Cloud-flexible AI deployment
We deploy where you need us to: AWS, Google Cloud, Azure, regional providers (OVHcloud, Hetzner, Scaleway), or on-premise. Particularly relevant for European clients with data residency requirements.
Service
AI Infrastructure & Deployment
Production AI infrastructure built for the cloud you choose.
We build and run production AI infrastructure on the cloud you choose: token cost tracking, response caching, multi-model routing, drift monitoring, and on-premise hosting where compliance requires it. We're an AWS Partner, and we deploy where your business needs to operate.
KUMO ships production AI on AWS →self hosted n8n deployment →
Where we focus.
Six application areas where this service ships measurable results, chosen against the failure modes most growing businesses hit.
AI observability & monitoring
Distributed tracing, drift monitoring, latency alerts, cost dashboards. You know about problems before customers do.
AI cost optimisation
Token usage instrumented per workflow, caching layers, smaller-model routing for high-volume tasks. Typical engagement finds 30-60% savings.
Model serving & inference platforms
GPU pools with auto-scaling, vLLM and Triton for self-hosted models, batch and real-time queues. Built so a traffic spike doesn't bankrupt you or stall the product.
RAG infrastructure
Vector databases, ingestion and chunking pipelines, freshness automation, and an eval harness that catches retrieval regressions before users do.
Data residency & on-prem deployments
EU, UK, India, and Saudi data residency. Healthcare and fintech on-prem. Confidential-compute environments where sensitive data can't leave the customer's perimeter.
Reality check
AI that survives its own success
Most AI infrastructure problems only appear once something works. Usage grows, and three things arrive together: the model bill rises faster than revenue, latency becomes the product's worst feature, and quality drifts without anyone noticing because nothing is measuring it.
None of these show up in a pilot, which is one reason pilots convert to production so rarely. A pilot has ten users, no cost ceiling and no customer waiting on a response.
The layer that fixes it is unglamorous: token usage attributed per workflow so you know what is actually expensive, caching for repeated queries, routing high-volume low-difficulty tasks to smaller models, tracing so a slow request can be explained rather than guessed at, and evaluation that runs continuously so drift is caught by a machine instead of a customer.
Related work
Where we've shipped this in production.
B2B SAAS · OUR OWN PRODUCT CampaignHQ
AWS-native customer engagement platform we built, run, and operate at production scale. AWS Partner.
Read case study ->
FINTECH · YC S20 Volopay
Production fintech infrastructure operating across six countries: observability, compliance, and uptime expected of a $31M+ fintech.
Read case study ->
ENTERTAINMENT · UK Flickd
Production AI social platform built to handle real traffic and real customers. Infrastructure that scales with engagement, not just downloads.
Read case study ->What it costs
What this costs
The short answer
An AI cost and reliability audit runs 2 to 4 weeks and finishes with a written report. Infrastructure builds and migrations sit inside the $20K to $50K band, larger platform work $50K to $100K, and ongoing monitoring and DevOps runs as a $5K to $10K per month retainer. If your current setup is fine for your volume, the audit will say that.
Starter Build
$20K to $50K
4 to 16 weeks
A first production build, shipped end to end with evaluation and human review built in.
Grow Build
$50K to $100K
16 to 24 weeks
Multi-workflow or multi-feature platform work, re-engineered for scale.
Support and Growth Team
$5K to $10K per month
Ongoing, cancel with 30 days notice
The engineering team on retainer once it is live: new work, evals, and monitoring.
These are our three live engagement bands, the same across every KUMO service. Not sure which one fits? That is exactly what the first call is for.
Technologies and platforms
Assembled for your build.
FAQ
Do you only deploy on AWS? +
No. AWS is our most common cloud (we're an AWS Partner), but we deploy on Google Cloud, Azure, regional European providers (OVHcloud, Hetzner, Scaleway), and on-prem. Cloud choice belongs to the client.
Can you migrate us from another cloud or hosting? +
Yes. We migrate from Heroku, DigitalOcean, on-prem, or other clouds. Zero-downtime migrations with data preservation. 4-8 weeks depending on complexity.
How do you keep AI costs under control? +
Token usage is instrumented from day one. We use smaller models (Haiku, GPT-4o Mini) for high-volume tasks, implement caching for repeated queries, and set per-workflow cost budgets. Quarterly cost forecasts.
Do you handle compliance: HIPAA, SOC 2, PCI? +
We can implement to compliance requirements. We don't hold certifications ourselves, we work with your compliance team during scoping to architect the controls each regime demands.
What does a typical infrastructure engagement look like? +
Greenfield setup runs 2-4 weeks. Cloud or platform migration takes 4-8 weeks depending on data volume and downtime tolerance. An AI cost-and-reliability audit usually finishes in 2-4 weeks with a written report. Ongoing monitoring and DevOps support is structured as a monthly retainer.
How much does AI infrastructure work cost? +
A cost and reliability audit runs 2 to 4 weeks with a written report. Builds and migrations sit in the $20K to $50K band, larger platform work $50K to $100K, and ongoing monitoring runs $5K to $10K per month.
Our AI costs are rising faster than usage. Why? +
Usually one of four things: every request going to a frontier model regardless of difficulty, no caching on repeated queries, retrieval pulling more context than the answer needs, or retries on failure that nobody is counting. An audit tells you which.
What is RAG infrastructure and do we need it? +
Retrieval-augmented generation grounds answers in your own documents rather than model memory. You need it when answers must be current, citable, or specific to your data. You do not need it if a frontier model already knows enough.
How do you know if AI quality is degrading in production? +
Continuous evaluation. A fixed test set runs against the live system on a schedule and alerts when scores move, so drift is caught by monitoring rather than by a customer complaint.
Tell us what you're solving for.
We'll listen first, ask the right questions, and follow up with a clear proposal.