There’s no universal “better” choice, but here are the main practical reasons why an ESP32-S3 is often a better fit for embedded ML/IoT than a Raspberry Pi Zero 2 W:
Power consumption ESP32-S3: Tens of mA when active, µA in deep sleep. Designed for battery/low-power IoT nodes. RPi Zero 2 W: ~250–350 mA idle, much higher under load. Not practical for battery operation without a big pack. For continuous sensor logging and periodic inference, S3 is far more efficient.
Real-time behavior ESP32-S3 runs bare-metal / RTOS (FreeRTOS). You can sample sensors deterministically at 10–1000 Hz. Pi Zero 2 W runs Linux. Great for flexibility, but not hard real-time → jitter in sensor sampling. For vibration/RPM sensing, deterministic timing is critical.
Integrated connectivity and peripherals ESP32-S3: Wi-Fi, BLE, ADC, SPI, I²C, UART, I²S, CAN built-in. Pi Zero 2 W: Wi-Fi/Bluetooth, but raw sensor I/O needs extra hardware (USB dongles, HATs). With S3 you connect sensors directly, without kernel driver overhead.
Built-in AI acceleration ESP32-S3 has SIMD + ESP-NN kernels (optimized TFLite Micro ops). Run small quantized ML models in ms range. Pi Zero 2 W can run full TensorFlow/PyTorch, but inference overhead is much bigger and not power-efficient. If your model is small (RUL classifier, anomaly detection), S3 handles it natively.
Cost and availability ESP32-S3 modules (N8R8/N16R8): $4–8 range. Pi Zero 2 W: Often hard to find at retail, higher cost (~$15–20 if available).
Simplicity and reliability ESP32-S3 firmware: Single binary, OTA updates possible, boots instantly. Pi: Full OS image, needs SD card, filesystem can corrupt if power is lost. For field-deployed IoT nodes, microcontrollers are usually more robust.
When Raspberry Pi Zero 2 W makes more sense
Summary: Choose ESP32-S3 if you want low-power, real-time, robust edge ML inference with direct sensor IO. Choose Pi Zero 2 W if you need a general-purpose Linux box with heavier ML frameworks or more flexible software stack.