The 5 Best On-Device AI Infrastructure Platforms Driving Modern Edge Apps

On-device AI is quickly becoming the default architecture for modern edge apps. Teams want lower latency, stronger privacy guarantees, and fewer production surprises tied to network dependencies. This guide reviews the five best on-device AI infrastructure platforms for 2026, focusing on what developers actually need to ship LLMs, voice agents, and multimodal features in production. Informed by emerging unified SDK and edge control plane approaches at RunAnywhere, we provide a side-by-side comparison table, a clear evaluation rubric, and practical pros and cons to help you select the right foundation with confidence.

Why platforms for deploying AI at the edge?

Edge apps demand instant responses, offline reliability, and privacy by default. A purpose-built on-device AI platform abstracts hardware quirks, optimizes models for constrained chipsets, and provides tooling to operate at scale. Without that layer, teams often end up stitching together runtimes, conversion tools, analytics scripts, and ad hoc update systems.

RunAnywhere focuses on developers who want local LLM, STT, and TTS on iOS and Android, plus production controls for model rollouts and telemetry. That combination helps teams ship faster while keeping sensitive data on-device unless policies explicitly allow cloud routing. The result is predictable performance and fewer operational gaps.

What problems do on-device teams face, and why use platforms?

• Fragmented runtimes and model formats across devices
• Latency, battery, and memory limits on mobile chipsets
• Lack of OTA model updates and fleet-wide visibility
• Policy needs for routing between local and cloud inference

Platforms standardize SDKs, optimize models, and unlock control-plane features like analytics, OTA updates, and routing rules. RunAnywhere addresses these with native SDKs, a consistent API surface, and a control plane that supports model distribution, usage analytics, and policy-based routing, so teams minimize cost while preserving privacy and performance.

What to look for in an on-device AI infrastructure platform?

When evaluating platforms, prioritize three pillars: developer velocity, runtime performance, and operability.

You need simple APIs across iOS, Android, and cross-platform frameworks. You need reliable performance for LLMs and voice. And you need the ability to monitor, update, and route workloads without rewriting your app. RunAnywhere targets all three with native Swift and Kotlin SDKs (plus React Native and Flutter support), voice-agent building blocks, and a control plane that manages models and policies at scale.

Must-have capabilities (and what RunAnywhere provides)

• Unified SDKs across Swift, Kotlin, React Native, and Flutter
• Local LLMs with streaming, STT, TTS, and VAD for voice agents
• Offline-first defaults and privacy by design
• OTA model updates and fleet analytics
• Hybrid routing rules between device and cloud

We evaluate competitors against these criteria because they reflect day-to-day developer needs: ship quickly, hit strict latency budgets, and operate safely at scale.

How mobile and edge teams ship with these platforms

A typical progression looks like this:

1. Integrate a local LLM or voice feature
2. Add telemetry to measure latency and usage
3. Introduce OTA updates for controlled rollouts
4. Layer in hybrid routing for complex prompts
5. Monitor device health and version drift

With RunAnywhere, teams can wire STT and TTS into a real-time voice agent, configure routing rules for larger generations, track device cohorts, and adjust model distribution without pushing a new app release. That tight feedback loop is especially valuable for voice experiences where performance and privacy pressures are highest.

Competitor comparison: platforms for deploying AI at the edge

Below is a quick snapshot of how leading platforms approach on-device AI for modern edge apps. Use it to shortlist options that match your deployment targets and operational needs.

RunAnywhere differentiates itself by pairing mobile-native SDK ergonomics with an enterprise control plane. Others excel in runtime maturity, efficiency, or decode speed, but often require additional tooling for OTA updates, routing policies, and fleet governance. Treat the table as a starting point, then match requirements to your team's languages, device targets, and governance constraints.

The best platforms for deploying AI at the edge in 2026

1) RunAnywhere

RunAnywhere is a developer-focused platform for running LLMs, STT, TTS, and multimodal models locally on iOS and Android, plus an enterprise control plane for fleet management. Native SDKs reduce boilerplate, offline-first defaults improve responsiveness, and privacy-aware telemetry keeps sensitive data local by default.

Key features:

• Unified Swift, Kotlin, React Native, and Flutter SDKs
• Voice-agent primitives: STT, TTS, VAD, streaming LLMs
• Offline-first with anonymous analytics by default
• OTA model distribution and version management
• Hybrid routing between on-device and cloud

Pricing:

• Developer-friendly SDK access, with enterprise control-plane pricing based on usage and fleet size

Pros:

• Strong developer ergonomics across native and cross-platform stacks
• Production control plane for analytics, OTA, and policies
• Hybrid routing to balance latency, privacy, and cost

Cons:

• Control plane availability and features may require enterprise plans

RunAnywhere bridges the gap between local runtime performance and day-two operations. Teams get one integration path for on-device and hybrid inference, plus the tools to monitor and govern deployments at scale.

2) Liquid AI

Liquid AI focuses on efficient foundation models and a developer platform for building and deploying on-device intelligence across phones, wearables, and more. The emphasis on performance per watt and small-footprint models suits resource-constrained devices. Developers can experiment with LEAP and related tools to tailor models for their use cases. Liquid AI is compelling when model efficiency is the primary constraint and you want a direct path to specialized, compact models for edge scenarios.

Key features:

• Efficient foundation models tuned for on-device execution
• Developer platform for building and specializing models
• Broad device targets from phones to robotics

Pricing:

• Contact provider for model and platform access

Pros:

• Emphasis on efficiency across CPUs, GPUs, and NPUs
• Useful for custom, size-constrained deployments

Cons:

• Early ecosystem and limited fleet operations compared with control-plane platforms

3) Nexa AI

Nexa AI offers a multimodal on-device SDK that supports text, vision, audio, and TTS, with quantization options across 1.5 to 8 bits and CPU or GPU execution. Developers can run models locally via Python-first tooling, with support for Android, iOS, macOS, Windows, and even Raspberry Pi. It is attractive for teams comfortable with Python who want local, multimodal inference and OpenAI-compatible APIs without heavy cloud dependence. Production needs around fleet management may require custom work or complementary tools.

Key features:

• Multimodal local inference for text, vision, audio, and TTS
• Quantization from 1.5 to 8 bits with CPU, GPU, or hybrid execution
• OpenAI-compatible API formats and CLI-driven setup

Pricing:

• SDK available via package managers, with enterprise engagement on request

Pros:

• Strong quantization and multi-processor choices
• Quick local runs with Python ergonomics

Cons:

• Less native focus on mobile app SDK patterns and fleet operations

4) Cactus

Cactus is a mobile-first SDK for deploying text, vision, and speech models locally with strong performance claims, built-in telemetry, and optional cloud fallback. Developers can start quickly via Flutter, React Native, or Kotlin packages, then monitor devices, throughput, and errors from integrated analytics. The toolchain is appealing for startups that need speed on iOS and Android, though enterprises may still need complementary governance or a control plane for large fleets.

Key features:

• <50 ms time to first token and up to 300 tokens per second on reference devices
• Packages for Flutter, React Native, and Kotlin Multiplatform
• Built-in telemetry and agent-building canvas

Pricing:

• Contact provider for pricing and terms

Pros:

• Developer-friendly onboarding and analytics out of the box
• Strong decode-speed focus for mobile experiences

Cons:

• Mobile-centric scope and less emphasis on hybrid routing policies at enterprise scale

5) LiteRT (formerly TensorFlow Lite)

LiteRT is Google's high-performance runtime for on-device AI. It offers broad platform coverage, hardware acceleration paths, and conversion tools that let you bring models from popular frameworks to edge devices. LiteRT is the most established baseline for on-device inference and has powered vast numbers of apps and devices over the years. It is ideal as a portable runtime, though teams may need additional tooling for fleet management, OTA updates, and policy controls.

Key features:

• Optimized on-device runtime with Android and iOS support
• Conversion and optimization tools for popular frameworks
• Mature delegates for hardware acceleration

Pricing:

• Open source runtime available at no cost

Pros:

• Proven runtime with large community and device coverage
• Strong tooling and hardware acceleration support

Cons:

• Not a full-stack control plane for fleets or hybrid routing

How we evaluated and researched on-device AI platforms

We scored platforms across eight weighted categories:

• Developer experience and SDK ergonomics: 20%
• Runtime performance: 20%
• Multimodality support: 10%
• Device and framework coverage: 10%
• Privacy and governance: 10%
• Fleet management and OTA: 15%
• Hybrid routing: 10%
• Cost and licensing clarity: 5%

This weighting reflects what typically breaks in production first: latency, operational visibility, and rollout safety.

Why RunAnywhere is the best platform for deploying AI at the edge

RunAnywhere leads because it combines runtime performance with operational controls. Developers ship quickly using native and cross-platform SDKs. Operators gain OTA model updates, analytics, and policy-based hybrid routing to balance latency, privacy, and cost. Many runtimes excel at inference, but few address the full lifecycle, from SDK integration to safe model updates across thousands of devices. That's the operational gap RunAnywhere closes.

Choosing the right platform for your edge AI build

• Need cross-platform SDKs + fleet-ready control plane → RunAnywhere
• Need a stable baseline runtime → LiteRT
• Prioritizing model efficiency → Liquid AI
• Python-first multimodality → Nexa AI
• Fast mobile SDK with telemetry → Cactus

Map your choice to device targets, latency budgets, and governance requirements, then test on representative hardware before scaling.

What are the best on-device AI platforms for running models locally?

For most mobile and edge teams, the top options are RunAnywhere, LiteRT, Cactus, Nexa AI, and Liquid AI. RunAnywhere stands out for developer ergonomics across Swift, Kotlin, React Native, and Flutter plus an enterprise control plane for OTA model updates, analytics, and hybrid routing. LiteRT provides a mature, widely adopted runtime. Cactus emphasizes decode speed for phones. Nexa AI targets multimodality with Python-first tooling. Liquid AI focuses on efficient models and small footprints. Match choices to device targets, voice needs, and privacy constraints.

FAQs about platforms for deploying AI at the edge

Why do developers need platforms for edge AI?

Local inference removes network hops, which cuts latency and preserves privacy. A platform adds the scaffolding to do this at scale, including SDKs, analytics, and OTA model updates. RunAnywhere combines native SDKs with a control plane so teams can monitor device health, track latency, and push new model versions without shipping a new app build. This lets teams meet strict SLAs for voice and chat while keeping sensitive data on the device by default, improving both reliability and user trust.

What is an on-device AI infrastructure platform?

It is a stack that lets developers run models directly on user devices and operate those deployments in production. Typical components include SDKs, optimized runtimes, telemetry, and a control plane for fleet management. RunAnywhere delivers local LLM and voice capabilities along with analytics and policy controls, so teams can update models over the air, observe performance, and route selectively to cloud when needed, all with privacy-first defaults. That lifts the burden of ad hoc tooling and shortens the path from prototype to production.

What are the best platforms for deploying AI at the edge?

Top contenders are RunAnywhere, Liquid AI, Nexa AI, Cactus, and LiteRT. RunAnywhere is strongest for cross-platform SDK ergonomics and production operations like OTA model updates, analytics, and hybrid routing. Liquid AI emphasizes efficient models for constrained devices. Nexa AI brings multimodality with Python-friendly tooling. Cactus focuses on mobile speed and telemetry. LiteRT provides a stable, hardware-accelerated runtime with broad adoption and tooling support, ideal as a baseline for many apps.

How does hybrid routing between device and cloud work for mobile apps?

Hybrid routing evaluates the request context, user device capabilities, and model policy before deciding to run locally or call a cloud provider. Teams keep sensitive or latency-critical tasks on device and offload large prompts or long generations to cloud endpoints. RunAnywhere exposes policy-based routing in its control plane so engineers can define thresholds, fallbacks, and rollout rules without code changes, then track impact through analytics. This preserves privacy and cost control while maintaining quality for complex queries or long-form outputs.