It offers a smart routing engine to balance performance, privacy, and cost, and tools like SDKs for Apple platforms (with upcoming support for Android) to simplify integration. Users can deploy AI capabilities—such as summarization, classification, general chat, and custom use cases—without relying on cloud offloading, which reduces dependencies on network connectivity and protects user data.
- Mobile & Desktop App Developers: Building applications that need fast, local AI inference (chat, classification, summarization).
- Privacy-Focused Companies: Businesses that require user data to stay on device, avoiding cloud exposure.
- Startups & Product Teams: Those who want reduce costs and dependency associated with cloud inference.
- Organizations with Offline Needs: Use cases where connectivity is limited or inconsistent.
- AI Experimenters & Researchers: People testing or deploying smaller-parameter models for prototyping or edge usage.
How to Use It:
- Choose a Model: Pick from the TryMirai models library sized for your performance and cost trade-offs.
- Integrate SDK: Use the platform’s SDK (e.g. for Apple) to embed the inference engine into your app.
- Configure Routing: Set up smart routing to optimize privacy, performance, and cost depending on user context.
- Run Inference On Device: Execute tasks like summarization, chat, classification locally without cloud.
- Monitor & Optimize: Use tools to measure latency, model performance, adjust model selection, or update models.
- On-Device Only Operation: No need to send user data to cloud for inference—improves privacy and reliability.
- Zero Inference Costs Model: Once integrated, inference happens locally so ongoing usage costs are low.
- Flexible Model Sizes: Offers a range of models so you can pick light or large depending on performance needs.
- Smart Routing Engine: Allows control over trade-offs of speed, power consumption, and resource usage.
- Fast Integration: SDKs and tools designed to make embedding AI into apps quick, especially for Apple platforms.
- Strong privacy guarantees with on-device processing.
- No need for constant internet connection for inference tasks.
- Broad Flexibility: model size choice and smart routing.
- Lower operational cost since cloud inference costs are minimized.
- Suitable for performance-sensitive apps (mobile, desktop) and edge use.
- Some features (Android SDK, cloud inference) are listed as “coming soon” / not yet available.
- Larger models may still demand significant device resources (battery, CPU/GPU).
- Limited public detail around pricing beyond free tier.
- Potential complexity in choosing optimal model/routing configuration.
- Less suited for use cases that require very large models or complex cloud-only architectures.
Free
Free
- per active device per month
- For individuals and early-stage teams
- Up to 10 000 devices
- Open-source (MIT License)
Pro
$ 0.10
- per active device per month
- For growing apps ready to scale
- Over 10 000 devices
- Standard terms
Enterprise
Custom
- For high-scale partners
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Reviews
Rating Distribution
Average score
Popular Mention
FAQs
An infrastructure platform that enables on-device AI inference, giving apps local AI capabilities with high performance and privacy.
No—TryMirai is built to run inference on device to avoid cloud dependency.
Common use cases include summarization, classification, general chat, and custom-defined workflows.
Yes—since inference happens locally, data does not need to be sent to third-party servers, improving privacy.
Yes, TryMirai offers multiple model sizes (e.g. 0.3B up to 7B parameters) to match performance and resource needs.
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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