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February 2026 · 8 min read

I Evaluated 8+ Voice AI Vendors. Here's How I Would Decide What to Build vs Buy.

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If you're building a production voice AI system today, you might think the hard part is the AI. It's not. The hard part is figuring out which pieces to build yourself and which to buy.

The voice AI stack has three core components: speech-to-text (STT), a language model (LLM), and text-to-speech (TTS). Each one has dozens of providers, each with different latency profiles, pricing models, and quality tradeoffs.

And you need all three working together in under 500 milliseconds to feel like a real conversation.

The Latency Problem Nobody Talks About

Human conversation operates within a 300 to 500ms response window. Delays beyond 500ms feel unnatural. Beyond 800ms, users start repeating themselves or hanging up.

Here's the problem: latency compounds. If STT takes 200ms, LLM takes 500ms, and TTS takes 150ms, you're already at 850ms before network overhead. That's too slow.

The first question isn't "build or buy?" It's "where does latency matter most for my use case?"

What I Actually Tested

I evaluated providers across three dimensions: latency, accuracy, and cost. After researching 8+ vendors and reviewing real-world benchmarks, here's what stands out.

Speech-to-Text: Buy

The STT market surprised me. Google Cloud ASR, despite the brand, consistently ranked last in independent benchmarks. Meanwhile, Deepgram hit 150ms streaming latency with strong accuracy on noisy audio.

Building competitive ASR would take years. The accuracy improvements from specialized providers are hard to match. I went with Deepgram as primary, Whisper as fallback for complex audio.

Text-to-Speech: Buy (with tiers)

TTS is where voice AI feels human or robotic. ElevenLabs was noticeably more natural than Polly, but at 2 to 3x the cost.

The tradeoff: premium TTS for high-stakes interactions (sales, support), cheaper fallback for transactional calls (confirmations, status updates). Built an abstraction layer to swap without changing application code.

LLM: Hybrid

Off-the-shelf LLMs are optimized for general conversation, not domain-specific voice interactions. They also don't know when to stop talking, which matters a lot in voice.

GPT-4 was most capable but slowest. Claude handled long context better. I ended up using frontier models for complex reasoning, but building custom logic for turn-taking, interruption handling, and domain-specific responses.

Orchestration: Build

This is where most teams make their biggest mistake. They assume they can chain APIs together: STT to LLM to TTS, done.

But real voice conversations have edge cases that off-the-shelf platforms don't handle well:

  • User interrupts mid-sentence (barge-in)
  • Background noise triggers false transcription
  • LLM generates a response that's too long for voice
  • Network latency spikes mid-conversation

The orchestration layer is where product differentiation lives. The individual components are commoditizing. How you stitch them together is not.

The Hidden Cost: Integration Complexity

One thing teams consistently underestimate: the integration tax. Every vendor has different authentication patterns, streaming protocols, error handling, and billing models.

Even if you buy everything, you're still building the glue. And that glue becomes critical infrastructure.

At scale, 10,000 calls per month at 5 min average means $3,500 per month in AI costs alone.

Budget 30 to 40% of engineering time for integration work, even in a "buy everything" scenario. Build abstraction layers from day one. You will switch vendors.

Key Takeaways

  1. Test latency end-to-end first. Component benchmarks don't capture real-world performance. Build a minimal pipeline and measure total latency before committing.

  2. Start with the orchestration layer. Don't pick STT/TTS/LLM first. Design the orchestration architecture, then fill in components.

  3. Build cost models early. Voice AI costs compound fast. Model your unit economics before you scale.

  4. Plan for hybrid architectures. No single vendor wins on everything. Design for mixing and matching from day one.

The Bottom Line

Voice AI is finally production-ready. The components are good enough and cheap enough to build real products. But "good enough" doesn't mean "plug and play."

The build vs buy decision isn't binary. It's about knowing where to spend your engineering effort. Buy the commoditizing components. Build the differentiated orchestration. Invest heavily in integration architecture.

The AI is the easy part. The plumbing is hard.