Key facts
| Local option | Ollama, LM Studio or llama.cpp with a local chat interface |
| Private cloud | Inference inside your network or VPC with managed operations |
| Hybrid | Sensitive steps local, other workloads routed to a private endpoint |
| API | OpenAI-compatible endpoint keeps clients portable |
| Models | 30+ models available on the hosted catalogue |
| Residency | Region selection plus VPC, on-prem and air-gapped options |
| Endpoint status | Chat, streaming, tools, JSON mode, embeddings, RAG and agents live |
| Coming soon | Audio, image, moderation, files, batch, fine-tuning, assistants and responses |
TL;DR
- Local, private cloud and hybrid all keep data under your control, at different effort levels.
- Local is private by construction but limited by your hardware.
- Private cloud adds scale, uptime and managed operations.
- Hybrid balances privacy and capability per workload.
- An OpenAI-compatible API keeps every option reachable from the same code.
How it works, step by step
- Classify the data the assistant will handle.
- Decide where each class may be processed: device, network or external.
- Prototype with a local runtime on real tasks.
- Compare quality and throughput against a private cloud deployment.
- Design hybrid routing rules for tasks that exceed local capacity.
- Add access control, logging and retention to the chosen path.
- Re-evaluate quarterly as models and requirements change.
Try it yourself
Open the ChatGPT alternative finder →
What makes a ChatGPT alternative private
Privacy is about where the data goes, who can access it and what is retained. A public chat service processes prompts outside your boundary, under terms you do not control. A private alternative changes that: inference runs on your device, in your VPC or inside your own data centre.
Privacy also has an operational half. Access control, audit logs, retention rules and the ability to revoke access matter as much as the network path, because an internal tool still needs governance if it touches sensitive material.
Local, private cloud or hybrid
Each option trades effort for capability.
- Local: a runtime such as Ollama or LM Studio on your hardware. Nothing leaves the device, offline work is easy, but model size and concurrency are capped.
- Private cloud: managed or self-managed inference inside your network or VPC. Scales further, supports larger models and can carry a service commitment.
- Hybrid: local for sensitive or offline steps, private cloud for everything else. Most teams converge here because it matches how data classes actually differ.
Anchor the design on the OpenAI-compatible API so all three look the same to your application.
Choosing by workload
List your workloads with their data class and quality requirement. Personal notes and offline field work fit local models. Internal document question answering typically fits a private deployment with a region choice. Heavy reasoning over permitted data can route to the same private endpoint with a larger model.
Plugsky provides an OpenAI-compatible API with 30+ models and region selection plus VPC, on-prem and air-gapped deployment. Chat, streaming, tools, JSON mode, embeddings, RAG and agents are live; audio, image, moderation, files, batch, fine-tuning, assistants and responses are coming soon. See pricing for plans and start free with plugsky-micro and plugsky-lite.
Honest comparison
| Concern | Local models | Private cloud (Plugsky) | Public chat service |
|---|---|---|---|
| Data path | Stays on your device | Stays inside your boundary | Leaves your network |
| Model quality | Limited by local memory | 30+ models on one API | Provider catalogue |
| Concurrency | One user per machine | Scales with the deployment | Provider-managed |
| Operations | You run everything | Managed or co-managed | Provider-managed |
| Cost shape | Hardware and power | Flat monthly plans | Subscription or per-token |
Frequently asked questions
What is the difference between local and private cloud?
Local runs on your own device with no network dependency. Private cloud runs in your network or VPC with managed operations, which adds scale and uptime at the cost of more infrastructure.
Is a local model as good as a hosted one?
Not for every task, because the largest models generally will not fit your hardware. Many everyday tasks work well with small quantized models, and hybrid routing fills the gap.
How do I keep data private in a hybrid setup?
Define which data classes may leave the device, enforce it in code rather than policy alone, and keep sensitive corpora and steps entirely local.
Do private alternatives support tools and RAG?
Local runtimes and hosted private platforms support tool calling and retrieval, though capability varies by model. Verify function calling and embeddings support for your chosen path.
What about compliance and audit?
Look for region selection, published terms, an SLA and audit logging. Map those against your requirements instead of assuming privacy alone satisfies compliance.
Can I start small and grow?
Yes. Start with a local pilot on real tasks, then move to a private cloud deployment when concurrency, model size or uptime becomes a constraint.
How do I compare quality objectively?
Build a fixed evaluation set from your own tasks and score task success, format validity and refusal behaviour. Fluency impressions are unreliable.