MLOps and serving for production LLMs
We design and run the infrastructure that keeps AI features fast, cheap, and observable — GPU inference on vLLM and Triton, autoscaling, model routing, and the monitoring stack that catches drift before users do.
What production AI infrastructure needs
Model serving at scale is more than a container — it's inference-aware autoscaling, safe rollouts, and observability that speaks the language of prompts and tokens.
GPU inference serving
vLLM, TGI, and Triton tuned for your models, context sizes, and traffic — with paged attention and continuous batching where they help.
Autoscaling & batching
Inference-aware autoscalers, warm pools, and dynamic batching that respect GPU startup times and cost floors.
Blue/green model rollouts
Traffic splitting, shadow evaluation, and one-click rollback so a new model version can't take the product down.
Prompt & response logging
Structured logs of prompts, tool calls, and responses with retention and access controls that meet security review.
Drift & regression alerts
Alerts on input distribution shifts, eval score drops, and latency regressions — issues surface before user complaints do.
Cost dashboards
Per-endpoint, per-tenant, and per-feature cost views so finance and product can see where AI dollars actually go.
From first deployment to steady state
A four-phase engagement that gets serving on solid ground and stays with the platform as it grows.
Infra audit
We review current serving, model registry, monitoring, and cost — spot the highest-risk gaps before touching production.
Serving architecture
Design the inference stack: engine choice, GPU sizing, routing, autoscaling policies, and rollout strategy.
Observability rollout
Prompt logging, tracing, evaluation dashboards, and alerting — the visibility layer that makes everything else safe to change.
Ongoing SRE support
On-call rotations, incident review, cost reviews, and capacity planning as your traffic and model set grow.
The infrastructure we build on
Inference engines, orchestration, and observability chosen for real production loads, not just benchmarks.
Where MLOps moves the needle
Serving becomes a distinct discipline once AI is on the critical path of a real product.
High-throughput chat backends
Serving stacks tuned for streaming chat traffic with tight tail-latency targets and predictable unit cost.
Batch inference jobs
Nightly enrichment, embedding refreshes, and bulk generation runs on spot GPU capacity with retries and idempotency.
Multi-model routing gateways
A single API in front of many models — hosted and self-served — with routing, retries, and fallback on provider failure.
Fine-tuned model hosting
Serving your own weights alongside adapters, with fast switching, quantization, and monitoring by model version.
Cost-optimized rollouts
Traffic shaping, model routing, and prompt-caching strategies that pull real dollars out of an AI bill.
On-prem GPU clusters
Serving on your own GPUs — for data residency, compliance, or when hosted APIs no longer make economic sense.
What good MLOps buys you
Serious serving turns AI from a demo into a durable line of your product.
Lower per-request cost
Right-sized GPUs, batching, caching, and routing pull real dollars out of the inference bill at scale.
Higher throughput
Modern serving engines and inference-aware autoscaling let a fixed GPU budget handle far more real traffic.
Fewer incidents
Observability, canary deploys, and rollback plans turn model changes from a risk event into routine work.
Faster rollbacks
Version pinning, traffic shifting, and clean model registries mean recovering from a bad release takes minutes.
AI infrastructure in production
A grounded LLM product running on a serving stack built for real support traffic.
AI • RAG
NeuraDesk
A retrieval-augmented support copilot that grounds answers in a company's own knowledge base.
Read Case Study →Why teams pick us for AI infrastructure
SREs and platform engineers who've run LLM stacks in production, not just deployed a container.
Learn more about us →Real production experience
We've been on-call for AI systems, and it shows in the alerts, runbooks, and rollback plans we build.
Cost-honest architecture
We design for the traffic profile you actually have — no cargo-culted GPU counts and no invisible warm-pool spend.
Observability first
Metrics, traces, and prompt logs come online before the first production request, not after the first incident.
Own your stack — no vendor lock-in
Infrastructure is Terraform in your accounts, dashboards you own, and standard open engines — swap providers without a rewrite.
MLOps questions we hear most
Practical answers on serving, cost, and running AI systems reliably.
What GPU should we run this on?
How does autoscaling work for LLM serving?
How do we control warm-pool cost?
Can we route between multiple models?
On-prem or cloud?
What does the monitoring stack look like?
Ready to put your AI stack on solid ground?
Tell us what you're running now. We'll come back with an audit, a target architecture, and a realistic path to get there.
Book a Discovery Session →