RunAnywhere: The default way of running on-device AI at Scale

Edge AI is inevitable, but shipping it is painful: every device class behaves differently, runtimes vary, models are huge, and performance collapses under memory and power constraints. RunAnywhere turns that into an enterprise-ready workflow: one SDK to run models on-device, plus a control plane to manage models, enforce policies, and measure outcomes across thousands of devices.

The Problem

Edge AI is inevitable — users want instant responses, full privacy (health, finance, personal data), and AI that actually works on planes, subways, or spotty rural connections.

But shipping it today is brutal:

• Every device (iPhone 14 vs Android flagship vs low-end) has wildly different memory, thermal limits, and accelerators.
• Teams waste quarters rebuilding model download/resume/unzip/versioning, lifecycle (load/unload without crashing), multi-engine wrappers (llama.cpp, ONNX, etc.), and cross-platform bindings.
• No real observability — you're blind to fallback rates, per-device performance, crashes tied to model version.

Most teams either give up on local AI or ship a brittle, hacked-together experience.

The Solution: Complete AI Infrastructure

RunAnywhere isn't just a wrapper around a model. It is a full-stack infrastructure layer for on-device intelligence.

The "Boring" Stuff is Built-in

We provide a unified API that handles model delivery (downloading with resume support), extraction, and storage management. You don't need to build a file server client inside your app.

Multi-Engine & Cross-Platform

We abstract away the inference backend. Whether it's llama.cpp or ONNX, you use one standard SDK across:

• iOS (Swift)
• Android (Kotlin)
• React Native
• Flutter

Hybrid Routing (The Control Plane)

The future isn't "Local Only"—it's Hybrid. RunAnywhere allows you to define policies: try to run the request locally for zero latency and privacy; if the device is too hot, too old, or the confidence is low, automatically route the request to the cloud.

Active Founders

Sanchit Monga, Co-founder & CEO

Former Intuit engineer building RunAnywhere, the infrastructure layer for deploying fast, private, multimodal AI on-device at scale. Deep background in mobile SDKs, platform tooling, and developer products, including systems used by 50M+ active users. Previously founded products across consumer discovery, context management, agentic documentation, and mobile testing, and now focused on making on-device AI production-ready across mobile, edge, and embedded devices.

Shubham Malhotra, Co-founder & CTO

Co-founder & CTO of RunAnywhere (W26). Built MetalRT: the first complete multi-modal inference engine for Apple Silicon. Custom Metal GPU kernels that pushed on-device voice AI from 900ms to ~110ms. Ex-Amazon EC2 Spot ($100M+ ARR), Ex-Microsoft Azure. Peer-reviewed researcher.

Get Started

Open Source SDKs: github.com/RunanywhereAI/runanywhere-sdks (~10.1k stars on GitHub)

Documentation: docs.runanywhere.ai

Website: runanywhere.ai

Try our demo apps:

• Android
• iOS

Get in Touch

We're in full execution mode post-launch and hunting design partners and early feedback.

Are you:

• Building voice AI, offline agents, privacy-sensitive features (health/enterprise/consumer), or hybrid chat in your mobile/edge app?
• Looking to eliminate cloud inference costs for repetitive queries while keeping complex ones fast?
• Managing a fleet where OTA model updates and observability would save you engineering months?

Contact us:

• Email: san@runanywhere.ai
• Schedule a call: calendly.com/sanchitmonga22

We'd love to hear what sucks about your current on-device AI stack — honest war stories help us prioritize.