Microsoft releases MAI-Transcribe-2 at US$0.10 per hour
Sub-US$0.10-per-hour transcription changes the economics of audio-heavy products, making always-on meeting capture and full-archive indexing viable at scale. Teams building voice agents, call analytics or accessibility features should benchmark it against Whisper and Google Speech-to-Text on their own audio before committing. Pricing is listed in US dollars, so ASEAN buyers should check regional Azure pricing and data-residency options in Microsoft Foundry.
Microsoft announced that MAI-Transcribe-2, described by the company as the best transcription model in the world, is now generally available in Microsoft Foundry for production use cases. The model is priced at US$0.10 per hour of audio, a rate that undercuts most commercial speech-to-text offerings and makes bulk transcription of meetings, calls and media archives affordable.
The release moves the model from preview into a production-ready endpoint inside Microsoft Foundry, Microsoft's platform for building and deploying AI applications. Developers can call it directly for real-world workloads such as call-centre analytics, captioning, meeting notes and audio search, without additional approval steps.
Aggressive per-hour pricing signals that Microsoft is treating speech-to-text as a volume business, putting pressure on competitors like OpenAI's Whisper API and Google Cloud Speech-to-Text on cost. For enterprises processing thousands of hours of audio monthly, the price difference compounds into meaningful savings.
MAI-Transcribe-2, the best transcription model in the world, is now generally available in Microsoft Foundry for production use cases! And it’s currently priced at just $0.10 per hour of audio.
Come build with it!
This report is categorized under AITOP's "Products" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include MAI-Transcribe-2, Microsoft Foundry, speech recognition, Microsoft.
Source attribution: curated from Microsoft AI (@MicrosoftAI). Cross-source corroboration: 6 independent sources have covered this development (including Mustafa Suleyman, arXiv: OpenAI, AWS Machine Learning Blog), confirming industry-wide signal strength.
