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Get startedCactus vs ONNX Runtime: Hybrid AI Engine vs Universal Model Format
ONNX Runtime is Microsoft's high-performance inference engine for the universal ONNX model format, supporting the broadest range of execution providers. Cactus is a hybrid AI engine focused on LLMs, transcription, and vision with automatic cloud fallback. ONNX Runtime excels at model portability; Cactus excels at mobile AI with quality guarantees.
Cactus
Cactus is a hybrid AI inference engine that runs LLMs, transcription, vision, and embeddings on-device with automatic cloud fallback. It provides sub-120ms latency, cross-platform SDKs for Swift, Kotlin, Flutter, React Native, Python, C++, and Rust, and NPU acceleration on Apple devices.
ONNX Runtime
ONNX Runtime is Microsoft's production inference engine for ONNX-format models. It supports the broadest range of execution providers including CUDA, DirectML, CoreML, NNAPI, TensorRT, and OpenVINO. ONNX Runtime runs on every major platform including Windows, with ONNX Runtime Mobile optimized for phones and edge devices.
Feature comparison
Performance & Latency
ONNX Runtime is highly optimized for ONNX model inference with execution providers tuned for each hardware target. Its graph optimizations and operator fusion are mature. Cactus achieves sub-120ms latency with zero-copy memory mapping and INT4/INT8 quantization. ONNX Runtime's execution provider diversity gives it an edge on Windows and server hardware.
Model Support
ONNX Runtime supports any model convertible to ONNX format, which covers virtually every ML framework. However, it requires a conversion step and its LLM-specific optimizations are less mature than dedicated frameworks. Cactus natively supports leading LLMs, transcription models, and vision models without format conversion.
Platform Coverage
ONNX Runtime covers iOS, Android, macOS, Linux, Windows, and web via ONNX Runtime Web. Its Windows and server support is particularly strong given Microsoft's backing. Cactus covers iOS, Android, macOS, Linux, watchOS, and tvOS with broader mobile framework SDKs. ONNX Runtime has a Windows advantage; Cactus has a mobile SDK advantage.
Pricing & Licensing
ONNX Runtime is MIT licensed by Microsoft and completely free. Cactus is also MIT licensed with an optional cloud API. Both are open source with identical license terms. ONNX Runtime benefits from Microsoft's enterprise support channels for Azure customers.
Developer Experience
ONNX Runtime requires converting models to ONNX format, which adds a workflow step but enables using models from any framework. Cactus provides direct model loading without conversion. ONNX Runtime's API is lower-level; Cactus offers higher-level SDKs designed for app developers. For ML engineers, ONNX Runtime's flexibility is powerful. For app developers, Cactus is more approachable.
Strengths & limitations
Cactus
Strengths
- Hybrid routing automatically falls back to cloud when on-device confidence is low
- Single unified API across LLM, transcription, vision, and embeddings
- Sub-120ms on-device latency with zero-copy memory mapping
- Cross-platform SDKs for Swift, Kotlin, Flutter, React Native, Python, C++, and Rust
- NPU acceleration on Apple devices for significantly faster inference
- Up to 5x cost savings on hybrid inference compared to cloud-only
Limitations
- Newer project compared to established frameworks like TensorFlow Lite
- Qualcomm and MediaTek NPU support still in development
- Cloud fallback requires API key configuration
ONNX Runtime
Strengths
- Universal ONNX format supported by all major ML frameworks
- Broadest execution provider ecosystem (CUDA, DirectML, CoreML, etc.)
- Strong Microsoft backing and Windows optimization
- Excellent model portability across platforms
Limitations
- Requires ONNX model conversion step
- No hybrid cloud routing
- No built-in function calling or tool use
- Mobile runtime is heavier than purpose-built solutions
- LLM-specific optimizations lag behind dedicated frameworks
The Verdict
Choose ONNX Runtime if you need the broadest hardware support including Windows and server environments, want a universal model format, or are deploying models from multiple ML frameworks. Choose Cactus if you are building mobile-first AI features, need hybrid cloud routing, or want purpose-built LLM and transcription support with native mobile SDKs. ONNX Runtime is the generalist; Cactus is the mobile AI specialist.
Frequently asked questions
Do I need to convert models to use ONNX Runtime?+
Yes. ONNX Runtime requires models in ONNX format. Tools like onnxmltools, torch.onnx, and tf2onnx handle conversion from major frameworks. Cactus loads models directly without a format conversion step.
Which is better for Windows deployment?+
ONNX Runtime is significantly better on Windows with DirectML, CUDA, and TensorRT execution providers. Cactus does not currently target Windows. For Windows deployments, ONNX Runtime is the clear choice.
Does ONNX Runtime support hybrid cloud routing?+
No. ONNX Runtime is purely a local inference engine. Cactus provides confidence-based automatic cloud fallback. ONNX Runtime can be used in cloud environments but does not have built-in on-device-to-cloud routing.
Can ONNX Runtime run LLMs efficiently?+
ONNX Runtime supports LLM inference but its LLM-specific optimizations are less mature than dedicated frameworks like Cactus or llama.cpp. Microsoft has been improving LLM performance with ONNX Runtime GenAI extensions.
Which has better mobile SDKs?+
Cactus provides native SDKs for Swift, Kotlin, Flutter, and React Native. ONNX Runtime Mobile offers a C API with Java/Kotlin bindings for Android and a C API for iOS. Cactus has more polished mobile developer experience.
Is ONNX a universal model format?+
ONNX (Open Neural Network Exchange) is the closest thing to a universal ML model format, supported by PyTorch, TensorFlow, scikit-learn, and most ML frameworks. This portability is ONNX Runtime's biggest advantage.
Try Cactus today
On-device AI inference with automatic cloud fallback. One unified API for LLMs, transcription, vision, and embeddings across every platform.