The Team Behind Deploy: Shipping AI, the DigitalOcean Way

Deploy 2026 came and went, and we’re still buzzing. For one day at Convene 100 Stockton in San Francisco, developers, startup founders, customers, and partners filled the room to talk about a shared challenge: how to build and scale AI products without unnecessary complexity. Conversations moved from infrastructure to inference costs, production workloads, vector databases, and what teams actually need to get AI applications from prototype to production. We’re grateful to everyone who showed up and made it what it was.

DigitalOcean took the covers off the AI-Native Cloud, a five-layer stack purpose-built for AI-native companies, with more than 15 product launches in a single keynote. That included Inference Router, Dedicated Inference, Managed Weaviate, Knowledge Bases, expanded GPU and model capabilities, and a new Kansas City data center with liquid-cooled B300s. The event had seven sponsors, including NVIDIA, AMD, Weaviate, OpenRouter, MongoDB, and others. Customers like Hippocratic AI, Character AI, and Higgsfield shared how they are building on DigitalOcean.

Deploy 2026 was a cross-company effort, and a reflection of how DigitalOcean works every day.

These three team members capture what our culture of true ownership looked like up close:

• Meghan Grady, Senior Director of Marketing and Communications. She and her team led the planning and execution behind Deploy, including keynote production, customer programming, live streaming, social coverage, and event communications.
• Mitchell Mocchi, Sales Account Executive, and the team worked alongside sales, solutions architecture, support, and engineering teams. He met directly with customers on the Deploy floor and helped them through the AI infrastructure decisions in real-time.
• Tyler Gillam, Senior Software Engineer II, and his team helped build the Inference Router, DigitalOcean’s AI routing product. He worked with engineering and product teams to demo it live during the keynote.

A career high for the marketing team

With a launch as big as the AI-Native cloud, our team knew we had to go big. Registrations came in so strongly that even the venue had room to grow. The customer experience was front-of-mind as we added a second speaker track and introduced a startup showcase, giving five companies from DigitalOcean’s startup program a stage to talk about their journey. On top of that, the marketing and design teams launched a brand new website and brand identity alongside the event to showcase the company’s evolution.

This was also the first Deploy with paid sponsorships. Meghan, who previously worked on DigitalOcean’s partnerships team, watched those relationships come to life on the event floor. “Seeing all of our sponsors there, with their huge teams, and observing how those relationships have evolved and how much value they see in our partnerships and our ecosystem was really exciting,” she says.

There was no shortage of memorable moments throughout the day. The one Meghan keeps coming back to came mid-keynote, when the audience raised their phones to photograph the AI-Native Cloud stack. Months of work behind a single slide, and people wanted to hold onto it.

Working on events like Deploy is a highlight for any employee who is involved. “DigitalOcean is a very special place. What makes it is the people. Both the people who work here and our customers. The company is growing exponentially right now, and it’s a true opportunity for growth,” Meghan says.

Building and launching products side-by-side with our customers

Mitch has been at DigitalOcean for over four years. As an Account Executive, his job is managing customer relationships, which is much closer to the infrastructure than that title suggests. They’re the first call when something isn’t working, and the ones sitting across from customers, figuring out what it takes to make a workload succeed on the platform.

At Deploy, that meant a full day on the floor. On one end, he was talking to established customers like Hippocratic AI, running large inference clusters on H200 and B300 infrastructure. On the other front, brand-new startups are looking to get their hands on any GPU they can.

“The product release cycle over the past few months is acting as empirical data to our customers of where the company is headed,” Mitch says. “Customers were really excited to hear how much we were building.”

One conversation stuck with him. A week before Deploy, DigitalOcean had connected with Metamorphic, a San Francisco startup. At the event, Mitch found himself sitting around a table with their lead engineer, talking through their H100 cluster deployment live. “Just seeing how excited they were really stood out to me,” he says. “Knowing that DigitalOcean is acting as the infrastructure layer for this project is just fantastic.”

What surprises customers most, Mitch says, is how far the team is willing to go during a proof of concept. Sales, support, solutions architecture, and forward-deployed engineering all engage with a single customer at once to make sure they succeed.

When a customer hits a limit or a missing feature, the answer today isn’t to walk away. “We need to change the product,” Mitch says. “Because if this customer is experiencing this issue, there’s probably a group of others experiencing it too.”

From writing code to the keynote stage and booth conversations

For Deploy, the engineering team built the Inference Router, a product that automatically routes each AI request to the right model based on cost, latency, and quality. Tyler, a Senior Software Engineer focused on AI infrastructure and inference products, helped build it, then demoed it live during the keynote. He worked closely with product and engineering leaders, the CTO, and the CEO to make sure the story landed.

“Standing on stage showing something our team had actually built. It wasn’t just me showing something that I had done. It was me representing the entire team and what they’d worked on for the past two to three months,” Tyler says.

After the keynote, developers started walking up to the booth to ask about the Inference Router. They immediately understood the problem, and they wanted to use it. “It wasn’t just something we thought was valuable internally,” he says. “It actually proved itself.”

That kind of feedback loop isn’t unique to Deploy. It’s how the engineering team works day to day. Engineers at DigitalOcean are directly connected to the people using what they build. “I get to meet with existing customers, prospective customers, and AI-native companies to understand what they’re building and where they’re getting stuck. We hear directly from developers, carry those problems into product ideas, build them, and get them into customer hands quickly.”

The result is a pace and scope of work that’s hard to find elsewhere. “I’ve shipped more interesting and meaningful features here than anywhere else I’ve worked,” he says.

Tyler says the engineers who thrive at DigitalOcean are builders with strong ownership. People who care about the customer, care about the details, and want to move fast. That means the role extends well beyond writing code. “You’re not just going to work on a tiny piece of something that may or may not ship. You can help shape the product. You can build it, launch it, and talk to customers who are using it.”

For anyone weighing whether to join us at DigitalOcean, Tyler puts it simply: “If you want ownership, if you want to build products that developers actually use, and if you want to work on infrastructure at a moment where AI is changing incredibly fast, DigitalOcean is a great place to do that.”

Join the team building the AI-Native Cloud

As Tyler put it, Deploy reflects DigitalOcean’s culture:

“We care about builders. We care about simplicity. We care about making powerful infrastructure accessible. Deploy gives us a chance to show that through more than slides or announcements, but also through real demos, real conversations, and real products.”

The company is growing fast, and the work is changing just as quickly. Today, DigitalOcean is shipping a full-stack inference cloud. For those who work here, that means there’s room to shape products from the ground up, and work directly with the customers using them.

If that sounds like the kind of work you want to do, explore our open roles.