Performance Without Compromise: Deepgram’s Reliability Playbook

Deepgram has been a leader in Voice AI for nearly a decade now. Our research teams have been curating data and training models tuned to the cadence of human speech and conversation for years. When we train and evaluate a model, it's thrilling to have that first "Aha!” moment, where you feed in garbled, static-infused speech; and the transcription on the other end is perfect.

However, that first Aha!-moment often happens on a training checkpoint of a model, with input audio from a researcher's desktop. At that point, there are a lot of steps remaining to evolve that model into a world-class voice model, we can offer to the world. Productionizing state of the art models and serving it at the scale required by enterprise customers is part of Deepgram's core DNA.

Enterprises are weaving Voice AI into their core workflows, and those interactions need to work every time—not just once in a demo, but thousands of times a day. In an era where cloud provider outages can bring down entire sections of the internet, enterprises need their tech stack to be resilient to these outages.

Let’s dive in to see how Deepgram’s foundation audio models solve real-world problems and the journey it takes to go from a research checkpoint to a globally available model serving millions of customers.

Our researchers work with and build our own cutting-edge technology to train our models. The stack is Python-heavy: using PyTorch to deploy large, multi-week training runs on our public and private compute clusters. That stack is optimized for experimentation and flexibility, and regularly yields large breakthroughs in our frontier models and research explorations. It was also designed to drive steady progress on the "long tail" of models, such as consistent, gradual accuracy improvements on our ever expanding supported languages.

Once a research team has trained and benchmarked and found something great to deliver, the value of experimentation and flexibility drops, and the value of reliability and performance dominates the value returned on the expended effort. This was the genesis of the Deepgram Enterprise Runtime. It optimizes for efficiency, providing the highest accuracy & latency at the lowest cost. Enterprise customers love the flexible deployment options including multi-tenant hosted service, self-hosted in customer environments or a Deepgram Dedicated configuration for maximum throughput and performance. Our core runtime is built with Rust, which is blazing fast and ensures we are always pushing inference work through available GPU cycles and makes sure customers aren’t bottle-necked on CPU throughput. The runtime seamlessly handles model swapping and provides observability hooks so customers can monitor performance as scale. This provides the foundation for serving millions of requests with consistent performance.

As mentioned, the Deepgram Enterprise Runtime is the core engine behind our products. For our hosted offering, Deepgram provisions and manages the underlying hardware it runs on. Intimate control of the hardware is almost as important as control of the software to reach the maximum performance and reliability levels our customers require.

Running in the cloud often means making tradeoffs around control of the raw hardware and system configuration. That affects your ability to make optimizations to the hardware and firmware. This means you've ceded control of reliability and performance to the cloud provider, and in-turn are paying some sort of premium for a managed service that takes on that responsibility.

At Deepgram, we care about more than just the application health. To deliver the absolute best performance and price for realtime Voice AI, we have to care about the entire stack end-to-end, including the hardware. That means we've made an intentional choice to retain control over the hardware by running Deepgram's managed services in Deepgram’s own datacenters. We have a number of our own datacenters where we rack our own GPUs, CPUs, and manage our own network. Infrastructure specialists and Site Reliability Engineers (SRE) at Deepgram are the fuel that makes those datacenters run smoothly 24/7/365.

This experience isn't new for us either; we've run data-centers for a long time now. Especially in the early years, that meant a lot of hiccups! Take one of our SREs out for a drink and they'll tell you war stories all night long 😄. Those experiences are hard-earned lessons on how to run an AI inference stack, and we wouldn't trade that for the world. We know how to build infra to account for ISP failures, GPUs failing sporadically, power supply issues, unbalanced capacity between datacenters, and more.

In 2025, that means when you build with Deepgram, you get a rock-solid experience, ready to scale from small startup to Fortune 100 business. Many AI companies are so focused on growth that they push all their compute into the cloud. Those company's processes are at the whims of their cloud provider. Look at the headlines for any cloud provider over the past 12 months - they have incredible global reach and reliability, but they are not infallible and outages continue to happen. They also have a wide variety of products to meet varied compute needs for the entire enterprise market but lack the specialization needed for the nuance of voice applications.

In contrast, Deepgram deeply invests in and continually optimizes the utilization of compute for AI inference workloads. This specialization allows us to provide the best performance and reliability experience in real-time AI.

The results speak for themselves. Deepgram's hosted API is:

• Fast: Independent benchmarks like Coval confirm it - Deepgram has the lowest latency managed voice AI services in the game.
• Reliable: Deepgram's speech-to-text, text-to-speech, and Voice Agent APIs all have at least 99.99% uptime (4 nines of reliability).
• Scalable: Deepgram is the big name in the Voice AI space. We process multiple days worth of audio every second, more than even traditional incumbents like Google.

Now, to be clear, Deepgram also leverages the cloud aggressively, and we have deep partnerships with several well-known cloud providers. They are an important part of our infrastructure strategy, and fulfill an important role helping us meet spikes in demand, global deployments in regions where we don't have a physical presence yet, and for customers who want single-tenant deployments in the same cloud region and availability zone as their other workloads. Cloud deployments are one pillar of our overall infrastructure strategy; it just can’t be the only pillar.

When you build with voice AI, it’s inherently deployed at the edge of your business. You might be building a next-gen call center, creating a production voice agent, or transcribing a doctor-patient interaction. In each scenario, voice AI is built directly on top of human dialogue, which means voice AI is deeply embedded in our human relationships. To earn and keep that trust, your voice AI must be reliable, consistent, and accurate.

Deepgram has built our entire infrastructure stack to help you deliver on that promise, engineered for performance, not compromise. Try it out yourself! Once you’ve seen the difference, let’s talk about how to bring your voice products to life and experience enterprise-grade AI at its best, so your voice applications perform flawlessly, every time.