RunAnywhere

Product Introduction

1. RunAnywhere is an on-device AI platform that optimizes large language model (LLM) deployment through intelligent request routing, real-time cost tracking, and privacy-preserving data processing. It operates across mobile devices and cloud infrastructure, automatically directing queries to the most efficient processing location based on complexity and sensitivity requirements. The platform supports all major LLM providers while enabling offline functionality for latency-sensitive applications.
2. The core value lies in reducing AI operational costs by 30-91% through hybrid processing, maintaining zero data leakage for sensitive information, and delivering sub-100ms response times for common queries. Enterprises achieve predictable billing through real-time cost analytics while meeting strict compliance requirements in regulated industries like healthcare and finance.

Main Features

1. Intelligent Routing dynamically selects between on-device and cloud-based LLMs using complexity analysis and privacy rules, processing simple queries locally on iOS/Android devices while reserving cloud resources for computationally intensive tasks. This dual-path architecture automatically adapts to network conditions, model availability, and cost parameters defined in the SDK configuration.
2. Privacy-First Architecture ensures sensitive data never leaves user devices through encrypted local processing, with configurable data governance policies that meet HIPAA and GDPR standards. The platform isolates Personally Identifiable Information (PII) using hardware-backed security enclaves on mobile devices, enabling healthcare diagnostics and financial transactions without cloud transmission.
3. Cost Optimization combines on-device processing (free of API charges) with cloud expenditure monitoring, providing granular cost breakdowns per model/provider in real-time dashboards. The system predicts monthly savings through an ROI calculator that factors in request volume, token counts, and customizable on-device processing ratios (1-100%).
4. Zero-Latency Execution leverages native mobile SDKs (Swift/Kotlin) for instant responses to common queries like text auto-completion and basic translations, eliminating network round-trips. Benchmarks show 50-200ms faster performance compared to cloud-only solutions, with full offline functionality when devices lose connectivity.
5. Universal Compatibility integrates with OpenAI, Anthropic, Google, and open-source LLMs through a unified API, allowing seamless provider switching without code changes. Developers deploy hybrid AI architectures where on-device models (e.g., Phi-3, Llama 3) handle 30-70% of queries while maintaining fallback to cloud models like GPT-4o or Claude 3.5.
6. Real-Time Analytics track per-request costs, latency metrics, and model performance through centralized monitoring, with automated alerts for budget overruns or SLA violations. Usage patterns are visualized across request types, user segments, and geographical regions to optimize resource allocation.

Problems Solved

1. Enterprises face uncontrolled LLM API costs due to inefficient cloud-only architectures and inability to process simple queries locally. RunAnywhere solves this by offloading 30-100% of requests to on-device models, reducing monthly cloud expenditures by up to $34,476/year for 10M requests while maintaining quality of service.
2. The platform specifically targets mobile-first industries handling sensitive data - healthcare providers processing patient records, fintech apps analyzing transaction patterns, and e-commerce platforms managing user behavior data. Developers in regulated sectors benefit from built-in compliance frameworks that eliminate custom security implementations.
3. Typical use cases include real-time medical symptom checking on patient devices, in-app financial fraud detection without data externalization, and offline language translation for travelers. Enterprises deploying AI chatbots at scale use RunAnywhere to balance cost/performance across user tiers - premium users get cloud models while free tiers use on-device processing.

Unique Advantages

1. Unlike cloud-only AI services, RunAnywhere uniquely combines device-edge-cloud processing with automatic failover, achieving 99.95% uptime even during provider outages. Competitors lack integrated cost controls and require manual model selection, whereas RunAnywhere uses ML-based routing that improves accuracy through usage feedback.
2. The platform introduces patent-pending "Privacy Routing" that classifies data sensitivity using on-device NLP before processing, ensuring compliance without human review. No competitors offer real-time cost tracking at the token level or infrastructure cost projections based on actual usage patterns.
3. Competitive differentiation stems from the SDK's 3-minute setup process, which automatically provisions on-device models (20MB average size) during initialization. Unlike web-based AI tools, RunAnywhere's native mobile implementation achieves 2-3x faster inference speeds through hardware acceleration (Core ML, TensorFlow Lite).

Frequently Asked Questions (FAQ)

1. How does automatic routing decide between on-device and cloud processing? The SDK analyzes query complexity (token count, intent classification), device capabilities (CPU/GPU availability), and privacy tags attached to input data. Simple requests like text summarization default to on-device models, while multi-step reasoning tasks route to cloud LLMs, with thresholds adjustable via API parameters.
2. What types of sensitive data can be processed securely? The platform handles PHI (Protected Health Information), credit card numbers, and government IDs using AES-256 encryption in secure enclaves, with automatic data masking for outputs. All PII processing occurs in memory without local storage, verified through third-party penetration testing reports available to enterprise clients.
3. Which LLM providers are currently supported? RunAnywhere integrates with 12+ providers including OpenAI (GPT-4o, GPT-3.5 Turbo), Anthropic (Claude 3.5 Sonnet), Google (Gemini 2.5 Pro), and open-source models (Llama 3, Mistral 8x22B). Custom model deployments are supported through ONNX runtime compatibility for enterprise-specific AI models.
4. How accurate are the savings estimates in the ROI Calculator? Projections account for real-time pricing from LLM providers (updated July 2025), infrastructure costs per cloud region, and historical patterns of on-device success rates. Enterprises can input actual usage logs via CSV for personalized simulations, with variance under 5% compared to real-world deployments.
5. Does on-device processing work without internet connectivity? Yes, the SDK's offline mode executes locally stored models (20-500MB depending on license) for 38 predefined task types including sentiment analysis and entity extraction. Developers configure fallback behaviors when connectivity resumes, with automatic sync to cloud logs for delayed analytics reporting.