The growing demand for secure healthcare services in medical IoT networks has raised the need for robust anomaly detection systems, with autoencoder (AE) as a compelling solution. Although mapping of neural networks on FPGAs is well-known, selecting a suitable High-Level Synthesis (HLS) framework for a particular use case remains a challenge. This work explores the trade-offs between two well-known, open-source HLS frameworks: hls4ml and FINN by evaluating non-functional metrics for deploying AEs. Our results highlight that hls4ml achieves roughly \(10\times \) lower latency and \(3\times \) higher throughput as compared to FINN. On the other hand, FINN provides a comprehensive, automated end-to-end solution for deploying quantized networks on FPGA with up to 80% fewer lookup tables (LUTs) and flip-flops (FFs), but with limited control over the design process. Indicating that FINN is suitable for low-power deployments where faster time-to-deployment is required, whereas hls4ml excels in latency-critical, high bandwidth applications, our study sheds light on the choice of HLS tools.