Use the right variant
General, reasoning, and distilled variants have different quality, latency, memory, and hardware implications.
Self-hosted models / DeepSeek
DeepSeek can be a strong local option, if the workload fits the model.
DeepSeek's open model family includes large reasoning and general models alongside smaller distilled variants. We help test the right variant on the client's questions, choose an inference path, and configure a private workflow without pretending every DeepSeek model is easy to run or right for every job.
Model version mattersLocal inferenceWorkload testNo fixed API pricing
Pattern demonstration / model version varies
Why DeepSeek enters the conversation
Value comes from the combination, not the headline.
An open model can reduce dependence on a hosted API and may lower recurring provider spend for suitable workloads, but hardware and operating cost still count.
Trade API spend for operation
Local inference may reduce per-request provider cost, while adding hardware, power, deployment, maintenance, and evaluation work.
Keep the route private
A local workflow can keep model requests and retrieval inside the chosen environment when external paths are disabled or internalized.
What the official model material makes clear
DeepSeek is a family, not one laptop download.
Large and distilled paths differ
The full models can require serious multi-GPU infrastructure. Smaller distilled models may be more practical for a first local workflow.
Benchmark is not your answer
Test documents, languages, context, citations, speed, and failure behavior on the questions the client actually asks.
Check the current license
The model and code licenses, versions, runtimes, and supported deployment methods should be checked at selection time.
A self-hosted DeepSeek review
Choose the model after the questions.
Build the test set
Collect representative questions, documents, languages, output formats, and unacceptable failure cases.
Compare variants
Test quality, latency, context, concurrency, memory, quantization, hardware, and operator complexity.
Define the offline boundary
Decide what can run without internet and what still needs network access for updates, connectors, telemetry, or sources.
Deploy the approved path
Configure the runtime, retrieval, access, monitoring, backups, and model update process in the client's environment.
DeepSeek is interesting because it expands the local model choice. The client still buys a tested workflow, not a model name.
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 DeepSeek always cheaper?
No fixed claim is safe. Local inference can reduce provider/API spend for a suitable workload, but hardware, power, engineering, support, and quality testing determine total cost.
Can DeepSeek run without internet?
A local deployment can be offline-capable after models and runtimes are installed. Updates, packages, external sources, telemetry, and connectors must be deliberately disabled or kept internal.
Which DeepSeek model should we use?
That depends on the task and hardware. We test the current general or distilled options on the client's questions instead of naming a permanent winner.
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 questions
What should a self-hosted DeepSeek setup do?
Tell us the workload, users, offline requirement, hardware, documents, and output. We will define the test before the model choice.