[ On-Premises Voice Infrastructure Deployment ]
Run the whole voice stack inside your own perimeter.
Self-hosted STT, LLM, and TTS on your private GPU or Kubernetes, so regulated data never leaves your network and cost stays predictable as call volume grows.
[ Why on-premises ]
Some data can't go to a third-party API, and some economics don't either.
For regulated workloads, managed cloud is a non-starter. At high volume, per-minute API fees quietly become the largest line item: end-to-end voice platforms can cost 10-100x more than the same capability on dedicated infrastructure. On-prem solves both problems: data stays put, and cost flattens as you scale.
- Data residency
- HIPAA, FINRA, and GDPR data that legally cannot leave your perimeter.
- Cost at scale
- Per-minute API fees overtake self-hosting past a volume threshold.
- Latency
- Every cross-vendor API hop costs 150-300ms per turn.
[ The latency budget ]
Voice has a physics problem APIs can't fix.
A natural conversation gives the whole listen-think-speak loop about a second. Where that second goes decides whether self-hosting is worth it.
| Component | What it costs |
|---|---|
| Human turn-taking | 200-300ms between speakers in natural conversation. That instinct is the bar voice agents are judged against: past about a second of silence, the exchange starts to feel like a phone menu. |
| Each cross-vendor API hop | 150-300ms of network round-trip, per provider, per turn. Chain three vendors and the budget is gone. |
| Colocated inference | Single-digit milliseconds between STT, LLM, and TTS running on the same infrastructure. |
| Voice-to-voice target | Under one second, sustained under production load – not in a single-stream demo. |
[ Deployment models ]
Matched to your risk level, not a default.
Serious on-prem voice AI is not plug-and-play. It starts with an assessment, and the right answer is sometimes a lighter model than you expected.
| Deployment model | Best for |
|---|---|
| Managed cloud | Fast pilots and standard production launches. |
| Private cloud / VPC | Higher control, private networking, regulated workflows. |
| Regional data residency | Workloads where data must stay in a specific jurisdiction: GDPR, EU operations, or local regulatory regimes. |
| Hybrid | Sensitive workloads on-prem, elastic capacity in the cloud. |
| Full on-premises | Enterprise, legal, healthcare, and finance with strict data control. |
[ What you own ]
Own the whole voice stack.
- Self-hosted open-weight STT, LLM, and TTS
- Self-hosted VAD and turn-detection models
- Private GPU or Kubernetes cluster
- No per-minute third-party API fees at scale
- Data never leaves your perimeter (HIPAA, FINRA, GDPR)
- Latency under your control
- Model and version pinning, upgrades on your schedule
[ What we deploy ]
Every layer, inside your environment.
| Layer | What we stand up |
|---|---|
| Inference | Self-hosted STT, LLM, and TTS on your GPU or cluster. |
| GPU fleet | Pools split by compute profile: a streaming STT model under 2GB serves dozens of concurrent streams on an entry-level GPU, while TTS dominates the bill. Splitting lets each scale independently and isolates failures. |
| Orchestration | Routing, fallback, and provider abstraction across the stack. |
| Telephony | SIP, VoIP, and PBX integration inside your network. |
| Data | Encrypted storage, retention rules, and audit logging. |
| Ops | Monitoring, scaling, latency tracking, and upgrade path. |
[ Vendor-neutral stack ]
Built from the components that fit, not one vendor's menu.
We choose per constraint: language coverage, latency, hardware, and compliance. Every layer stays swappable.
Self-hosted STT
NVIDIA Nemotron ASR 
Whisper / faster-whisper 
Qwen-STT Self-hosted TTS
Qwen TTS CosyVoice 
Kyutai TTS 
Chatterbox Kokoro Self-hosted LLMs
Llama Qwen 
Mistral NVIDIA Nemotron GPT-OSS Voice activity & turn detection
Serving & inference
vLLM TensorRT-LLM NVIDIA Triton NVIDIA Riva 
Kubernetes 
Docker Speech providers with self-hosted options
Deepgram 
AssemblyAI 
Cartesia 
Speechmatics 
Azure Speech containers Transport, telephony & orchestration
WebRTC 
LiveKit 
Daily 
Pipecat SIP / PBX 
Asterisk 
FreeSWITCH 
Twilio 
Jambonz Avatar layer, when the agent needs a face
Three.js / TalkingHead 
Simli 
Anam.ai NVIDIA ACE [ Why it's not procurement ]
The open-source speech landscape is a minefield.
Self-hosting a voice stack sounds like a procurement decision. It isn't. These are the traps we screen for before a single GPU is provisioned – because published benchmarks are not deployed reality.
- Models that miss their published benchmarks once deployed
- Single-stream latency that collapses past a handful of concurrent streams
- Open weights under licenses that quietly prohibit commercial use
- Streaming and batching locked behind paid enterprise serving containers
- Strong models shipped with broken or community-only serving stacks
- The only reliable test: production-realistic concurrent load, with stall tracking at the audio-frame level
[ When on-prem fits ]
The drivers that justify it.
Not every workload needs full on-prem. The assessment often lands on private cloud or regional residency. These are the drivers that justify going further.
| Driver | Why on-prem wins |
|---|---|
| Compliance | Regulated data cannot legally leave your environment. |
| Cost at scale | Per-minute API fees overtake self-hosting past a volume threshold. The math: GPU hourly rate ÷ concurrent streams ÷ 60 = cost per minute. In our benchmarks, self-hosted streaming STT runs 17-50x cheaper than commercial APIs at saturation. |
| Latency | Local inference removes third-party network round-trips: each one costs 150-300ms per turn. |
| Control | You pin models and upgrade, customize, or fine-tune them for your domain on your own schedule. |
| Existing telephony | Your SIP, PBX, and VoIP systems stay. We integrate, not replace. |
[ Security and control ]
You keep control of the data.
- Data stays inside your perimeter
- Encrypted storage and access control
- Role-based access and audit logs
- Configurable data-retention policies
- Regional data-residency options: GDPR, EU, and other jurisdictions
- SOC 2 readiness where relevant
[ How a deployment runs ]
From assessment to handover.
-
1–2 weeks
Assessment
Map the use case, data sensitivity, call flows, systems, and compliance needs.
-
1–2 weeks
Architecture
Choose the deployment model, providers, routing, storage, and monitoring plan.
-
2–4 weeks
Pilot
Validate the setup with controlled real or simulated calls.
-
2–4 weeks
Deployment
Stand up infrastructure, integrations, access controls, and monitoring.
-
1–2 weeks
Testing
Latency, reliability, failover, security, and call-quality verification.
-
1 week
Handover
Documentation, dashboards, runbooks, and a support process your team owns.
[ What you receive ]
An operation your team owns, not a black box.
- Architecture blueprint and deployment plan
- Integration map for your business systems
- Security and data-flow documentation
- Monitoring dashboard and alerts
- Multi-provider failover plan
- Cost and latency model
- Deployment runbook
- Support and incident process
[ Proven in production ]
Infrastructure work we've shipped.
[ Common questions ]
Asked before most deployments.
| Question | Answer |
|---|---|
| Do we need full on-prem from day one? | Not always, and rarely all at once. The assessment often lands on private cloud, VPC, or regional residency, and most deployments stage the move: speech layers first, where the per-minute savings are largest, then transport and the LLM as volume justifies. We instrument from day one so real conversations become fine-tuning data for the later stages. |
| Will it work with our existing phone system? | In most cases yes: SIP, PBX, and VoIP setups, plus providers like Twilio, RingCentral, Dialpad, and Vonage. |
| Why not just use a platform like Bland, Vapi, or Retell? | Platforms are one layer of the stack. We design the architecture, integrate your systems, and keep you free to swap providers. |
| What actually stays inside our perimeter? | In a full on-prem model: audio, transcripts, model weights, logs, and analytics. Hybrid splits this deliberately, and we document exactly where data flows. |
| How do we know self-hosting will actually be cheaper? | Concurrency is the denominator of every self-hosting cost model, and vendor benchmarks routinely overstate it. We measure the real ceiling per GPU under production-realistic load before you commit to hardware. |
| Is air-gapped possible? | Where technically feasible and justified. It constrains model updates and monitoring, so we scope it honestly. |
6 yrs
in complex B2B software
20+
experts across AI, product, design, and engineering
4.9/5
average client satisfaction
5+
industries: SaaS, hospitality, LegalTech, MarTech, support
"What truly stood out was Softcery's deep AI expertise. They were able to take our vision and turn it into a reality, and the final product has exceeded our expectations. Working with Softcery has been a game-changer for our business."
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Managing Director, The Compliance Company & Upskill AI
"Softcery is not your typical software development agency – they're a full-scale product consultancy. The benefit of working with them is the collaboration."
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Founder, Bullseye