I Tested 5 Cloud NLP APIs on the Same 1,000 Sentences — Here's What the Numbers Say

I needed to add sentiment analysis to a side project last year. Like most developers, I hit the classic question: build or buy? The "buy" side looked obvious at first. AWS Comprehend, Google Natural Language API, Azure Text Analytics — serious products backed by massive R&D. HuggingFace's Inference API offered open-source models without the infrastructure headache. And if I wanted free, there was always textstat and similar Python libraries. But which one actually performs? And at what cost? I couldn't find a comparison that used the same dataset across all five, so I built one. Here's what I found. The Setup I assembled a dataset of 1,000 sentences pulled from three sources: 400 product reviews (mixed positive/negative/neutral) 300 news headlines (objective tone) 300 social media posts (informal, sarcastic, mixed) Each sentence was hand-labeled by me with ground-truth sentiment (positive / negative / neutral). This matters — most benchmarks use datasets the APIs were trained on. I wan