CaseDesk
CaseDesk vs RunPod

CaseDesk vs RunPod

RunPod rents GPU compute on shared cloud infrastructure. CaseDesk gives your team a dedicated AI endpoint in your chosen region — answer five questions and it is live in minutes.

✓ Dedicated endpoint — no shared GPU ✓ Data stays in your chosen region ✓ Answer 5 questions — live in minutes

What is RunPod?

What it is

RunPod is a GPU cloud marketplace where you rent compute by the hour. It offers a large catalogue of pre-built AI templates, on-demand and spot GPU pods, and serverless endpoints — all running on RunPod's shared infrastructure.

Who it's for

RunPod is well-suited for ML researchers, fine-tuners, and individual developers who need cheap GPU access for experiments, training runs, or short-lived inference workloads and do not have compliance or data-residency requirements.

Where CaseDesk differs

CaseDesk deploys open-source models — DeepSeek, Llama, Qwen, Gemma — to a dedicated managed endpoint in your chosen region (UK, EU, or US). CaseDesk manages all infrastructure. You answer five questions and receive a running endpoint with OpenAI, Anthropic, and Gemini-compatible APIs. Your data never leaves your chosen region. CaseDesk also includes an OKF knowledge layer — your organisation's documentation and approved policies are built into every endpoint, available to the model at query time. No cloud GPU platform offers this.

Feature comparison

Comparison based on publicly available product information and CaseDesk's current positioning. Last updated 2026-07-10.

Feature CaseDesk RunPod
Infrastructure model ✓ CaseDesk Dedicated — managed endpoint in UK, EU, or US RunPod-owned shared cloud infrastructure
Dedicated GPU ✓ Yes — your endpoint, no shared workloads No — shared GPU pods
Data residency ✓ UK, EU, or US — your choice, data stays in region RunPod cloud regions — no residency guarantee
Infrastructure management ✓ Fully managed by CaseDesk — no ops required Managed by RunPod on their infrastructure
OpenAI-compatible API Yes — built-in for every deployment Yes — via vLLM templates
Anthropic-compatible API ✓ Yes — built-in for every deployment No
Gemini-compatible API ✓ Yes — built-in for every deployment No
Pricing model ✓ Flat subscription — Starter from £249/month Pay per GPU-hour (variable, usage-based)
Setup ✓ Answer 5 questions — live in minutes, no code Manual template selection, pod configuration
Always-on endpoint ✓ Yes — persistent dedicated endpoint with scale-to-zero Pods terminate unless kept running manually
Organisation knowledge layer ✓ Yes — OKF bundle built in, your docs and policies at query time No

Detailed breakdown

Dedicated vs shared infrastructure

With RunPod, your models run on RunPod's shared hardware alongside other customers' workloads. CaseDesk gives every team a dedicated GPU endpoint — no other customer's traffic runs on the same hardware. For engineering teams handling sensitive data, regulated industries, or simply wanting predictable performance, a dedicated endpoint removes the uncertainty of shared infrastructure.

Predictable pricing vs pay-per-hour

RunPod's per-GPU-hour billing makes sense for short experiments, but for a team running AI queries all day the cost is unpredictable. CaseDesk uses flat subscription pricing — you know your monthly cost before the month starts. At production scale, a dedicated endpoint on a predictable subscription is consistently more cost-effective than hourly shared GPU billing.

Data privacy and regional residency

Every prompt sent through RunPod is processed on RunPod's infrastructure with no guaranteed data residency. CaseDesk deploys to your chosen region — UK (eu-west-2), EU (Netherlands), or US — and your data never leaves that region. CaseDesk's control plane never sees your inference traffic. This matters for GDPR-regulated teams, UK-based organisations, and any team that needs to tell auditors exactly where their AI data lives.

No vendor lock-in

RunPod's pod and serverless model is RunPod-specific. Migrating away means rebuilding your deployment setup. CaseDesk deploys standard vLLM on Kubernetes. If you stop using CaseDesk, the underlying deployment is portable — it is a standard container on standard infrastructure, not a proprietary runtime.

Which one should you choose?

Choose RunPod

Choose RunPod if you need cheap GPU access for short-term experiments, one-off fine-tuning runs, or burst workloads and you have no data-residency or compliance requirements.

Choose CaseDesk

Choose CaseDesk if your team needs a dedicated, always-on AI endpoint with OpenAI/Anthropic/Gemini-compatible APIs, regional data residency, and flat predictable pricing — with no infrastructure to manage.

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