Less to operate
Use a client-owned or approved managed environment with documented provider, storage, access, and support paths.
Comparison / who operates the AI
On-premise versus managed AI is an operations decision, not a privacy slogan.
We compare on-premise and managed AI by data path, operations, support, cost, latency, and ownership. Both can be private in a useful sense; the difference is who runs the system and what the team can sustain.
Data pathOperationsSupportMeasured workload
Pattern demonstration / same workflow, synthetic material
Fixed-scope setupClient-owned accountsDraft-and-review firstFull handover
What changes between the two
The model is only part of the system.
Managed AI reduces infrastructure work. On-premise increases physical control but adds hardware, updates, and failure response.
More physical control
Run the workload on local hardware when the network or inference boundary justifies the operations.
Let the work decide
Compare quality, latency, concurrency, cost, backup, updates, and support against a fixed test set.
Do not compare labels
Compare the operating consequences.
Who can access what?
Map user roles, administrator access, provider support, logs, backups, and incident response.
What keeps running?
Include hardware, power, storage, model usage, engineering time, support, upgrades, and downtime.
What does the user need?
Latency and concurrency can make an option useful or unusable. Measure the real interaction.
A fair comparison
Put both options through the same test.
Define the requirement
Name the data, network, region, procurement, latency, and support constraints that triggered the comparison.
Run the same workload
Use the same corpus, users, questions, output format, and acceptance criteria on each route.
Cost the operations
Include setup, provider bills, hardware, power, updates, monitoring, backup, and incident response.
Choose the sustainable fit
Select the route that meets the requirement with an operating model the team can actually maintain.
The best route is the least complicated one that satisfies the real boundary and survives the week after launch.
Why trust Pristine3D?
We build and operate production software.
Pristine3D Ltd builds and operates live digital products, and we run private AI workflows internally as part of our own operations. We scope around your real workflow: the documents you own, the questions your team asks, and the access boundary you approve. Based in Lagos, Nigeria, we work remotely with clients worldwide.
We already run live products
Based in Lagos, Nigeria, Pristine3D currently builds and operates smartcards.ng, venu.ng, photoshoot.ng, and ugc.ng in production.
We start with the actual workflow
One input, one output, one test set, and one person who owns the result. We scope a real workflow instead of a transformation programme.
The boundary stays visible
Cloud, model, storage, and messaging accounts stay in your name. The chosen data path, access rules, test record, documentation, and training are part of the agreed scope.
Pricing / fixed scope
Know the starting numbers before you ask.
The final quote follows the workflow. Infrastructure and model bills stay on your accounts.
One Workflow
Starting from$3,500 / ₦2.5m
2 to 3 weeksOne workflow for a small team, with training and handover.
See the one-workflow packageFirm-wide
Starting from$7,500 / ₦8m
4 to 6 weeksMultiple sources, roles, integrations, and admin handover.
See the deploymentAnnual support
Starting from$3,000 / ₦1.5m
per yearStandard care for one delivered workflow. Optional. Larger deployments and active monitoring are separately scoped.
See supportArchitecture review from $500. Standard annual support is $3,000 / ₦1.5m per year for one delivered workflow. New workflows, integrations, active monitoring, and infrastructure are separately scoped; infrastructure, model, storage, and messaging bills stay on client accounts. Full pricing and what changes the quote
Straight answers
Common questions.
Is managed AI less private?
Not automatically. Privacy depends on the client's accounts, provider terms, region, access, logs, and the people who operate the system.
Is on-premise cheaper?
Not automatically. Compare hardware, power, support, updates, storage, model performance, and staff time with the full managed option.
How is this different from on-premise versus regional cloud?
That page compares locations. This page compares operating models: who runs the system and what the team must support.
Own your knowledge base
The model is not the product. The knowledge base is.
Documents, the retrieval index, access rules, and the workflows built around them are the asset, and they compound. We deploy so the knowledge base stays yours: on your accounts, in the environment you choose, under access rules your team defines. The model behind the answers is a connector, so the knowledge base moves with you, not with a vendor.
Your corpus, your index
The document store, metadata, and retrieval setup live on accounts you own. No vendor holds the corpus.
Models are swappable parts
Change the model provider, move regions, or go local without rebuilding the knowledge base or the workflow.
The knowledge base is the alpha
Every improvement to the corpus improves the answers, and the improvement stays with you, not with a vendor.
Keep exploring
Related setups.
Start with the constraint
What makes managed or on-premise the right operating model?
Tell us the data path, users, workload, latency, and support capacity. We will compare both routes against the real requirement.