Introduction: Voice AI Agents Move Too Fast to Commit Too Early

Voice AI is evolving at a pace that’s both exciting and paralyzing.

Every few weeks, a new text-to-speech, speech-to-text, or speech-to-speech model claims lower latency, better expressiveness, or dramatically lower cost. For teams building voice-driven applications—support agents, copilots, games, accessibility tools—this creates a real problem: how do you commit to a stack when the ground keeps shifting?

Too often, teams feel forced into an early, single-provider decision. You pick a vendor, wire their SDK deep into your app, and hope they keep up. But at an early stage—when you’re still learning about users, usage patterns, and unit economics—that kind of commitment is risky.

Commitment with this much uncertainty requires you to build with optionality in mind. Today’s landscape doesn’t usually come with that flexibility from the jump.

The Core Problem: Vendor Lock-In

Most voice AI teams don’t plan to get locked in—but it happens anyway.

You start with a managed provider like ElevenLabs because it’s fast to integrate and sounds great out of the box. Over time, you discover friction:

• Costs scale faster than revenue
• Latency spikes during peak usage
• Missing features (streaming control, fine-grained prosody, regional routing)
• New models appear that are clearly better—but incompatible

At that point, your options are brutal:

1. Rebuild your entire voice stack around a new provider
2. Stick with a suboptimal solution and accept the trade-offs
3. Abandon voice features altogether

Meanwhile, the pace of innovation keeps accelerating. Locking yourself into a single provider today is a bet that they’ll still be the best option six months from now—a bet history suggests you’ll lose.

Key Considerations for Voice AI Infrastructure

Before talking about proxies, it’s worth grounding in the real constraints teams face.

1. Cost & Latency Trade-offs

Not all models optimize for the same thing:

• Some minimize time-to-first-token
• Others optimize for overall transcript quality
• Some are cheap at scale but slow to start
• Others are fast but expensive under concurrency

Usage patterns compound this complexity. Many applications see:

• Huge spikes (e.g., 1,000 concurrent calls at 9am)
• Long troughs (e.g., 10 concurrent calls the rest of the day)

Fixed plans and per-minute pricing struggle with this kind of burstiness. You either overpay for capacity you don’t use—or throttle when you can least afford to.

2. Technical Flexibility Needs

As products mature, teams want more control:

• True streaming with partial results
• Fine-grained configuration (temperature, pacing, emotion)
• GPU-level concurrency optimization
• Regional placement to reduce network latency

Many managed providers abstract these details away—which is great at first, and painful later.

3. The Build vs. Buy Question

At some point, every team asks:

• What should we own?
• What should we rent?

Self-hosting can make sense for cost, control, or compliance—but comes with real operational overhead. Fully managed services are easy—but rigid. The hard part is finding a middle ground that doesn’t lock you into either extreme too early.

The Models Solution: The Best of Both Worlds

This is where a proxy architecture comes in.

Instead of binding your application directly to a single voice provider, you place a thin abstraction layer in between. That proxy gives you:

• A unified API across multiple providers (ElevenLabs, Cartesia, open-source models, etc.)
• The ability to switch models without rewriting your app
• The freedom to optimize independently for model quality and network latency

Your application talks to one interface. Behind the scenes, you decide:

• Which model to use
• Where it runs
• When to switch

Today, that might mean ElevenLabs. Tomorrow, it might mean Cartesia. Next month, an open-source speech-to-speech model beats them both. Your product doesn’t care—and that’s the point.

How Hathora + Coval Work Together

A proxy is only as good as your ability to choose the right model. That’s where evaluation matters.

Coval provides the evaluation infrastructure:

• Run the same agent or prompt across multiple models
• Measure latency, quality, success rate, and task completion
• Compare results side-by-side in a single dashboard

Hathora provides the execution layer:

• Run those models across 14+ global regions
• Optimize GPU usage and concurrency
• Switch models without redeploying your app

Together, the workflow looks like this:

1. Run an agent 10 times across different Hathora-supported models
2. View performance and quality metrics in Coval
3. Select the best model for your actual workload
4. Flip traffic to that model—instantly

No rewrites. No migrations. No long-term bets on a single vendor.

Conclusion: Keep Optionality as the Industry Evolves

A proxy lets you wave a small magic wand:

• New model released? Try it.
• Latency creeping up? Reroute.
• Costs out of control? Swap in a cheaper backend.

As voice AI continues to evolve, the winning teams won’t be the ones who guessed the “right” model early. They’ll be the ones who designed for change from the start.

Maintain flexibility. Protect performance. And give yourself room to grow without rebuilding your stack every time the industry moves.

Interested in learning more about realistic simulations and evaluations for your conversational AI Agents?

Book a demo with Coval.

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