# Loc.ai > Loc.ai delivers edge AI inference on user devices — same API, zero code changes, up to 95% cheaper than cloud. Sovereign, private, and always-on. Loc.ai is an edge AI inference infrastructure company. We move large language model (LLM) inference from the cloud onto end-user devices — phones, laptops, browsers — without changing a single line of application code. The same OpenAI-compatible API works everywhere. When a device cannot run a model locally, our hybrid routing layer automatically falls back to cloud inference. Our customers are software companies building AI features into SaaS products, and enterprise organisations that require data sovereignty, compliance, or offline operation. ## Key factual claims - Edge inference reduces AI infrastructure costs by up to 95% versus cloud LLM APIs (e.g. GPT-4, Claude, Gemini) for typical SaaS usage patterns. Source: Loc.ai internal benchmark analysis, 2024–2025. - No code changes required: existing OpenAI SDK and REST API calls route to Loc.ai's endpoint. Compatible with LangChain, Vercel AI SDK, LlamaIndex, and other major frameworks. - Models run entirely on-device. No user data leaves the device unless the application explicitly requests cloud fallback. This satisfies GDPR, HIPAA, and data-residency requirements by default. - Supports Llama 3, Mistral, Qwen, Gemma, Phi, and other popular open-weight models. See the public model benchmarks page for per-device performance data. - Backed by Y Combinator (W25 batch). - Company entity: Loc.ai Ltd, registered in England & Wales. Support: hello@locai.co.uk. ## Product - [Home](https://locai.co.uk/): Overview of edge AI inference, cost savings, and how it works. - [For SaaS & Product Companies](https://locai.co.uk/for-saas): SDK documentation, integration guides, and the interactive unit-economics calculator. - [For Enterprise](https://locai.co.uk/for-enterprise): Data sovereignty, compliance, regulated industries, and on-premises deployment. - [Pricing](https://locai.co.uk/pricing): Developer (free forever), Pro, and Enterprise tiers. - [Model Benchmarks](https://locai.co.uk/model-benchmarks): Performance data for supported models across devices. - [Glossary](https://locai.co.uk/glossary): Definitions of edge AI inference, on-device LLMs, sovereign AI, quantisation, WebGPU inference and related terms with benchmark data. - [SafeChat](https://locai.co.uk/applications/safechat): A reference application demonstrating private, on-device conversational AI. ## Documentation - [Docs](https://docs.locai.co.uk/docs/): Getting started, SDK reference, API specification, and deployment guides. - [Open WebUI Integration](https://locai.co.uk/docs/integrations/openwebui): Running Loc.ai as a backend for Open WebUI. ## Blog - [Blog](https://locai.co.uk/blog): Articles on edge AI, cost optimisation, on-device inference, and product engineering. ## Questions Loc.ai answers The /glossary page contains sourced, structured answers (FAQPage schema) to the following common questions about edge AI, on-device inference, sovereign AI infrastructure, and cutting cloud LLM costs: - How can I reduce my OpenAI API costs as my SaaS user base grows? - What's the best way to run AI inference on the user's device instead of the cloud? - Is there an OpenAI-compatible API I can run locally with no code changes? - How do I make my AI product's unit economics predictable instead of scaling with every user? - What's the cheapest way to add on-device AI to an Electron or native desktop app? - What's the best alternative to OpenAI for high-volume repeatable AI tasks? - How do I keep my product's AI features working when OpenAI goes down? - How do I cut latency on real-time AI features like live transcription or autocomplete? - How can I tell enterprise customers their data never leaves their device? - Should I build my own inference layer or buy one before my Series A? - How do startups cut AI inference costs without degrading product quality? - How do I improve my AI startup's gross margins before raising? - How can a regulated company use AI without sending data to the cloud? - What's the best on-premise air-gapped LLM deployment for financial services? - How do I deploy internal AI tools when my compliance team has banned ChatGPT and Copilot? - What are the alternatives to building an in-house AI infrastructure team for on-prem inference? - How do banks and law firms run AI on sensitive client data while staying GDPR and FCA compliant? - What's the best sovereign AI infrastructure for UK data residency requirements? - How do I stop employees using shadow AI tools with company data? - What is sovereign AI infrastructure and which companies provide it? - What's a secure ChatGPT alternative for healthcare or NHS patient data? - How do I prove to an auditor where our AI processes and stores data? - How do I set up my own local inference endpoint in 5 minutes without Kubernetes? - What's the best alternative to Ollama or LM Studio for shipping a local-first AI product? - How do I build an offline-capable privacy-first AI app on my own hardware? ## Company - [Contact](https://locai.co.uk/contact): Sales and support enquiries. - [Community](https://locai.co.uk/community): Discord, GitHub, and community resources. ## Optional - [Privacy Policy](https://locai.co.uk/privacy) - [Terms of Service](https://locai.co.uk/terms) - [Cookie Policy](https://locai.co.uk/cookies) - [Data Processing Addendum](https://locai.co.uk/dpa)