Personal AI assistants help with tasks, answers, and automation. The main fork in the road is where they run: on your own hardware (local / self-hosted) or in the cloud (hosted by a vendor). This guide helps you choose based on privacy, cost, control, latency, and hardware.
Why it matters
- Privacy and data: Local assistants keep data on your machine; cloud assistants send data to vendor servers.
- Cost: Self-hosted usually has no per-query fee but may require hardware; cloud often has subscriptions or usage-based pricing.
- Hardware and edge: Some use cases need offline or embedded deployment; others only need a browser and an internet connection.
Comparison axes
Privacy and data
- Local / self-hosted: Data stays on your device or your infrastructure. No requirement to send prompts or logs to a third party.
- Cloud: Requests and often data are processed on vendor infrastructure. Check the provider’s privacy policy and data handling if that matters for your use case.
Cost
- Local: Typically free software; you pay for hardware and power. No per-user or per-token billing.
- Cloud: Often freemium or subscription; heavy use can mean higher costs. Predictable if usage is predictable.
Control and customization
- Local: You control updates, models, and integration. You can tailor or fork the stack.
- Cloud: You follow the vendor’s roadmap and feature set. Less control, more convenience.
Latency and offline use
- Local: No round-trip to the cloud; can work offline if the model and tools run on-device.
- Cloud: Depends on network; usually requires internet. Often lower latency for very large models that you wouldn’t run locally.
Hardware (edge and embedded)
- Local: Can target edge devices, embedded systems, or constrained hardware (e.g. PicoClaw for lightweight and edge).
- Cloud: No local hardware requirement; any device with a browser and connectivity can use it.
When to choose local / self-hosted
Consider local when:
- You want to minimize data leaving your environment.
- You have or can provision hardware (PC, server, or edge device).
- You need offline or air-gapped use.
- You want to customize the stack or avoid per-seat or per-token costs at scale.
Examples of self-hosted personal AI assistants on db.fyi:
- OpenClaw — open-source personal AI assistant with autonomous capabilities; self-hosted, modular workflows.
- PicoClaw — lightweight personal AI assistant for embedded and edge environments.
- Ollama — run open-source LLMs locally; often used as the model layer behind local assistants.
When to choose cloud
Consider cloud when:
- You want minimal setup and no hardware to manage.
- You need access from many devices or teams with a single account.
- You prefer the vendor to handle scaling, uptime, and model updates.
- Offline use and data locality are not primary requirements.
Examples of cloud assistants on db.fyi:
- ChatGPT — conversational AI and coding assistance; free tier and Plus.
- Claude — AI assistant with long context and tool use.
- Perplexity — research-oriented assistant with citations.
Quick comparison
| Axis | Local / self-hosted | Cloud | |------|----------------------|--------| | Privacy | Data on your side | Data on vendor infrastructure | | Cost | Hardware + power; no per-query fee | Often subscription or usage-based | | Control | Full control over stack and updates | Vendor roadmap | | Offline | Possible if model runs on-device | Typically requires internet | | Hardware | Can target edge and embedded | No local compute required |
Tool links (db.fyi)
Personal AI Assistants (self-hosted):
Local LLMs / runtimes:
Cloud assistants: