Pricing Details
Price Calculator
Units?
1 unit = 8 vCPU, 8 GB RAM, 50 GB storage
VRAM is billed fractionally each 8 GB = 1 unit
1 unit = €3 / day
1
Cost per desktop / day
€3
Total cost?Cost per desktop * number of desktops.
€3
Simple, Transparent Pricing
Four ways to pay for DaDesktop
Most popular
Public Cloud
Pay only for the days a desktop actually runs. No commitment, no idle costs.
From €3 / desktop / day
Weekend Price: €0.25
- 8 GB RAM, 8-core CPU, 50 GB SSD
- +€3 per extra 8 GB / 8 cores / 50 GB
- +€1 a day for Windows, or free with your own licence
- Billed per calendar day, not per hour
Private Server
A whole server to yourself, so your cost stays fixed no matter how many desktops you run.
From €10 / day
- Unlimited desktops within the server's capacity
- No per-unit or per-desktop counting
- Predictable monthly spend
- Sizing agreed with our team
Reseller & Volume
Discounts for partners integrating, reselling or buying DaDesktop at scale.
Up to €0.10 / desktop / day
- Integrate DaDesktop into your own product
- Resell under your commercial terms
- High-volume purchasing
- Discounts for larger volumes
On-Premise
Run DaDesktop inside your own data centre, on your own hardware and network.
From €6 / core
- Deployed on your infrastructure
- Data never leaves your environment
- Priced per CPU core
- Suits regulated and air-gapped setups
Not sure which option fits? Contact Us
Course Billing
For Public Cloud and Reseller engagements, course fees are invoiced to the Organization Owner, calculated based on the number of users who provisioned desktops during the course period.
Trainer Responsibilities
- When a Trainer is invited to a course, the billing responsibility lies with the Organization Owner.
- If a Trainer initiates and creates the course, they are directly responsible for the costs.
- Consequently, the Organization Owner associated with the created course is always the party billed for the training services.
Start Your Free Trial Today
Run seamless virtual IT training with cloud-based labs, no downtime, just scalable learning that works.