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

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/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.

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.

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

Founder

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.

Shubham Malhotra

Founder

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.

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 perf, crashes tied to model version

Result: 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.

1. 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.

2. Multi-Engine & Cross-Platform We abstract away the inference backend. Whether it's llama.cpp or ONNX etc, you use one standard SDK.

iOS (Swift)
Android (Kotlin)
React Native
Flutter

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

Voice AI Pipeline Demo

Quick Links

OSS SDKs: github.com/RunanywhereAI/runanywhere-sdks (star if it vibes!)
Full Docs: docs.runanywhere.ai
Website: runanywhere.ai

Try our demo apps:

Android
iOS

Our Ask

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

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

Get in touch:

Drop us a line: san@runanywhere.ai
Book a quick call: calendly.com/sanchitmonga22
Or just tell us what sucks about your current on-device AI stack — honest war stories help us prioritize.

Excited to hear what you're building and how we can make on-device AI actually shippable at scale.

YC Photos