Quality at the required task
Use representative documents and exceptions to compare models. Count the cases that need human correction or cannot be completed.
Choose where AI runs. Know what it takes.
Data location is one part of the decision. Model quality, access, support, updates and recovery matter too. Welf helps you assess a deployment boundary and build the workflow that can operate inside it.
Deployment decision
What needs to stay inside your environment?
Choose where the workflow runs based on the data it uses and the task it performs. Assess access controls, monitoring and compliance requirements alongside hosting.
On smaller screens, scroll sideways to compare all columns.
| Model | Why consider it | Operational trade-off |
|---|---|---|
| Managed model endpoint | Access to a provider-operated model where the data terms and processing locations fit the task. | Provider dependency, retention settings, network access and change management need review. |
| Dedicated private cloud | Workloads and data services run in a customer-controlled cloud environment. | Your team needs an operating model for identity, networking, capacity, patching and support. |
| On-premises or isolated | The intended use requires local infrastructure or tightly restricted connectivity. | Model availability, hardware capacity, updates and recovery become explicit engineering responsibilities. |
The complete data path
Control extends beyond the prompt.
Map source documents, embeddings, caches, logs, backups and support access. Decide retention and deletion across the whole workflow.
Cost and service quality
Price the accepted result.
A low inference price can hide expensive review or infrastructure. Evaluate the workload at realistic volumes and concurrency.
Capacity under load
Test document size, peak demand and response time. Account for idle capacity, queues and the cost of making the service available when work arrives.
An owner for the service
Assign monitoring, patching, model changes, incident response and recovery tests. Include these responsibilities in the operating budget.
Capacity with perspective
Match the model to the workload.
Extraction, retrieval and difficult reasoning do not necessarily need the same model. Test smaller models where they meet the task requirements and reserve more expensive routes for cases that justify them.
Discuss the architectureQuestions before we start
Before choosing private infrastructure.
Only if the complete architecture is designed that way. Document extraction, embeddings, inference, telemetry, support tools and backups can each create an external data path. We map these before choosing services.
Where their license, quality and operating requirements fit. Evaluate them on the actual workflow and include the cost of hosting, updating and supporting the model.
We can design interfaces and retain evaluation cases to make a change more manageable. A provider change still requires testing of quality, permissions, cost and system behavior.
Define the boundary before buying capacity.
Bring the intended workflow, data constraints, expected demand and existing infrastructure. We can compare the deployment options against the work.
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