European enterprises face three paths for Voice AI: self-host everything, stitch together vendors, or use an integrated platform. Here is what each option actually costs in latency, complexity, and production reliability.
European enterprises deploying Voice AI face a decision that sounds simple but has significant technical implications: build or buy?
In practice, there are four distinct paths:
Each path has different trade-offs in latency, cost, compliance complexity, and operational burden. This guide breaks down what each option actually requires, so you can make the right call for your situation.
The appeal of self-hosting is clear: complete control over data residency, no per-minute vendor fees, and the ability to customize every layer. For European enterprises with strict sovereignty requirements, it sounds like the obvious choice.
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But self-hosting Voice AI is fundamentally different from self-hosting a web application or database.
A production Voice AI system requires:
Each layer requires different expertise. Telephony is carrier engineering. STT/TTS is ML ops. LLM hosting is GPU infrastructure. Most teams don't have depth in all three.
| Component | What Happens | Impact |
|---|---|---|
| Carrier routing | Leased wholesale routes vary 200-800ms by time of day | Conversations become unusable |
| Audio codecs | Demo uses Opus HD, production uses G.711 compressed | STT accuracy drops significantly |
| STT/TTS rate limits | At 100+ concurrent calls, providers cap requests | Cascade failures, customer complaints |
| Media servers | At 1,000+ concurrent calls, memory limits hit | System crashes under load |
| Vendor blame game | Call fails at 2AM, each vendor points at another | No unified debugging, extended outages |
If anyone could make self-hosted Voice AI work, it would be Siemens. They have thousands of engineers, billions in revenue, their own data center in Switzerland with H200 GPUs, and a dedicated platform team.
Their code.siemens.com team built a fully sovereign LLM platform with:
What they documented:
"Solid expertise required in multiple areas: Having a sovereign AI stack demands deep technical knowledge in addition to the initial financial investment. Not an easy feat."
Siemens cycled through three different routing tools (FastChat → LiteLLM → vLLM Router) before finding one that worked. Each migration required engineering time and production disruption.
"Hardware might (and does) break: Even the most reliable hardware can fail. A recent incident with a failing GPU reminded us of the importance of redundancy and proactive monitoring, and again, the expertise necessary down to bare metal."
The reality: If Siemens, with H200 GPUs and hundreds of engineers, describes self-hosting as "not an easy feat," what happens to a 50-person team with an AWS account and a deadline?
The most common approach today: buy best-of-breed components and integrate them yourself.
A typical European Voice AI stack looks like:
Five vendors. Five contracts. Five DPAs. Five compliance audits. And a latency problem that compounds at every boundary.
Each vendor boundary adds network overhead. When your audio hops from Paris to Virginia for STT, to San Francisco for LLM, to Dublin for TTS, then back to Paris, you've added 500ms+ before any processing begins.
| Hop | Latency Added |
|---|---|
| EU telephony → US STT | +300ms |
| US STT → US LLM | +250ms |
| US LLM → US TTS | +250ms |
| US TTS → EU caller | +300ms |
| Total round-trip | +1000ms |
Each vendor takes margin. At scale, you're paying 3-5x what integrated infrastructure costs.
| Layer | Frankenstack | Integrated Platform |
|---|---|---|
| Telephony | Twilio (margin) | ✓ Included |
| STT | Deepgram (margin) | ✓ Included |
| LLM routing | OpenAI (margin) | ✓ Included |
| TTS | ElevenLabs (margin) | ✓ Included |
| Orchestration | Vapi/Retell (margin) | ✓ Included |
| Total cost | €0.25-0.35/min | starting from €0.05/min |
GDPR requires control over where data is processed, not just stored. When audio routes through multiple US-based services for processing, you've created compliance gaps that contracts can't fully address.
Most Frankenstack vendors claim "EU data residency" while routing voice data through US infrastructure for actual processing. The metadata stays in EU. The audio doesn't.
The hidden liability: When regulators ask where AI inference happens, nobody in the Frankenstack can answer with certainty. Each vendor points to their piece. Nobody owns the full picture.
Many teams land on a middle ground: use a telephony provider for SIP trunking and real-time media streaming, but build their own AI layer with external STT and TTS providers.
This solves the telephony problem. You get SIP, global numbers, and reliable media delivery without building PSTN infrastructure. The audio streams to your server, you process it with your AI stack, and send audio back.
The architecture looks like this:
Call arrives → SIP → Your server → External STT API → Your LLM → External TTS API → Your server → SIP → Caller hears response
Every arrow is latency, making round-trips to external STT and TTS providers. If those providers are US-hosted (most are), you're adding 200-400ms per hop.
You also inherit the operational complexity of managing multiple AI vendors: separate contracts, separate rate limits, separate debugging when something breaks at 2AM.
If you're already using Telnyx for SIP and media streaming, the path to lower latency is straightforward: move your STT and TTS to Telnyx's co-located AI infrastructure.
Telnyx provides access to multiple STT engines (Whisper, Deepgram, Google, AssemblyAI, xAI, Speechmatics, Soniox) and 8 TTS providers (Telnyx, Rime, Azure, Amazon Polly, Resemble AI, and more), all running on the same network as your telephony. Instead of streaming audio to your server, then to external AI providers, you can use Telnyx STT and TTS where the audio never leaves Telnyx's infrastructure until it's processed.
Same Telnyx account. Same API patterns. Significantly lower latency.
The fourth option: a single vendor that owns the entire stack, from carrier infrastructure to GPU clusters, deployed in-region.
Telnyx is the only platform combining:
When AI inference and telephony run on the same private network, you eliminate the transport overhead that exists in every other architecture. The audio enters Telnyx infrastructure and never leaves until it's ready to return to the caller.
| Capability | Full Build | Hybrid Build with Telnyx | Frankenstack | Telnyx Integrated |
|---|---|---|---|---|
| EU telephony | Must build | ✓ Telnyx | Varies by vendor | ✓ Frankfurt, Amsterdam, London |
| EU GPU inference | Must provision | Depends on vendor | External (US) | ✓ Paris cluster |
| Private network | Must build | Depends on vendor | Public internet | ✓ MPLS backbone |
| Carrier licensing | Must obtain | Via Telnyx | Via vendor | ✓ 30+ countries |
| Wideband codecs | Must implement | ✓ Telnyx | G.711 only | ✓ G.722 + Opus |
| Single DPA | N/A | 2-3 DPAs | 5+ DPAs | ✓ 1 DPA |
| Architecture | Typical RTT | Why |
|---|---|---|
| Full build (fragmented) | 600-1000ms | Separate servers for each layer |
| Hybrid build with Telephony provider | 720-950ms | External round-trips |
| Frankenstack (Vapi/Retell) | 1000-1500ms | 4-6 vendor hops, public internet |
| Telnyx integrated | <500ms | Co-located EU GPUs + telephony |
Ready to deploy production Voice AI in Europe? Contact our team to discuss your architecture, or explore Telnyx UK to see the full EU infrastructure.
