the number substantially. A bounding box is a rectangle. Semantic segmentation traces the outline of the object pixel by pixel and takes far longer per object. Specialist domains where the labeller must be qualified, medical imaging being the clearest case, are a different market entirely.
Quality passes multiply the count. Labelling each image once is cheapest and least reliable. Having a second person review, or labelling a subset twice to measure agreement, is how you find out whether your labels are consistent. Budget the review, because inconsistent labels produce a model that is confidently wrong and an investigation that costs more than the review would have.
To get your own figure: estimate objects per image, multiply by images, multiply by the published rate, then add the review pass. That number is derived from your project rather than from an article.
Training Compute: Priced by the Hour, Sized by Attempts
GPU time has clear published pricing. Lambda publishes on-demand rates including $1.29 per GPU-hour for an A10, $1.99 per GPU-hour for an A100, and $3.29 per GPU-hour for an H100 PCIe (Lambda GPU cloud pricing). Rates from the major cloud providers are published too and vary by region and commitment.
The rate is not the variable that decides your bill. The number of training runs is.
Nobody trains the right model on the first attempt. The realistic sequence is a run that reveals a labelling problem, a run that reveals the images do not cover a case that matters, a run that works but is too slow for the hardware it has to sit on, and then several more tuning it. Each is a multiple of the hourly rate by however many hours the run takes.
This is why the honest way to budget compute is a range with an explicit assumption about attempts, rather than a single figure. It is also why an engineer who has trained models in production is worth more than one who has followed tutorials: the difference shows up as fewer wasted runs, and wasted runs are the whole cost.
Two things reduce it. Starting from a pretrained model rather than from scratch is standard practice and cuts training time substantially. Testing on a small subset before committing to a full run catches the labelling and coverage problems above at a fraction of the price.
Where It Runs: Cloud or Edge
The last build decision is where the trained model executes, and it changes the shape of the cost rather than just its size.
Cloud keeps everything recurring. You rent capacity, it scales with usage, and there is nothing to install. The cost that catches people is bandwidth: shipping continuous high-resolution video to a data centre is expensive, and it is charged on volume that only goes up.
Edge means hardware physically where the camera is. It converts a recurring bill into an upfront purchase, removes the bandwidth problem because the video never leaves the building, and adds work that rarely appears in the budget: mounting it, powering it, networking it, and going out to fix it when it fails.
We publish no figure for edge hardware, because the vendors do not publish one on a page we can cite. Prices are quoted through partners and resellers and move with the product line. What we can say is what drives it: how much processing the model needs, how many camera locations you have, and whether those locations already have power and network. The count of sites is usually the multiplier that matters, because everything else is per site.
The bandwidth question generally decides it. A handful of cameras sending occasional stills is a cloud problem. Twenty cameras streaming continuously is an edge problem, and the arithmetic is usually not close.
| Build cost | Published rate available? | What actually drives it |
|---|---|---|
| Collecting images | No | Whether images already exist, how rare the event is, and who controls the site |
| Annotation | Yes, from some vendors | Objects per image, task type, and how many quality passes you fund |
| Training compute | Yes | Number of training attempts, not the hourly rate |
| Deployment hardware | No published page we can cite | Processing required, number of sites, and whether power and network already exist |
| The engineer | Proxy only | No BLS occupation covers computer vision; Software Developers is the closest published match |
| Who should NOT use F5 Hiring Solutions | Companies needing a W-2 US employee, on-site presence, fractional or part-time work, an engagement under six months, or a self-serve platform for browsing profiles. F5 Hiring Solutions places full-time professionals from India and the Philippines through a concierge process |
The Cost of the Person
Through the build, this is one person's full attention rather than a task on the side. The sequence above, collect, label, train, evaluate, retrain, deploy, only converges when somebody holds the whole thread.
BLS publishes no occupation for computer vision engineering. The closest published proxy is Software Developers, SOC 15-1252, at a median annual wage of $135,980 (BLS OEWS, May 2025), loading to roughly $193,975 at the 1.4265 multiplier from BLS Employer Costs for Employee Compensation (ECEC, Dec 2025). It is a proxy in the strict sense: the occupation is broader than the role and may overstate or understate it. Loaded figures are estimates from a national benefits average rather than a measured cost at your company.
F5 Hiring Solutions is a managed remote workforce company placing full-time professionals at $375-$1,200 per week, all-inclusive, with AI roles from $600 per week. All-inclusive covers salary, HR administration, payroll, equipment, compliance, and management, with no setup fee, recruiting fee, or termination cost. Shortlists arrive in 7-14 business days from a network of 85,500+ pre-vetted professionals, which is F5's own published commitment rather than an independently measured benchmark. F5 Hiring Solutions has served 250+ US companies with a 95% client retention rate, measured as clients continuing beyond the first three months.
The side-by-side comparison of what that engineer costs in each market is in what a computer vision engineer costs, India versus the USA. Whether the hire is justified before any of it applies is argued in whether an AI engineer is worth the cost at all.
The Bottom Line
Budget four items, and expect the two with published prices to be the smaller half.
Annotation and GPU hours you can look up and multiply, and this page has given you the published rates to do it with. Image collection and deployment hardware you have to work out from your own situation, because no vendor publishes a price for access to a warehouse or for waiting three months to catch a rare defect on camera.
The single largest lever is not any of the four. It is how many training attempts the project takes, and that is set by whether the person doing it has done it before.
To put a named owner on the build, hire remote computer vision engineers from India.
Schedule a 15-minute call: https://calendly.com/joel-f5hiringsolutions/f5