Hire AI Model Deployment Expert — inference infrastructure that scales without surprises

Managed APIs win on speed to market and zero ops burden; self-hosting wins when per-request costs at your volume exceed GPU costs, or when data cannot leave your network. We run the numbers on your traffic before recommending.

For LLM inference: time-to-first-token under a second and tens of tokens per second are achievable with proper serving on good hardware. Your exact budget depends on model size and GPU choice — we benchmark it.

With a mix: autoscaling for sustained growth, request queueing with graceful degradation for bursts, and semantic caching that absorbs repeated queries without touching the GPU.

Done properly, barely — 4-bit quantization typically costs single-digit percentage points on benchmarks, which often does not matter for your task. We verify against your evals, not public benchmarks, before accepting any trade.

Yes — AWS, GCP, Azure, or your own hardware. The serving stack is portable; what changes is procurement, networking, and how we handle GPU availability.

I usually work on a fixed-scope project fee for a defined deployment, or a day rate for ongoing tuning and hardening. Before quoting I want to see your model, traffic shape and your latency and cost targets, because a single-model endpoint and a multi-tenant platform are very different jobs. Most first engagements are scoped so the inference spend they save covers the fee within a few months.

A single model with a clear quality bar and existing cloud access can be serving real traffic in two to three weeks, including load testing and a canary rollout. Multi-model platforms, on-prem hardware or strict compliance review take longer, mostly because of procurement and networking rather than the serving work itself. I scope the critical path up front so you know exactly what gates the date.

Alongside, almost always. Your team owns the model, the training data and the evals; I own getting it to run fast, cheaply and reliably in production, and I leave the stack documented so they can operate it without me. When people hire an AI model deployment expert they rarely want a permanent dependency, they want the deployment problem solved and the knowledge transferred.

It depends on your priorities. vLLM is fast to adopt, flexible across models and excellent for throughput with continuous batching; TensorRT-LLM can squeeze out lower latency on NVIDIA hardware but costs more engineering time and is less forgiving when you swap models. For most teams I start on vLLM and only move to TensorRT-LLM where the latency gain is measured and genuinely needed.

Yes. I deploy embedding models, rerankers, speech-to-text, vision and classical models alongside LLMs, often on the same platform. The serving concerns are similar, batching, GPU sizing, autoscaling and observability, but the optimal framework differs, so vision models might run on Triton or ONNX Runtime while LLMs run on vLLM. Tell me the full set of models and I will design one coherent serving layer.

You get the serving code, infrastructure-as-code, dashboards, load-test results, a runbook and the key tuning decisions written down, so nothing lives only in my head. Many clients then keep me on a light retainer for new model versions, capacity planning and incident support. Others take it fully in-house. Either way the handover is designed so the deployment outlives the engagement.