Self-hosting is not automatically cheaper
GPU cost, utilization, maintenance, reliability, and engineering time decide whether private serving wins.
Self-Hosted LLM Deployment
Self-hosted LLM deployment with break-even math first.
NavyaAI helps teams decide whether private LLM serving should exist, then designs the deployment path: model choice, quantization, vLLM or TensorRT-LLM serving, GPU sizing, latency targets, security boundaries, and operating cost.
Case signal
42% cost reduction
Throughput
2.3x improvement
Budget fit
$20K+ monthly AI spend
Latency and privacy shape the architecture
A private model can solve data concerns but still fail if throughput and response targets are wrong.
GPU sizing happens too early
Teams often choose hardware before measuring token volume, batchability, and model fit.
Audit Focus
What we inspect before prescribing a platform change.
The first pass is designed to identify the smallest useful intervention: routing, caching, prompt control, serving tuning, or a deeper break-even audit.
Self-host LLM vs API cost comparison
vLLM, TensorRT-LLM, and serving architecture fit
GPU sizing for latency, throughput, and VRAM
Quantization and model-size right-sizing
Private AI security, monitoring, and operations plan
Decision Map
Private LLM deployment map
The decision starts with economics, then moves to serving architecture.
| Decision | Risk | Audit question |
|---|---|---|
| Model size | Larger model than task requires | What quality floor is needed? |
| GPU class | VRAM or throughput mismatch | What batch and context shape is real? |
| Serving stack | Framework choice limits throughput | Does vLLM fit the workload? |
| Operations | Hidden maintenance and uptime cost | Who owns incidents? |
| Break-even | Private serving loses at low volume | What monthly tokens justify migration? |
Qualified Intake
Start with spend, provider, and workload shape.
The audit form routes teams below $20K/month toward self-serve estimators and routes qualified spend into follow-up.
FAQ
Common questions
When should a team self-host an LLM?
A team should evaluate self-hosting when traffic is predictable, token volume is high, privacy or data residency matters, and GPU utilization can beat managed API economics.
What is included in self-hosted LLM deployment?
A self-hosted LLM deployment usually includes model selection, quantization, serving stack setup, GPU sizing, monitoring, security controls, and cost/performance tuning.
Can NavyaAI deploy vLLM?
Yes. NavyaAI can evaluate and implement vLLM-style serving where it fits the model, latency target, batching pattern, and operational constraints.