Digest: Rime TTS Integration on Together AI
Coverage of together-blog
In a recent blog post, Together AI announced it is expanding its multimodal infrastructure by adding enterprise-proven Rime Text-to-Speech models, enabling co-location with LLMs for lower-latency voice agents.
In a recent update, the team at Together AI announced the integration of Rime's Text-to-Speech (TTS) models into their platform. This development marks a specific effort to consolidate the infrastructure required for building conversational AI, moving beyond standalone Large Language Model (LLM) inference to support the full audio-text-audio loop.
Context: The Latency Challenge in Voice AI
As the industry shifts focus from text-based interfaces to real-time voice agents, infrastructure fragmentation has emerged as a primary bottleneck. A typical conversational stack involves three distinct stages: Speech-to-Text (transcription), LLM processing (reasoning), and Text-to-Speech (synthesis). When these components are hosted by different providers or in disparate geographic regions, the cumulative network latency can degrade the user experience. A delay of even a few hundred milliseconds can make a digital agent feel sluggish or robotic. Consequently, the ability to co-locate these services-running the "ear," the "brain," and the "mouth" of the AI on the same cluster-is becoming a critical architectural requirement for enterprise-grade applications.
The Gist: Co-located, Proven Synthesis
Together AI's post highlights the addition of two specific Rime TTS models designed for enterprise use. The announcement emphasizes that these are not experimental research previews; rather, they are production-ready systems that have already processed billions of calls. This metric serves as a proxy for reliability and stability, addressing a common concern regarding the robustness of newer generative audio models.
Perhaps most significantly for systems architects, these models are available on Together AI's dedicated infrastructure. This allows developers to deploy Rime's TTS capabilities alongside existing LLM and Speech-to-Text endpoints within the same environment. By unifying the compute stack, organizations can theoretically minimize network overhead and optimize the end-to-end latency of their voice applications.
While open-weights models offer flexibility, enterprise-grade proprietary models like Rime's often come with different performance guarantees regarding throughput and consistency. The integration suggests that Together AI is positioning itself not just as a hub for open-source models, but as a comprehensive serving layer for high-performance proprietary tools as well.
Why This Matters
For developers, this integration simplifies the vendor landscape. Instead of managing API keys and network security across a transcription provider, an inference provider, and a synthesis provider, the stack can be centralized. For the broader market, it signals that infrastructure providers are rapidly evolving into holistic platforms that support the entire lifecycle of a multimodal interaction, rather than just the text generation phase.
We recommend reading the original post to understand the specific model variants available and how the dedicated infrastructure pricing and setup function.
Read the full post on Together AI
Key Takeaways
- Two enterprise-grade Rime TTS models are now live on Together AI.
- The models are verified at scale, having handled billions of calls.
- Developers can co-locate TTS, LLM, and STT on dedicated infrastructure to reduce latency.
- The integration supports the consolidation of multimodal AI stacks for real-time applications.