Text-to-speech (TTS) software transforms written text into audio using neural voice synthesis, but real-world deployment requires careful consideration of performance, cost, and compliance. While demos showcase TTS capabilities under ideal conditions, production environments demand systems that can handle variable traffic, maintain accuracy, and adhere to regulatory standards. A TTS pipeline involves stages like text normalization and neural synthesis, each affecting reliability. Challenges such as concurrency constraints, latency issues, and input validation failures can arise during production use. Architectural decisions between streaming and batch processing, as well as deployment models, influence system performance and cost management. Streaming is essential for real-time interactions due to its responsiveness, whereas batch processing prioritizes accuracy for structured documents. The choice of TTS model, whether for enterprise or entertainment, also impacts clarity and expressiveness. Evaluating TTS systems involves performance testing under real conditions, analyzing cost structures, and ensuring compliance with data handling requirements, which are crucial for sustaining accuracy and stability in enterprise applications.