Deepgram and AWS: Making Enterprise Voice AI a Native Cloud Service

LAS VEGAS, NV – November 30, 2025 – For years, deploying sophisticated, real-time voice artificial intelligence has been a heavy lift for enterprises, often requiring complex custom infrastructure, delicate data pipeline orchestration, and a constant battle against latency. This week, as the tech world converges on Las Vegas for AWS re:Invent, that barrier to entry has been significantly lowered. Voice AI specialist Deepgram announced a native integration with Amazon SageMaker, effectively transforming its high-performance voice models into a turnkey service within the AWS ecosystem.

The collaboration makes Deepgram’s streaming speech-to-text (STT), text-to-speech (TTS), and advanced Voice Agent APIs available as native real-time endpoints in SageMaker. For the vast number of businesses built on AWS, this means the ability to build and deploy enterprise-grade voice applications is no longer a separate, complex project but a streamlined extension of their existing cloud workflow.

Lowering the Barrier to Real-Time Voice Innovation

The core business impact of this integration lies in its radical simplification of the development process. Previously, building a real-time voice application, such as a conversational AI agent for a contact center or a live transcription service for a trading floor, involved significant engineering effort. Developers had to manage WebSocket connections, orchestrate data flow between their application and the AI model, and build custom infrastructure to handle the streaming audio—all while trying to keep latency low enough for a natural conversation.

Deepgram's integration directly addresses this pain point. By leveraging a new bidirectional streaming capability in Amazon SageMaker Inference, developers can now use a single API call (InvokeEndpointWithResponseStream) to send a continuous stream of audio and receive a stream of transcripts back. This eliminates the need for what Deepgram CEO Scott Stephenson calls "workarounds or hoops to jump through." In a statement, he emphasized the goal: "Our native integration with Amazon SageMaker removes the complexity from deploying real-time voice capabilities, allowing AWS customers to focus on innovation rather than infrastructure."

This shift is more than a convenience; it's a strategic accelerator. By offloading the infrastructural burden to the managed SageMaker environment, development teams can shorten their time-to-market for new voice-powered features and services. The resources once spent on "plumbing" can now be reallocated to refining the user experience, improving agent logic, and creating tangible business value. For companies competing on customer experience or operational efficiency, this newfound agility is a significant competitive advantage.

Redefining Performance and Security Standards

While ease of use is a major draw, the integration would be meaningless without enterprise-grade performance and security. Deepgram has built its reputation on speed and accuracy, claiming sub-300ms latency for its latest Nova-3 models. This level of responsiveness is critical for applications where conversational flow cannot be broken, from AI receptionists handling customer calls to live analytics tools monitoring broadcast media. Independent benchmarks have often cited Deepgram's leadership in speed-versus-price performance compared to giants like Google and open-source alternatives like OpenAI's Whisper, particularly in streaming scenarios.

However, for many large enterprises, especially those in regulated industries, performance is secondary to security and compliance. This is where the native AWS integration becomes a game-changer. The solution allows customers to deploy Deepgram's models directly within their own Amazon Virtual Private Cloud (VPC). This means that sensitive voice data—whether it's a patient discussing medical history with a clinic's AI assistant or a client sharing financial details—never has to leave the secure, compliant perimeter of the customer's own AWS environment.

This capability directly addresses stringent data residency and regulatory requirements like HIPAA for healthcare and GDPR for data privacy. For a Chief Information Security Officer (CISO), this architecture provides peace of mind, making it far easier to approve a voice AI project. A Fortune 20 healthcare company is already leveraging Deepgram on AWS to modernize its contact center, a move made possible by the platform's ability to handle sensitive data securely. By baking security and compliance into the deployment model, Deepgram and AWS are unlocking voice AI for the most risk-averse, high-value enterprise sectors.

The Strategic Alliance Powering Enterprise Adoption

This launch is not merely a technical handshake; it's the product of a deepening strategic alliance between Deepgram and AWS. Deepgram recently achieved the AWS Generative AI Competency, a coveted designation that AWS awards to partners who demonstrate deep technical proficiency and proven customer success. This isn't just a badge; it's a rigorous vetting process that assures customers that the solution is tested, secure, and production-ready on AWS.

Furthermore, the companies have inked a multi-year Strategic Collaboration Agreement (SCA). Such agreements are reserved for key partners and signal a long-term commitment to joint go-to-market strategies, deeper technical integrations, and co-investment in developing new capabilities for enterprise customers. For businesses, this translates into tangible benefits: simplified procurement through the AWS Marketplace with unified billing, access to AWS credits to offset costs, and the confidence that both companies are invested in the long-term success of the integration.

Ankur Mehrotra, General Manager for Amazon SageMaker at AWS, highlighted the mutual benefit, stating that the integration "helps developers accelerate innovation while maintaining data security and compliance on AWS." This partnership effectively positions Deepgram as a premier voice AI solution within the world's largest cloud ecosystem, giving it unparalleled access to a massive market of enterprises looking to harness the power of generative AI.

From Contact Centers to Clinical Notes: Expanding the Use Cases

While high-volume contact centers and fast-paced trading floors are marquee use cases, the true impact of this simplified access to real-time voice AI will be felt across a much broader spectrum of business functions. The combination of performance, security, and ease of deployment opens the door for innovation in numerous areas.

In healthcare, for example, Deepgram's HIPAA-compliant platform and specialized Nova-3 Medical model can power digital scribes that accurately transcribe complex clinical terminology during patient visits, freeing physicians from administrative burdens. In customer service, companies like Abby Connect are already using the technology to build "AI Receptionist" products that can handle scheduling, answer FAQs, and triage calls, allowing human agents to focus on more complex, high-value interactions.

The technology is also transforming productivity tools. The meeting assistant UpdateAI uses Deepgram's fast transcription to identify and summarize action items from customer calls, improving follow-through and efficiency for sales and success teams. In finance, beyond the trading floor, lenders are using voice analytics to analyze calls, identify performance gaps in their agent scripts, and improve successful loan closure rates.

By making its state-of-the-art models available as a simple SageMaker endpoint, Deepgram is positioning real-time voice AI not as a futuristic concept, but as a practical, accessible utility. It’s a tool that any developer on AWS can now pick up and integrate, enabling a new wave of applications that will transform how businesses engage with customers, analyze conversations, and unlock value from the spoken word.