Why CaseDesk

The honest comparison against the alternatives.

Every team evaluating CaseDesk is already using or considering one of these. Here is how they compare for teams that need data residency and privacy.

CaseDesk vs OpenAI API

Use OpenAI if data residency does not matter

OpenAI API

  • All prompts processed on US servers regardless of your location.
  • No UK or EU data residency option on standard plans.
  • Not suitable for NHS DSPT or regulated healthcare workflows.
  • Pricing changes without notice - your costs can double overnight.
  • Model behaviour can change with silent updates mid-deployment.
  • Your data may be used to improve OpenAI's models unless you opt out via enterprise terms.

CaseDesk

  • Inference runs in the UK or EU region you select. No data leaves that region.
  • Hardware-level isolation available for NHS and regulated workloads.
  • Pricing is fixed GPU-hour rate - no surprise increases mid-contract.
  • You control the model version. Updates happen on your schedule.
  • We never see or log your prompt content.
  • OpenAI-compatible API - existing integrations work without code changes.
When OpenAI is the right choice: if your use case involves no sensitive data, no compliance constraints, and you need access to GPT-4-class capability today, OpenAI API remains the fastest path. CaseDesk solves a different problem.

CaseDesk vs Azure OpenAI / AWS Bedrock

Use hyperscalers if you have existing cloud commitment spend

Azure OpenAI / AWS Bedrock

  • Requires an existing Azure or AWS account with appropriate IAM setup.
  • Model selection is limited to what the provider chooses to offer.
  • Provisioning a deployment takes hours and requires cloud expertise.
  • Costs are complex - compute, storage, egress, and API calls all billed separately.
  • Open-source model support is limited or unavailable.
  • Data residency depends on your subscription tier and region configuration - easy to misconfigure.

CaseDesk

  • No cloud account required. We handle the infrastructure.
  • Deploy any open-source model - Llama3, Mistral, DeepSeek, Qwen, CodeLlama, and more.
  • Ready in minutes, not hours. No IAM policies, no resource groups.
  • One line item: GPU-hours. No hidden fees.
  • Data residency enforced at the networking level, not by configuration.
  • No vendor tie-in to a single hyperscaler ecosystem.
When Azure OpenAI is the right choice: if you have significant existing Azure spend and need GPT-4 specifically, Azure OpenAI makes sense within a Microsoft-centric stack. For open-source model inference with simpler pricing and no cloud prerequisites, CaseDesk is faster to deploy.

CaseDesk vs Self-hosting

Self-host only if you have dedicated ML infrastructure engineers

Self-hosting vLLM / Ollama

  • Requires GPU hardware procurement or cloud GPU instance setup.
  • Kubernetes or Docker expertise needed to serve models reliably.
  • You are responsible for uptime, scaling, and incident response.
  • Model updates, driver patches, and CUDA maintenance fall on your team.
  • Total cost of engineering time often exceeds managed service cost.
  • Takes days or weeks to reach production, not minutes.

CaseDesk

  • No hardware. No Kubernetes. No driver patches.
  • Deployment in minutes via a simple dashboard - no CLI required.
  • CaseDesk handles uptime, scaling, and model updates.
  • Same privacy guarantee as self-hosting - your data stays in your region.
  • Exit strategy included: the OpenAI-compatible endpoint works on your own vLLM cluster if you ever want to migrate.
  • Scale to zero when idle - no idle GPU cost for workloads that run part-time.
When self-hosting is the right choice: if you have a dedicated ML platform team, GPU hardware already procured, and need total infrastructure control, self-hosting is completely valid. CaseDesk is purpose-built for teams that want the privacy of self-hosting without the operational burden.
At a glance
How the options compare
Feature OpenAI API Azure OpenAI Self-hosting CaseDesk
UK / EU data residency ✗ Varies ✓ ✓
DSPT-ready (NHS) ✗ Partial Possible ✓
Open-source model support ✗ Limited ✓ ✓
OpenAI-compatible API ✓ ✓ ✓ ✓
No DevOps required ✓ Partial ✗ ✓
Dedicated GPU (no shared compute) ✗ Varies ✓ ✓
Predictable per-hour pricing ✗ ✗ Hardware cost ✓
Scale to zero when idle ✓ Varies ✗ ✓
No vendor lock-in ✗ ✗ ✓ ✓

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