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For the easiest setup and strongest documented accuracy, choose a cloud LLM; for inference that stays on hardware you control, consider a local Ollama agent. Home Assistant supports both in Assist, but neither option is automatically the right one for every smart home. Your decision depends on command reliability, data locality, ongoing fees, hardware and maintenance, and how much control you expose to the agent.
What is the difference between a local and cloud LLM in Home Assistant?
A local LLM processes a conversation on an Ollama server you operate; a cloud LLM sends the conversation context needed for the request to an external provider, such as OpenAI or Google Generative AI. Home Assistant can use either as the conversation-agent stage in Assist. The LLM is not the whole voice assistant: wake-word detection, speech-to-text, the conversation agent, and text-to-speech are separate stages that can each use local, integration-provided, or cloud services.
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Which option is more accurate for smart-home commands?
Home Assistant’s published 2024.8 release notes reported a 98% benchmark score for cloud-based models and 84% for local models after prompt tuning. These are historical results for the models, prompts, and benchmark used at the time—not a forecast for every current model, Home Assistant release, or household command.
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In its 2024 comparison, Home Assistant said it saw the best results with cloud LLMs because they were then more powerful and easier to run than open-source options. That is the project’s dated assessment, not a universal or independently verified ranking. Models and integrations change, so treat it as useful context rather than a permanent winner declaration.
Accuracy for a home-control agent includes more than producing a sensible sentence. The model must interpret what you asked, select an available tool, identify the right exposed entity, and issue an appropriate action. A model that does not support tool use cannot control Home Assistant through the LLM API. A smaller local model may also make more mistakes or be less reliable over a conversation, so test the exact model and configuration you intend to use.
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The practical trade-off is not simply “private versus convenient.” Local inference moves more responsibility to your hardware and maintenance; cloud inference moves it to a provider and usage-based service. The table separates what Home Assistant’s published material establishes from what depends on your setup.
| Factor | Local Ollama agent | Cloud agent |
|---|---|---|
| Documented command benchmark | 84% for tuned local models in Home Assistant 2024.8; historical benchmark result | 98% for cloud models in Home Assistant 2024.8; historical benchmark result |
| Where inference runs | On the configured Ollama server; can stay on your local network | At the selected provider; the prompt and relevant home context are sent for the request |
| Outage dependency | Does not need a provider connection for inference, but the Ollama server must be reachable | Depends on network access and provider availability |
| Latency | Varies with host, model size, quantization, context length, and whether the model remains in memory | Varies with network conditions and provider load |
| Cost pattern | Hardware, electricity, updates, and maintenance; total depends on what you already own and local electricity costs | Recurring usage fees; total depends on provider, model, and request volume |
| Setup and upkeep | Requires an external Ollama server, a reachable server URL, model downloads, and model management | Requires the provider integration and account/API credentials |
| Control scope | Limited to tools and entities exposed to the selected Home Assistant API | Also limited to tools and entities exposed to the selected Home Assistant API |
| Current universal latency or total-cost figure | Not stated in Home Assistant’s cited documentation and comparison | Not stated in Home Assistant’s cited documentation and comparison |
Home Assistant’s 2024 comparison said its recommended Google model was 14 times cheaper than its recommended OpenAI model in that comparison. This is a model-specific, dated comparison—not a current price ratio or a general rule about all Google and OpenAI usage. Compare the models and rates available to you rather than projecting that figure onto a new configuration.
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Is a local LLM more private?
It can improve data locality: with Ollama configured on a server you control, inference can remain on your local network instead of being sent to a cloud LLM provider. Home Assistant’s privacy FAQ says the platform runs on the user’s own hardware and stores data locally, while optional services can involve third parties. The important distinction is that a cloud LLM integration sends the prompt and relevant home context needed to answer the request to its provider.
“Local” describes where inference happens, not an unconditional privacy guarantee. The Ollama server has to be configured and reachable, and the agent receives access to the tools and entities you expose. Review the server location and access, the conversation context being sent, and the agent’s permissions. Do not expose entities merely because the model might find them useful.
What can a Home Assistant LLM actually control?
An LLM can use only the tools and entities made available through its selected API. Home Assistant’s built-in Assist API provides intents for reading information about exposed entities and controlling them; it is not an administrative interface. That is the appropriate boundary for ordinary device commands.
Home Assistant also has a separate API that can expose administrative operations, including managing registries, system logs, automations, and configuration entries, to authorized users. That is a different and broader capability intended for administrators. Do not treat a demonstration of an LLM controlling a light as evidence that it should be allowed to edit automations or configuration.
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For experimentation with local models, Home Assistant recommends exposing fewer than 25 entities. This is a limit-minded starting point, not a magic safety threshold: choose only the devices and information the agent needs, and verify the available tools before granting control. The project’s 2024.8 notes recommended Llama 3.1 8B for local experimentation, but that recommendation is tied to the release-era guidance and does not establish the best model for a later setup.
How do I set up a local Ollama agent?
The documented local route is an Ollama conversation agent connected to an external Ollama server. Home Assistant does not run that model merely because the integration is installed; the server must be running, the model must be downloaded, and the configured server URL must be reachable over the network.
- Run an Ollama server. Choose a host you control and make sure it is available to Home Assistant over your network. The documentation describes an external server; it does not prescribe one universal hardware configuration.
- Add the Ollama integration and connect it to the server. Provide the server URL and, if your configuration uses one, the optional API key.
- Select and download a model. The model is downloaded during setup. For Home Assistant device control, confirm that it supports tools; ordinary text generation alone is not enough.
- Configure the agent deliberately. Relevant options include the model name, instructions, context-window size, conversation-history limit, model keep-alive behavior, and whether the model may control Home Assistant.
- Restrict its exposed entities. Start with only the devices needed for a small set of commands. Home Assistant’s guidance for local experimentation is fewer than 25 entities.
- Select the conversation agent in an Assist voice assistant and test it. Check both routine requests and likely ambiguities, then narrow access or adjust the configuration if it selects the wrong device or makes an unwanted tool call.
A useful pattern is to maintain two Ollama configurations: one for general conversation without device control and another with a deliberately restricted set of entities for control. This separates open-ended chat from actions that can change the home.
How do I set up a cloud agent?
Home Assistant’s conversation-agent framework documents cloud-provider paths including OpenAI and Google Generative AI. You need the corresponding provider integration and account/API credentials, then select that conversation agent in an Assist voice assistant.
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- Choose a supported provider integration and arrange credentials. Use the account/API access required by that provider; its usage terms and current rates are provider-specific.
- Configure the integration and select its conversation agent in Assist. Keep the scope of exposed entities limited to what the agent needs, just as you would for a local agent.
- Test the commands and context you plan to use. Verify that the agent understands the names of your devices and uses the intended actions before relying on it for routine control.
Cloud processing does not eliminate the need for least-privilege access. The provider receives the prompt and relevant home context required by the request, while the set of Home Assistant actions available to the agent remains bounded by the selected API and exposed entities.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What hardware and ongoing work does local inference require?
The documented setup requires an Ollama server reachable by Home Assistant, plus a model that supports tool use if you want device control. Home Assistant’s cited material does not establish a minimum RAM, VRAM, processor, or GPU specification that applies to every model. Performance depends on the host, model size, quantization, context length, and whether the model stays resident in memory, so a single universal hardware recommendation would be misleading.
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Local ownership also means taking responsibility for downloading and managing models, keeping the server available, and handling updates and maintenance. If the model is not kept in memory, its first response may behave differently from later ones; test the keep-alive setting and typical conversation history on your own host. These are operational variables, not a published latency guarantee.
How should I compare speed, cost, and outage behavior at home?
There is no universal response-time or dollar figure in Home Assistant’s cited material. Measure your own setup with the same representative requests, because local performance depends on model and host while cloud performance depends on the network and provider load.
- For speed: time both a simple device command and a longer request that needs context. Note whether the local model had to load, and repeat cloud checks at different times if provider load or network conditions vary.
- For cost: estimate cloud usage from your expected request volume and the provider’s current model pricing. For local use, account for hardware you must buy, electricity, and the time spent maintaining it; hardware already owned changes the comparison.
- For outages: check whether the Ollama server is reachable from Home Assistant and test your cloud agent when provider access is unavailable. A local model removes provider connectivity from inference, but it does not help if its own server is down.
- For reliability: test the exact entities and tools exposed, including device names that could be confused, and review whether the action performed matched the request.
Home Assistant’s 2024 article described local AI as better for privacy and, in the long term, the wallet, while noting that cloud models were then more capable and easier to run. Local models also tend to be smaller and use less electricity, according to that article. Those are broad trade-offs, not a promise that a particular local server will cost less than a particular volume of cloud use.
Which option should you choose?
Choose based on the constraint you most need to satisfy, rather than assuming one deployment is best on every axis.
- Choose cloud first if you want a simpler setup and prioritize the strongest documented Home Assistant benchmark result available here. Accept provider dependence, usage fees, and sending relevant conversation context to that service.
- Try local Ollama if keeping inference on hardware you control is a priority and you are willing to operate a server, manage models, and test tool use carefully.
- Use a staged approach if you are undecided: begin with a small, non-sensitive entity set, compare the same representative requests, and expand only when the model reliably uses the intended tools.
- Keep administration separate from ordinary device control. The Assist API’s device-oriented scope and the separate administrative API are not interchangeable permission levels.
The appropriate comparison is the current model and configuration you will actually run. Home Assistant’s 2024 results are a useful historical baseline, but they cannot settle a present-day choice without accounting for model changes, your host, current provider rates, and the entities you expose.
Frequently Asked Questions
Does a local Ollama setup work if the internet goes down?
Inference can continue without a cloud-provider connection if the Ollama server and Home Assistant remain reachable on your local network. That does not guarantee every part of a voice setup will work offline, because wake-word, speech-to-text, and text-to-speech are independently configured stages.
Does a 98% Home Assistant benchmark mean 98 out of every 100 commands will work?
No such broad interpretation is established by the release note figure. It is a score from Home Assistant’s 2024.8 benchmark under its tested conditions, not a household-level success guarantee or a universal prediction for arbitrary commands.
Can I let an LLM edit my automations as well as control devices?
That is a broader administrative capability than ordinary Assist device control. Home Assistant’s separate API can expose operations such as managing automations and configuration entries, so treat that access as administrator-level and do not grant it merely to enable routine device commands.
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