Private AI vs Local AI: What's the Difference?

Private AI and local AI are related, but they're not the same thing. In this article, we'll break down the differences between private and local AI and explain where cloud AI fits into the picture.

What Is Private AI?

Private AI refers to any AI system designed to process data within defined privacy, security, and governance boundaries. Under this broad definition, the term can apply to many different types of AI systems, for example:

• A model running entirely on your laptop.
• A self-hosted model running on a company server.
• An AI service deployed in a private cloud or virtual private cloud.
• A hosted assistant that removes identifying metadata and does not retain prompts.
• A hybrid system that handles sensitive tasks on-device while routing more computationally intensive tasks to protected cloud infrastructure.

When a product claims to offer private AI, that does not automatically mean your data never leaves your device. In other words, active inference—the point at which the model processes your prompt and generates a response—can still happen in the cloud.

A private AI service may say, "We do not train on your data," but that does not necessarily mean "Your data never leaves your device."

What Is Local AI?

Local AI refers to AI systems that run on a device or machine you control. After you download the model weights, inference takes place directly on your device, with your CPU, GPU, or neural processor handling the computation.

On a laptop, a typical local AI workflow looks like this:

1. You download a local AI app and a compatible model.
2. The model is then stored on your device.
3. When you send a prompt, it is processed using local memory and compute.
4. The response is generated locally.
5. Chat history is stored locally—or not stored at all—depending on the application's settings.

Once the model and runtime have been downloaded, many local AI tools can work without an internet connection. However, you'll still need internet access to search for models, download updates, or use optional online features.

Local AI is not limited to desktop chatbots. On-device models can summarize a webpage, classify text, transcribe audio, power writing tools, or help an agent make decisions.

What makes these systems local is where inference happens.

Private AI vs Local AI at a Glance

To summarize, local AI answers, "Where does the computation happen?" Private AI answers, "Who can access the data, for how long, and for what purpose?"

See the table below for a more detailed comparison of local AI and private AI:

Is Local AI Always Private?

Not necessarily. Local inference removes one major risk—the need to send your prompt to a remote model—but other parts of the application can still expose data.

For example, a local AI tool may still contact external services for:

• Web search and live research.
• Cloud model fallback when the local model cannot complete a task.
• Account sync and cross-device chat history.
• Analytics, crash reports, or telemetry.
• Plugins, connectors, and agent actions.
• Model discovery, downloads, and software updates.

Other risks, such as malware, insecure backups, or excessive app permissions, can also expose locally stored chats and documents.

Can Cloud AI Be Private?

Yes, although it provides a different kind of privacy boundary. A privacy-focused cloud service can remove identifying metadata, encrypt traffic, avoid training on conversations, minimize retention, and isolate processing. However, your active prompt still reaches remote infrastructure unless the service is specifically designed to prevent the operator from accessing it during processing.

Here are a few examples of different private AI architectures in practice:

• Sigma Browser protects cloud-assisted AI workflows with end-to-end encryption while keeping sensitive parts of each interaction on your device to reduce unnecessary server processing. Sigma also does not store credentials or task data after an AI Agent finishes a task.
• Duck.ai anonymizes requests before sending them to supported model providers. DuckDuckGo says providers must not train on chats and generally use zero data retention. The model still runs remotely.
• Proton Lumo processes prompts on Proton-controlled servers, discards the processing data after generating a response, and uses zero-access encryption for saved history. It is a private hosted AI service, not local AI.
• Apple Private Cloud Compute is a hybrid example. Apple Intelligence handles tasks on-device when possible and can route more computationally demanding work to protected cloud infrastructure designed for stateless processing.

The caveat with cloud AI is that, in many cases, these services are proprietary and use closed-source models, meaning that you can't inspect how personal data is really processed once it leaves your device. Essentially, you're trusting another company with your data based on a promise. This is why, when it comes to privacy, local AI processing has a big advantage.

Why Local AI Models Are Better for Privacy

To understand why local AI models offer better privacy, let's consider Sigma Browser, the AI-powered browser we've built.

Sigma includes a catalog of downloadable local models inside Private Mode, which allows you to easily download and chat with Qwen, Gemma, GLM, or Nemotron using our built-in AI agent. With a local model in Sigma, you can:

• Chat with websites
• Summarize web content you're currently viewing
• Write and edit text
• Analyze documents and web pages
• Ask questions and brainstorm ideas
• Perform actions on web pages

This setup has three main practical advantages:

• The AI is browser-native. It can work with page context while you read, research, and write.
• The local runtime is integrated. You do not need to configure a localhost endpoint or install a separate model server, making it easy to get started.
• It can work offline. Once the required model is available, supported local tasks do not depend on cloud inference.

At the time of writing, Sigma's catalog includes models from these families:

Do keep in mind, however, that this does not mean that every Sigma feature works offline. For example, live web search, Deep Research, connected services, downloads, and websites themselves require an internet connection.

Our local-browser comparison makes this distinction clear: local models can power private chat and selected agent workflows, while online tools remain online.

With that context, it might seem like local AI is always superior to cloud AI, but that's not necessarily the case in every situation. Let's talk about the trade-offs between local AI and private cloud AI.

Local AI Advantages

Because local AI runs inference on your hardware, it offers more privacy in most cases. Its main advantages include:

• Stronger data control. Prompts and local documents can stay within the device boundary.
• Offline access. Downloaded models can keep working without a connection.
• Predictable availability. A provider outage or API rate limit does not stop local inference.
• No per-message cloud cost. After hardware and electricity, repeated local use does not consume hosted-model credits.
• Customization. Advanced users can choose model sizes, quantization levels, system prompts, and local tools.

However, the biggest local AI limitation comes down to the fact that the best AI models can't run locally—they need data-center-grade hardware and GPUs with hundreds of gigabytes of VRAM. If you're running a local AI model on a laptop, for example, you may only be able to run models with 9–24 billion parameters at aggressive quantization levels. This may be sufficient for your tasks, and models like these are certainly powerful enough to summarize web pages and answer questions, but they're less advanced than flagship models with 100+ billion parameters.

Private Cloud AI Advantages

By contrast, with hosted or private cloud AI, you can usually run much more powerful models. The main advantages include:

• Larger models. Cloud infrastructure can run models that do not fit on consumer hardware.
• Lower hardware requirements. You only need a browser or app, not a high-memory computer.
• Cross-device access. You can sync data between multiple devices.

However, as we said above, you need to understand how the provider handles data during active processing, and you don't always have a way to verify its claims. Also, hosted AI deployments can easily cost hundreds of dollars per month for rented VPS services and hardware, while local AI is essentially free.

Which Should You Choose?

Choose local AI when:

• You don't need the best model for your work
• You're concerned about privacy in AI
• You don't want prompts to leave your hardware

Choose privacy-focused hosted AI when:

• You need access to larger or more capable models
• You want fast responses without relying on powerful local hardware
• You need to use the same AI service across multiple devices

Choose private cloud or self-hosted AI when:

• You are deploying AI across an organization
• You work with regulated or highly sensitive data
• You need centralized access controls, auditing, or approved infrastructure

Final Thoughts

To summarize, the terms private AI and local AI are sometimes confused, but they refer to different concepts in artificial intelligence. Local AI tells you where the model runs, while private AI tells you how the entire system handles your data. Local AI isn't necessarily private, though most local AI services are privacy-focused. Private AI can, in theory, run in the cloud. In practice, however, cloud AI services usually offer less privacy than local AI services.

If you want the clearest possible privacy boundary, start with local inference and use online features only when the task requires them. Sigma Browser is one of the easiest ways to get started with local AI: you can choose and run a local model inside the browser without installing any specialized inference software or configuring a separate local server. Sigma is free on desktop and iPhone.