Businesses across industries are adopting speech-to-text (STT) technology to unlock new use cases and meet growing customer expectations. Whether it’s powering virtual assistants, transcribing conversations, or analyzing audio data for insights, STT has become essential for delivering seamless and engaging experiences.
But this rising demand brings with it a critical decision: should teams build an in-house STT solution or outsource to a commercial provider? The choice isn't just about cost—it's a decision that can impact your time-to-market, resource allocation, and ability to scale effectively. Getting it right means weighing a range of factors, from technical expertise and operational complexity to long-term costs and product roadmap alignment. It’s a lot to consider…
In this blog, we’ll weigh the pros and cons of each approach, highlight the trade-offs, and provide a practical guide to help you move forward confidently. Bonus: you’ll find a handy open-source vs. API cheat sheet at the end to simplify your decision-making process.
Looking for a more general primer on STT technology and what it takes to build a voice app? Check out this comprehensive guide instead.
Only a few years ago, building AI models required teams of specialists and extensive resources. Now, platforms like Hugging Face and GitHub provide engineers with immediate access to open-source code, democratizing AI development. This is particularly true for Automatic Speech Recognition (ASR) applications, where open-source tools like Mozilla DeepSpeech and Kaldi have accelerated innovation.
However, while open-source models (check out the top 5 here) have spurred the proliferation of voice platforms and apps, their adoption for enterprise use cases often comes with challenges and, oftentimes, an unexpectedly high total cost of ownership (TCO).
To really get to the heart of the buy vs build decision, let’s explore five key considerations in more detail: expertise, customization, scalability, cost, and time/resources.
Building an in-house STT solution with open-source tools like Whisper requires advanced AI and data science expertise. Beyond basic model deployment, production-grade solutions involve fine-tuning, optimization, and ongoing maintenance, which only well-resourced teams can handle.
Open-source STT solutions give businesses unparalleled control over their models, enabling bespoke optimizations for domain-specific needs. However, customization comes with added complexity. For instance, Whisper’s multilingual capabilities don’t equally support all languages, and adapting it to specific features like accurate speaker diarization or improved formatting may require extensive additional work.
While open-source tools can handle low-scale use cases effectively, scaling them for high-volume transcription demands significant investment in infrastructure. Whisper’s Large-v2 model, for example, is resource-intensive, requiring GPUs with substantial memory capacity. Enterprises must account for additional compute power for parallel processing, especially for real-time transcription.
Open-source software may appear cost-effective at first, but the TCO tells a different story. The costs of hosting (GPUs, CPUs), network traffic, certifications, and security measures quickly add up. Depending on the scale of operations, maintaining an in-house solution could cost between $300k and $2 million annually 🤯
Developing and deploying an in-house ASR system requires months of work, and with the rapid pace of AI evolution, models can become obsolete within a few years. This demands constant reinvestment to stay competitive. Outsourcing to commercial STT providers often saves time and ensures access to the latest technology without diverting resources from core product development.
While this high-level overview should help you grasp the key considerations for your STT strategy, some of you might be looking for a more structured way to evaluate the buy vs build decision. The Risk, Cost, and Focus (RCF) framework should help you determine whether self-hosting an STT solution aligns with your needs and priorities.
We know that businesses at different stages of growth have different scalability needs, so we’ve segmented the framework by transcription volumes (hours of audio per month) and the overall growth stage of your company. Where do you see your business on this scale?
If you're in the early stages of finding product-market fit and transcribing less than 5,000 hours of audio per month, hosting Whisper in-house may make sense. At this volume, costs are manageable, and while the vanilla model lacks optimizations, features, and accuracy in certain scenarios, it’s good enough for proof-of-concept work.
The trade-offs? Whisper’s tendency to hallucinate, limited support for features like diarization, and formatting inconsistencies. However, these downsides are usually acceptable for low-stakes prototyping.
When transcription volumes rise to 5,000–15,000 hours per month, the equation changes. Costs increase as you’ll need full-time employees to:
Parallel transcription requests will also surge, necessitating on-demand GPU availability—significantly more expensive than reserved instances—alongside a robust queuing system.
At this stage, hosting in-house is rarely worth the effort. Your resources are limited, and market pressures demand you focus on your platform’s core differentiators, not on perfecting transcription infrastructure.
For platforms transcribing over 15,000 hours per month, transcription becomes core to your business operations. While you likely have the budget to host in-house—easily exceeding $2M annually—consider whether it’s the best strategy.
The pace of innovation in ASR technology is rapid and maintaining an in-house team dedicated to keeping up with advancements will likely detract from delivering a reactive, competitive roadmap for your core product.
All in all, outsourcing your transcription needs to specialized STT providers is a more viable option for scaling platforms, enabling faster time-to-market and wiser allocation of resources to the core features of your platform.
As we’ve said, for some organizations, building an in-house solution offers the control and customization they need. For others, partnering with BigTech or specialized providers is a faster, more scalable option. Which camp are you in?
Did you know? 72% of functional leaders are opting to buy generative AI capabilities from either existing or new vendors, according to the Gartner Generative AI 2024 Planning Survey. This trend reflects the growing preference for outsourcing critical AI functionalities to trusted providers.
As we’ve said, choosing between building in-house or partnering with a commercial STT provider is a pivotal decision. While open-source offers control, the complexity and costs often outweigh the benefits for most businesses.
Ready to simplify your STT integration? Book a demo with Gladia or sign-up for free now to see how our industry-leading API can help you innovate and scale effortlessly.
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