BaseRT vs Decode: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of BaseRT and Decode — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
BaseRT
Base Compute
BaseRT is a high-performance LLM runtime for Apple Silicon that runs open-source models locally, faster than llama.cpp and MLX.
Key features
- Apple Silicon Optimized Runtime: A native inference engine tuned for M-series chips that outperforms llama.cpp and MLX on decode and prefill benchmarks.
- One-Line Install: BaseRT ships as a single curl-piped install script, so users can go from download to serving a model in seconds.
- Broad Open-Source Model Support: Runs Qwen3, Llama 3.1/3.2, Gemma 3/4, Mistral, Phi-3, and Nomic BERT out of the box, with quantized (Q4/Q8) weights.
- Local Serving for Coding Agents: `basert serve <model>` exposes a local endpoint that pairs with the pi plugin so coding agents run fully on-device with no API keys.
- Privacy by Default: All inference happens on the user's machine, so prompts, code, and outputs never leave the device.
- Benchmark-Driven Performance: Publishes tokens/sec comparisons on Apple M5 Pro against MLX and llama.cpp for reproducibility.
Best for
- On-Device Coding Assistant: Engineers pair BaseRT with a local coding agent to get autocomplete and refactoring without sending source code to a cloud API.
- Private Model Evaluation: ML practitioners benchmark open-source models on their own laptop without renting GPUs or exposing test data.
- Offline LLM Applications: Developers ship desktop apps that call a locally served model, avoiding rate limits and per-token costs.
- Prototyping on Apple Silicon: Researchers experiment with new quantizations and open-weight models on M-series Macs at high throughput.
- Enterprise On-Prem Inference: Teams with data-residency constraints run production inference on employee devices instead of external APIs.
Decode
Entropik Technologies
A human insights platform that uses emotion AI, webcam eye tracking, and predictive models to test creative, products, and experiences before launch.
Key features
- Emotion AI Measurement: Face emotion, voice emotion, and text sentiment analysis reveal how respondents actually feel during a study rather than only what they report in an answer.
- Webcam Eye Gaze Tracking: Real eye tracking runs through a participant's own webcam with zero hardware, producing attention heatmaps that show where people look first and what they miss.
- AI Creative Insights: Neuro AI predicts attention, emotional resonance, brand recall, and conversion impact for ad creative, packaging, OOH, and web layouts before media spend is committed.
- Synthetic Audience: Build reusable synthetic personas and compare how each creative performs persona by persona ahead of fielding a study with real respondents.
- AI Moderator: Runs moderated and unmoderated interviews at scale, then extracts themes, emotions, and supporting evidence from raw interview and video feedback automatically.
- Shopper and Shelf Simulation: Simulates real-world shelf and pack testing with attention heatmaps, shelf visibility analysis, planogram optimization, and purchase-intent prediction.
- UX Research Suite: Prototype testing, unmoderated task studies, usability and wireframe testing, card and tree sorting, and live website and app testing, each enriched with gaze and emotion data.
- Global Respondent Panel: Access to more than 103 million respondents worldwide, or bring your own panel free of charge on any plan.
Best for
- Pre-Flight Ad Testing: Comparing creative variations and messaging options to predict which version earns attention and recall before buying media.
- Packaging and Shelf Decisions: Testing pack designs and planograms in a simulated retail environment to forecast visibility and purchase intent.
- Product Concept Validation: Screening product concepts, storyboards, and innovation ideas for early-stage market fit before committing development resources.
- UX Friction Discovery: Running prototype and usability studies where webcam eye tracking and emotion signals expose confusion users cannot articulate.
- Qualitative Research at Scale: Using the AI Moderator to conduct and synthesize many interviews into structured themes instead of manual transcript coding.
- Brand Tracking and Price Testing: Running recurring consumer studies on brand perception, pricing, and the customer journey across multiple markets.