Companies that use ElevenLabs

Source: Analysis of job postings that mention ElevenLabs (using the Bloomberry Jobs API)

I noticed the purchasing decision for ElevenLabs spans creative leadership (15% Creative Directors), technical leadership building AI infrastructure (10% Backend Engineers, 8% AI Engineers), and heads of marketing or product teams. These buyers are focused on scaling content production velocity while maintaining quality. They're hiring to build what one posting calls "AI-powered creative workflows for content generation" and "scalable creative systems powered by emerging AI technologies."

The hands-on users are predominantly Video Editors (12%) and Content Creators (10%) who integrate ElevenLabs into daily production workflows. These practitioners use it alongside tools like Runway, Midjourney, and Adobe Suite to produce high volumes of short-form content for Meta, TikTok, and YouTube. I found practitioners tasked with creating "10-15 reels/short-form videos every day" and building "AI-powered content pipelines" where ElevenLabs handles voiceover generation at scale.

The core pain point is production bottlenecks. Companies want to "accelerate rate of learning" and achieve what multiple postings describe as producing content "in 12 minutes" or "weeks of authentic content" from minimal input. One posting explicitly states the goal of "radical efficiency" while another seeks to "automate the toil." These organizations are racing to match the "velocity" demands of modern content marketing, using ElevenLabs to eliminate the traditional time and cost constraints of professional voiceover production.

Source: Analysis of Linkedin bios of 450 companies that use ElevenLabs

I noticed ElevenLabs attracts two distinct customer types that both need to reach audiences at scale. The first group includes major broadcasters and media companies (SVT, Fox Corporation, RTS, Scripps stations) that produce news, entertainment, and educational content for millions of viewers. The second group comprises customer-facing businesses across healthcare, finance, insurance, and e-commerce that need to handle high-volume communications. Whether they're creating video content or managing customer calls, these companies share a common challenge: speaking to diverse, global audiences efficiently.

The company profiles reveal a clear enterprise bias. Most are mature organizations with 500+ employees and established market positions. I see numerous post-IPO companies, Fortune 500 firms, and businesses with decades of operating history. However, there's also a meaningful contingent of Series A through Series C startups (Panjaya, Assort Health, Wonderful) that are scaling rapidly in AI-native categories. The funding stages and employee counts suggest ElevenLabs appeals to companies with resources to invest in quality voice AI, not bootstrapped startups.

Source: Analysis of tech stacks from 450 companies that use ElevenLabs

I noticed that companies using ElevenLabs are distinctly technical and data-driven organizations, likely in the scale-up phase where they're investing heavily in both product quality and operational infrastructure. The presence of Monte Carlo Data, Docker Business, and Tines tells me these aren't casual tech adopters. They're building sophisticated systems that require data observability, containerization, and security automation. They're treating voice AI as a serious product component, not just an experimental feature.

The pairing of ElevenLabs with Perplexity Enterprise is particularly revealing. These companies are betting on cutting-edge AI across multiple functions, suggesting they're either AI-native products or heavily integrating AI into their core offering. When I see UserTesting alongside voice technology, it signals these teams are obsessively focused on user experience. They're probably using ElevenLabs to create customer-facing voice interactions and want to validate that the synthetic speech actually works for real users. The Docker Business correlation suggests they're deploying voice capabilities at scale, likely through APIs integrated into production environments.

The full stack reveals product-led companies in their growth stage, probably Series A through C. They have the budget for premium tools like Perplexity Enterprise and Monte Carlo, but they're choosing solutions that scale efficiently. The emphasis on data quality, testing, and automation shows mature engineering practices. These aren't bootstrapped startups experimenting with voice, nor are they enterprise giants with lengthy procurement cycles. They're mid-sized companies moving fast but with proper infrastructure.