Key facts
| First step | Separate app code from the model runtime behind a stable API |
| Portability | OpenAI-compatible endpoints keep clients unchanged |
| Artefacts | Version models, prompts and configuration together |
| Evaluation | Run the same eval set locally and in the target environment |
| Cutover | Route a share of traffic, then widen as metrics hold |
| Rollback | Keep the local endpoint as a configurable fallback |
| Deployment options | Plugsky offers cloud, VPC, on-prem and air-gapped options |
| Endpoint status | Chat, streaming, tools, JSON mode, embeddings, RAG and agents live |
TL;DR
- Containerise and standardise the API before moving anything.
- Version models, prompts and configuration so environments match.
- Re-run your evaluation set in the target before cutting traffic.
- Shift traffic gradually and keep rollback as a config change.
- Private cloud removes the single-machine ceiling without losing control.
How it works, step by step
- Document the local stack: runtime, models, quantization, prompts and data flow.
- Containerise the runtime and expose an OpenAI-compatible API surface.
- Move prompts, model names and settings into version-controlled configuration.
- Choose the target: VPC, on-prem or an air-gapped deployment inside your boundary.
- Re-run your evaluation set and compare outputs and error rates.
- Cut over a small share of traffic, then widen as metrics hold.
- Keep the local endpoint as fallback and document the rollback procedure.
Try it yourself
Open the API migration checker →
Why local pilots stall
A workstation pilot proves the idea and then hits structural limits. One machine serves one user well and a small team badly. Long contexts consume memory that cannot be added without downtime. There is no redundancy, no shared audit trail and no clean way to grant access to more people.
None of that invalidates the pilot. It means the workload has outgrown the form factor. The migration question is not whether to move, but how to move without losing the privacy and cost control that made the local setup attractive.
The migration sequence
Start by separating concerns. Model inference should sit behind an HTTP API that speaks the OpenAI-compatible shape, with your application talking to that API rather than to a specific runtime. Containerise the runtime so the same image runs locally and in the target environment.
- Version everything: prompts, model identifiers, decoding parameters and retrieval settings.
- Keep data flows explicit: document what leaves the application and where it is stored.
- Choose the boundary: a VPC, an on-prem cluster or an air-gapped environment, depending on obligations.
Because the API surface is standard, the target can be a self-managed cluster or a managed private deployment without changing client code.
Evaluation and cutover
Run the same evaluation set in both environments before any traffic moves. Compare task accuracy, JSON validity, tool-call correctness and refusal behaviour, because environment and version differences change outputs in ways that fluency scores hide.
Then cut over in stages: internal users, then a small share of production, then the rest. Keep the local endpoint reachable as a fallback and make the switch a configuration value. Plugsky offers OpenAI-compatible chat, streaming, tools, JSON mode, embeddings, RAG and agents as live endpoints with region selection plus VPC, on-prem and air-gapped deployment; audio, image, batch and fine-tuning endpoints are coming soon. See pricing for plan details.
Honest comparison
| Stage | Local workstation | Private cloud deployment | Check before deciding |
|---|---|---|---|
| Capacity | One user on one machine | Scales and supports concurrency | Peak load |
| Control | Full physical control | Inside your network or VPC | Compliance boundaries |
| Operations | You run everything | Managed or co-managed | Team time available |
| Model ceiling | Limited by local memory | 30+ models available | Quality requirements |
| Rollback | Immediate and local | Config change to fallback | Risk tolerance |
Frequently asked questions
Do I have to rewrite my application?
No. If your local runtime and the target both expose an OpenAI-compatible API, the migration is configuration: base URL, key and model names, plus a fresh evaluation run.
What should be in version control?
Prompts, model identifiers, decoding parameters, retrieval settings and the evaluation set. Data itself usually stays out unless it is small and non-sensitive.
How do I compare local and cloud quality?
Run the same fixed evaluation set in both environments and compare task accuracy, structured output validity and refusal behaviour, not just fluency.
When should I move off a workstation?
When you need concurrency, guaranteed uptime, models larger than local memory, or an audit trail that a personal machine cannot provide.
Can I keep some workloads local?
Yes. Hybrid routing is normal: sensitive or offline steps stay local while heavy reasoning and long-context work runs in the private cloud.
How long does a migration usually take?
The technical change is small if the API is compatible; most of the time goes into evaluation, data-handling review and a gradual cutover.
What about data residency?
Choose a deployment region or an in-boundary option that matches your obligations, and document the data flow before moving production traffic.