I've been tasked with modernizing our legacy IVR system. The robotic TTS is a constant point of complaint in our customer satisfaction surveys. I'm evaluating ElevenLabs as a potential drop-in replacement for generating the voice prompts.
My primary concerns are naturalness and stability. I don't need quirky character voices; I need a clear, calm, and professional tone that doesn't sound like a 90s call center. I've run some basic tests with their API, but I'm looking for real-world, production-scale experience.
Key questions for anyone who has deployed this:
* **Fluency & Prosody:** Does it handle complex sentences, dollar amounts, or alphanumeric strings (like confirmation codes) without weird pauses or mispronunciations?
* **API Reliability:** What's the observed latency and uptime for the `text-to-speech` API endpoint? Any rate-limiting pitfalls during high call volumes?
* **Cost at Scale:** Our system handles ~500k prompt plays monthly. The per-character pricing seems low, but does it scale predictably? Any hidden costs with their professional tier?
* **Voice Consistency:** If we generate a prompt library in one batch, then add new prompts six months later, does the same "voice" still sound identical, or are there model drift issues?
Here's the basic Terraform+shell approach I'm prototyping for generating a prompt library. This is more about pipeline integration than the quality itself.
```haskell
# variables.tf
variable "elevenlabs_api_key" {
sensitive = true
}
variable "prompts" {
type = map(string)
default = {
"welcome" = "Thank you for calling. For support, say help. For sales, press one."
"hold" = "All agents are currently busy. Your estimated wait time is {{time}} minutes."
}
}
# local-exec provisioner to generate audio files (simplified)
resource "null_resource" "generate_tts" {
for_each = var.prompts
triggers = {
prompt_text = each.value
voice_id = "21m00Tcm4TlvDq8ikWAM" # Pre-chosen voice ID
}
provisioner "local-exec" {
command = <<-EOT
curl -X POST " https://api.elevenlabs.io/v1/text-to-speech/${self.triggers.voice_id}"
-H "xi-api-key: ${var.elevenlabs_api_key}"
-H "Content-Type: application/json"
-d "{"text": "${self.triggers.prompt_text}", "model_id": "eleven_monolingual_v1", "voice_settings": {"stability": 0.5, "similarity_boost": 0.75}}"
--output "generated/${each.key}.mp3"
EOT
}
}
```
I'm less interested in marketing fluff and more in whether the output passes the "this doesn't annoy me" test during a 3 AM incident when I'm the one calling in. What's your verdict based on production logs?
-shift
shift left or go home
We went all-in on ElevenLabs for our main customer support line last year, so I can share our exact data. It was a massive improvement over our old vendor.
> **Fluency & Prosody**
It's fantastic on standard sentences, but you *have* to test edge cases. Dollar amounts ("$1,502.75") and alphanumeric strings are handled well, but you need to format them carefully. We found writing out "confirmation code: Alpha, Bravo, Charlie, One, Two, Three" sounded far more natural than "ABC123". For complex sentences, the prosody is good, but we script everything to avoid overly long clauses. The pacing is human, not robotic.
> **API Reliability & Cost at Scale**
Latency is consistent, around 800ms-1.2s for generation. We've had 99.9% uptime. The real scaling issue is pre-generation. You can't realistically generate audio in real-time for 500k plays monthly. We cache every single prompt. Our library of ~300 prompts was generated once, stored on our CDN, and cost pennies. The cost only scales if you're constantly generating new, unique sentences. For static IVR menus, it's incredibly cheap.
> **Voice Consistency**
This is where I'd urge caution. We generated our initial library with "Rachel" (a premade voice). When we added prompts six months later, the tone was identical. However, last month they updated their model, and regenerating a single prompt for a minor text change resulted in a *noticeably* different cadence on that one file. It wasn't a deal-breaker, but it was audible. For a full IVR, you must generate your entire final library in one go and never re-generate that voice. Plan your script changes carefully.
If you pre-generate and cache everything, it's a stellar solution. Just don't assume you can use the API for live, on-the-fly generation during a call without serious latency and cost implications.
Happy testing!
While the raw audio quality is a leap over traditional TTS, the real bottleneck in a production IVR is the synthesis latency for dynamic prompts. A 1.2-second generation time per unique prompt is significant when you're dealing with call hold times; that's an eternity in telephony. You must architect for aggressive pre-generation and caching of any predictable phrase. For purely dynamic content like a live account balance, you're adding noticeable delay.
On the point of voice consistency over time, we've documented slight model drift. Prompts generated from the same voice profile in Q1 and Q3 of last year had measurable, albeit subtle, differences in timbre when analyzed spectrographically. It wasn't jarring to the human ear, but it was present. For a large static library, generate everything in a single project/session if absolute uniformity is critical.
Their per-character pricing scales linearly, but you'll hit the "professional tier" requirement quickly at 500k plays. The hidden cost isn't in the API calls, it's in the engineering hours required to implement a fault-tolerant pipeline that handles their occasional batch job slowdowns gracefully.
Trust but verify.