ElevenLabs scales global AI voice innovation

THE CHALLENGE

Hitting the limits of legacy data stores

Before shifting from AI research to enterprise products, ElevenLabs’ legacy database lacked native free-text search, draining engineering time.

The rapid advancement of artificial intelligence has transformed how global enterprises interact with audiences, shifting the focus from simple text interfaces to audio experiences. During the initial wave of the generative AI boom in 2022, London startup ElevenLabs set out to change the way humans interact with businesses through technology by building APIs for audio models.

“Our voice Text to Speech model was revolutionary,” said Alex Holt, Field CTO at ElevenLabs.

“It was the first AI model that sounded human.” However, ElevenLabs soon realized that deploying exceptional voice models was only part of the operational matrix. 

“Building an API that enables you to turn text into speech is fantastic,” said Holt. “But our customers all struggled with the same challenge: building something that not only sounds human but also interacts in a human way.” To unlock true business value, the technology had to be interactive, context-aware, and integrated into complex enterprise environments.

This realization drove ElevenLabs’ evolution from a voice research lab into a product-focused platform provider as well. Beyond offering developer APIs, it began delivering end-to-end solutions that allowed enterprises to instantly deploy human-sounding agents connected directly to their core sales, support, and operational workflows.

Yet the company faced an architectural roadblock during the development of its high-accuracy Speech to Text transcription and editing product. The application required advanced interfaces to let users edit, format, and correct transcribed text in real time. However, ElevenLabs’ legacy database provider lacked native support for free-text search. To bypass this limitation, the company’s  engineering team had to build a makeshift architecture, layering secondary index stores on top of the primary database and writing custom code to keep specific data fields synchronized.

“It became a full-time job for someone to maintain the secondary database and the code to synchronize between the two,” explained Holt.

OUR SOLUTION

Consolidating the data layer

ElevenLabs moved to MongoDB on Google Cloud, eliminating separate index stores and sync code to bring simplicity to its data tier.

Realizing that infrastructure complexity was slowing its product development speed, ElevenLabs shifted to a unified database layer. When launching its next product phase, the team selected MongoDB on Google Cloud to serve as the simplified foundation for its operational data.

By consolidating its data into MongoDB, ElevenLabs eliminated the need for separate search index stores and fragile synchronization code. MongoDB’s flexible document model natively handled the diverse text assets, formatting corrections, and metadata needed for the company’s high-accuracy Speech to Text workflows, rendering the previous multi-store workarounds obsolete. The database provided built-in text querying capabilities that solved previous search limitations out of the box.

By transitioning to MongoDB, ElevenLabs brought simplicity to its development environment, enabling the company to focus on product evolution rather than infrastructure management. The database’s ability to scale automatically meant that the startup’s engineering teams no longer had to actively manage or worry about the data tier.

"It was easy to say, ‘let's try something different’—we know that MongoDB is very good at this,” said Holt.“ And the thing that was really impressive was that, once we’d done it, we didn't have to think about it."

OUTCOME

Unlocking enterprise-scale growth

With the data layer fully stabilized and running seamlessly in the background, ElevenLabs unlocked the engineering agility required to scale rapidly. By eliminating database infrastructure headaches, the company successfully completed its transition into an enterprise platform giant.

The growth metrics achieved over a three-year span underscore the impact of this streamlined operational foundation. By eliminating early data infrastructure roadblocks, ElevenLabs grew from a 10-person startup into a powerhouse valued at $11 billion, achieving $600 million in annual recurring revenue (ARR) to become the fastest-growing AI-native company out of London. This rapid growth is backed by deep enterprise adoption, with two-thirds of the Fortune 500 now trusting the platform to handle complex, high-scale operational workflows. Ultimately, this modernized data layer operates at a massive agentic scale, seamlessly powering millions of autonomous voice and chat agents across more than 70 different languages.

Today, ElevenLabs leverages this robust data foundation to deploy an advanced commercial strategy. The company now embeds specialized deployment strategists and forward-deployed engineers directly with major global enterprises to build system-connected conversational agents that deliver clear, measurable ROI.

Instead of relying on traditional web forms or long customer service hold times, companies can deploy autonomous voice layers to handle end-to-end sales, customer support, and intricate back-end operational tasks. For example, delivery platforms now use ElevenLabs' voice agents to autonomously call restaurants, verify real-time opening hours, and instantly update internal systems without any human intervention.

Ultimately, resolving its early database challenges proved that infrastructure simplicity is the ultimate catalyst for AI innovation. 

As Holt concluded: "For any infrastructure the best outcome is that the engineering team doesn't have to worry about it because it just runs.  You focus on the innovation with the models because you don't have to worry about the data layer—and the data layer is MongoDB."