ElevenLabs transformed itself from a voice research lab into an enterprise platform powerhouse by solving a seemingly unglamorous problem: database infrastructure. The London-based AI startup, now valued at $11 billion and generating $600 million in annual recurring revenue, achieved this explosive growth after migrating to a new database system that eliminated years of technical debt and freed its engineering team to focus on innovation rather than infrastructure maintenance.

What Problem Was Slowing Down ElevenLabs' Growth?

When ElevenLabs first launched, the company's revolutionary text-to-speech model captured attention across the industry. The technology sounded genuinely human, a breakthrough that seemed like the foundation for massive success. But the startup quickly discovered that exceptional voice models were only half the battle.

"Building an API that enables you to turn text into speech is fantastic," explained Alex Holt, Field CTO at ElevenLabs. "But our customers all struggled with the same challenge: building something that not only sounds human but also interacts in a human way."

Alex Holt, Field CTO at ElevenLabs

To deliver real business value, ElevenLabs needed to evolve beyond simple voice generation. The company began building end-to-end solutions that allowed enterprises to deploy autonomous voice agents capable of handling sales calls, customer support, and complex operational workflows. This required developing a high-accuracy speech-to-text transcription and editing product with advanced interfaces for real-time text correction and formatting.

But here's where infrastructure became the unexpected bottleneck. ElevenLabs' legacy database system lacked native support for free-text search, a critical feature needed for the transcription product. To work around this limitation, the engineering team built a makeshift architecture: they layered secondary index stores on top of the primary database and wrote custom code to keep data fields synchronized between the two systems.

"It became a full-time job for someone to maintain the secondary database and the code to synchronize between the two," explained Holt. "It was easy to say, 'let's try something different', we know that MongoDB is very good at this. And the thing that was really impressive was that, once we'd done it, we didn't have to think about it."

Alex Holt, Field CTO at ElevenLabs

This wasn't a minor inconvenience. The company was dedicating significant engineering resources to maintaining database infrastructure instead of building features that customers actually needed.

How Did Switching Databases Unlock Growth?

Recognizing that infrastructure complexity was becoming a serious drag on product development velocity, ElevenLabs made a strategic decision. When launching its next product phase, the team selected MongoDB on Google Cloud as the foundation for its operational data layer. This choice proved transformative.

MongoDB's flexible document model natively handled the diverse text assets, formatting corrections, and metadata required for high-accuracy speech-to-text workflows. More importantly, the database provided built-in text querying capabilities that eliminated the need for separate search index stores and fragile synchronization code. By consolidating its data into a single system, ElevenLabs freed up engineering resources and reduced operational complexity.

Steps to Building Infrastructure That Scales With Your AI Company

• Identify Infrastructure Bottlenecks Early: ElevenLabs recognized that maintaining multiple database systems was consuming engineering time that could be spent on product innovation. Regularly audit whether your infrastructure is enabling or hindering development velocity.
• Choose Systems With Native Support for Your Use Case: Rather than building workarounds for missing features, select infrastructure that natively supports your core requirements. ElevenLabs needed free-text search capabilities, and MongoDB provided this out of the box.
• Prioritize Automatic Scaling and Maintenance: The best infrastructure is infrastructure that doesn't require constant attention. MongoDB's automatic scaling meant ElevenLabs' engineering team could focus on model innovation instead of database management.

The results were immediate and dramatic. By transitioning to MongoDB, ElevenLabs brought simplicity to its development environment. The database's ability to scale automatically meant that engineering teams no longer had to actively manage or worry about the data tier. As Holt noted, the goal of any infrastructure 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".

As Holt

What Growth Metrics Followed This Infrastructure Shift?

The impact of this decision became visible in ElevenLabs' growth trajectory. Over a three-year span, the company achieved remarkable expansion metrics:

• Company Valuation: ElevenLabs grew from a 10-person startup into a company valued at $11 billion, making it the fastest-growing AI-native company out of London.
• Annual Recurring Revenue: The company reached $600 million in annual recurring revenue, a figure that reflects deep enterprise adoption and trust in the platform.
• Enterprise Adoption: Two-thirds of the Fortune 500 now use ElevenLabs' platform to handle complex, high-scale operational workflows.
• Global Scale: The modernized data layer powers 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 embeds specialized deployment strategists and forward-deployed engineers directly with major global enterprises to build system-connected conversational agents that deliver measurable return on investment.

Real-world examples demonstrate the practical impact. 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. These aren't simple voice bots; they're sophisticated agents capable of handling complex, context-aware conversations that previously required human operators.

The broader lesson here extends beyond ElevenLabs itself. For any AI company scaling rapidly, infrastructure decisions made early in the company's life can either accelerate or constrain growth. By choosing a database system that eliminated technical debt and provided native support for core use cases, ElevenLabs freed its engineering team to focus on what actually matters: building better AI models and delivering more value to customers. In the competitive landscape of AI voice technology, that focus on innovation rather than infrastructure maintenance may have been the difference between becoming a unicorn and remaining a promising startup.