If you are building voice-enabled AI applications in 2025, you probably know already that the STT/TTS provider landscape is sort of wild right now: Deepgram, ElevenLabs, Cartesia, OpenAI, Google Cloud, Azure - each week someone releases a new model that claims to be faster, cheaper or more natural sounding than the rest.
The question is not "which provider is best?": it's "should I ever pick only one?"
We at Sayna have been thinking about this issue a lot: from the beginning, we designed our voice layer to be provider-agnostic - and I want to share why this architectural decision matters more than most developers realize.
The hidden cost of provider lock-in
Let me be honest: when you start building a voice agent, it's tempting to just pick one provider and go all-in. Deepgram has great latency? Cool, let's use Deepgram everywhere. ElevenLabs has the most natural voices? Perfect, we'll just integrate ElevenLabs.
This is exactly what we did at Sayna for our first voice features, and guess what happened: six months later we were stuck.
Here is the reality:
Pricing changes. OpenAI cut its realtime API prices in August 2025 by 20% and Google's Gemini 3.0 flash came out with pricing that made voice automation economically viable for workflows that were previously too expensive. If you are tied to one provider, you can't take advantage of these market shifts.
Quality improvements. Deepgram Nova-3 released in February 2025 with Sub-300ms latency and significantly better accuracy, Cartesia's Sonic became the fastest TTS API on the market, new open-source models like Kokoro are catching up to proprietary solutions. The provider that was "best" when you started may not be best anymore.
Regional requirements. Some use cases require data to stay in specific regions. Some providers have better infrastructure in Europe vs Asia vs Americas. If your users are global, one provider might give great latency in San Francisco but terrible experiences in Singapore.
The real-world scenarios
Let me break down specific scenarios where multi-provider strategy gives you huge advantages.
Cost optimization:
Various providers have different pricing models and sweet spots, and right now the market looks kind of like this:
- Deepgram is great for high-volume STT at $0.0036/min for batch processing
- Speechmatics offers TTS at $0.011 per 1,000 characters: that's 11-27x cheaper than ElevenLabs for enterprise workloads
- Google Cloud Dynamic Batch pricing hits $0.003/min for bulk transcription
- Cartesia has developer-friendly starter plans for prototyping
What if you could arrange simple, high-volume transcription to cheaper providers while retaining premium voices for customer interaction? That is not theoretical -- enterprises are doing this right now and saving 40-60% on their speech costs.
Quality Fallback
This is something that most developers don't think about until it bites them; every provider has downages; every provider has edges where their model struggles.
ElevenLabs excels at emotional, expressive voices: perfect for audiobooks and gaming; but for a medical dictation app where accuracy is critical, AssemblyAI's domain-specific models perform better: for real-time customer service bots where latency is king, Cartesia's response times beat all others by sub100ms.
Having an abstraction layer means that you can implement fallback logic:
- Primary: ElevenLabs for quality
- Fallback: Deepgram if ElevenLabs latency exceeds threshold
- Emergency: Local Model if both cloud providers are down
Latency Routing
Here's a scenario we deal with constantly: A user in Tokyo connects to voice agent. The nearest STT provider is in Oregon. Round-trip latency is already 150ms before processing happens.
If you have configured multiple providers, you can route based on geography:
- Asia-Pacific users: Google Cloud (Singapore region)
- Users in Europe: Azure (West Europe)
- US users: Deepgram (lowest overall latency)
This isn't premature optimization: In voice AI everything above 300 ms feels robotic. Human conversation tolerates about 200ms of gaps: anything longer and users start talking to your agent because they assume it hasn't heard them.
The compliance angle
For regulated industries like healthcare and finance, multi-provider architecture is not simply nice to have: it becomes mandatory.
Native speech to speech models function as 'black boxes' You can't audit what the model analyzed before responding - without visibility into intermediate steps - you can't verify that sensitive data was handled correctly or that the agent follows required protocols.
A modular approach with provider abstraction maintains a text layer between transcription and synthesis, which enables:
- PII redaction: Scan intermediate text and strip social security numbers, patient names, or credit card numbers before they enter the reasoning model
- Audit trails: Log exactly what was transcribed, what was processed and what was synthesized
- Compliance controls: Apply different rules based on conversation context
These controls are difficult, and sometimes impossible, to implement inside opaque, end-to-end speech systems.
When a Single Provider Really Makes Sense
I'm not saying that everyone needs a multi-provider strategy, there are legitimate cases where locking to a provider is the right call:
Early prototyping. When you just validate whether voice-based AI works for your use case, keep it simple: pick one provider, build fast, learn fast. You can abstract later.
Very specific quality requirements. If you need theatrical voices for a video game and ElevenLabs is the only provider that meets your bar, optimize for this relationship.
Tiny scale. If you work with 100 minutes of audio per month, the engineering overhead of a multi-provider doesn't pay off. Keep it simple.
Deep integration features. Some providers offer unique capabilities: Deepgram's real-time sentiment analysis, AssemblyAI's speaker diarization, Azure's pronunciation assessment. If you are building around a specific feature, lock in.
The architecture that scales
Here's how we think about it in Sayna: Your application code should never contain provider-specific calls, instead you talk to a unified interface and this interface handles provider selection, failover and optimization.
This means:
Configuration changes not code. Switch from Deepgram to ElevenLabs by changing config, not rewriting your voice pipeline.
Runtime decisions. Route to different providers based on latency measurements, cost thresholds or quality requirements – all without deploying new code.
Graceful degradation. If your primary provider has a fault at 3AM, traffic automatically routes to the backup without human intervention.
A/B testing. Try new providers on a small percentage of traffic. Compare quality metrics. Gradually shift traffic based on data.
The best part is this architecture doesn't add complexity to your business logic – your AI agent code stays exactly the same: it just sends text and receives audio. All provider management happens under that abstraction layer.
The market is moving fast
A year ago, the only reliable way to add natural sounding voice to an AI agent was to call the API of a model provider and accept the cost, latency and vendor lock-in. Today, open-source models like Kokoro match proprietary solutions in blind tests.
This trend is increasing: the provider that is best today might be irrelevant in six months; the pricing that seems reasonable today might be undercut 50% by a competitor tomorrow.
Building with provider abstraction isn't just about optimizing for today: it's about keeping your options open for a market that is evolving faster than any of us expected.
The demand for optionality creates a natural moat for platforms that can orchestrate multiple models and abstract the complexity of the switching between them.
If you're thinking "Oh, we probably should have some flexibility in our speech stack...", then you have already made the right point: the question is just how much abstraction you need and when to invest in building it up.
The answer for most teams is: sooner than you think.
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