Ship AI Features Without the Runaway Bill.
We build hybrid AI infrastructure — frontier models for the hard reasoning, your own local models for the high-volume work — so your team ships LLM-powered features and cuts inference spend 60–80%. No ML platform hire needed.
Sound Familiar?
AI works in the demo, then gets expensive and opaque in production. You shouldn't have to choose between shipping it and controlling the bill.
“Our AI bill is out of control”
Every feature ships another frontier API call, and the monthly invoice keeps climbing. You're paying Claude and GPT prices for high-volume work a local model could do for the cost of electricity.
“Our AI demo never made it to production”
The proof of concept works on a laptop. But nobody knows how to deploy it reliably, scale the inference, or keep its costs predictable once real traffic hits.
“We can't see what AI is costing us”
Tokens, latency, cost per request — it's all a mystery until the bill arrives. You can't optimize what you can't measure, so spend only goes one direction.
Fixed-Price Packages
No hourly billing surprises. Pick a package, get a clear scope, and know exactly what you're paying before we start.
AI Cost & Infrastructure Audit
1 week
$2,500
A focused review of your AI spend and the infrastructure around it — where you're overpaying for frontier models, what could run locally, and how to make costs predictable. Includes a clear, prioritized report.
- ✓AI / LLM cost analysis & token spend breakdown
- ✓Hybrid routing opportunities (what can move to local models)
- ✓Cloud & GPU cost analysis (AWS, etc.)
- ✓Inference architecture & observability review
- ✓Security posture check
CI/CD & Model Deployment Pipeline
1-2 weeks
$5,000
A production-grade pipeline for your application — and your models. From code push to live across dev, staging, and production, including rolling out self-hosted inference if you run it. We use the right tool for your stack.
- ✓Automated build & test pipeline
- ✓Multi-environment deployments (dev/staging/prod)
- ✓Self-hosted model rollout (vLLM / Ollama) where needed
- ✓Infrastructure as Code (Terraform, Helm)
- ✓Monitoring, alerting & cost tracking basics
Hybrid AI Feature Sprint
2 weeks
$7,500
Add AI capabilities to your product on a cost-efficient hybrid stack — frontier models for reasoning, self-hosted local models for the high-volume work — shipped, deployed, and instrumented so you can see what it costs.
- ✓Requirements scoping & hybrid model selection
- ✓Hybrid routing design (frontier + local)
- ✓Self-hosted inference setup (vLLM / Ollama)
- ✓RAG / LLM / agent integration development
- ✓Cost-per-request observability & production deployment
Monthly Retainer
Ongoing
$3,000/mo
Ongoing AI infrastructure support, hybrid model work, and cost optimization — like having a senior AI infrastructure engineer on your team without the full-time salary.
- ✓15 hours/month of AI & infrastructure work
- ✓Monthly AI cost & performance reviews
- ✓Responsive support for inference & infrastructure issues
- ✓Model selection & routing guidance
- ✓CI/CD, deployment, and AI feature improvements
- ✓Security updates & patching
Cost-Efficient AI, By Design
Most teams overpay by routing everything to frontier models. We architect a hybrid stack that sends each task to the cheapest model that can do it well — and lets you see exactly what it costs.
[ Hybrid Routing ]
Cloud for thinking, local for doing
Frontier models for planning and complex reasoning; local models for the 60–80% of work that's high-volume and well-defined. You keep frontier quality where it matters and pay pennies everywhere else.
[ Self-Hosted Inference ]
Own your models
Run open models on your own hardware or cloud GPUs. The marginal cost per token approaches the cost of electricity — and your proprietary data never leaves your infrastructure.
[ Cost Observability & Foundations ]
Measure every token
Every request traced: tokens, latency, GPU utilization, and cost per feature. It all runs on the production infrastructure we've always built — Kubernetes, CI/CD, Terraform — so your AI stack stays reliable, documented, and yours to own.
How It Works
Book a Call
Tell us what’s broken, expensive, or missing. 30 minutes, no obligation, no sales pitch.
Get a Proposal
We scope the work, pick the right package, and send you a clear proposal with a fixed price. No surprises.
We Ship It
We do the work, keep you informed, and hand off with documentation so your team can maintain it.
Latest from the Blog
Practical insights on running AI cost-efficiently — hybrid models, self-hosting, and inference observability — from the problems we solve every day.
The Cost-Efficient AI Stack: Ship AI Features Without the Runaway Bill
Most teams overpay for AI by routing every request to a frontier model. This is the architecture we build instead — hybrid cloud+local routing, self-hosted inference, agent orchestration, and cost-per-request observability — and the single principle that ties it together: send each unit of work to the cheapest model that can do it well.
Build a Personal AI Dev Environment: Hybrid Models, Local Inference, and a Workflow That Costs Almost Nothing
The production patterns we deploy for teams — hybrid cloud/local routing, self-hosted models, agent orchestration — scaled down to a single developer's workstation. A practical guide to building a personal AI dev environment with Ollama, Claude Code, and a local router that keeps your token bill near zero.
The Agent Control Plane: Frontier Models Plan, Your Kubernetes Fleet Executes
How to orchestrate a fleet of AI agents using a shared task queue — frontier models like Claude handle planning and decomposition, while a local Kubernetes worker pool runs the high-volume execution tasks. Covers the task ledger, dynamic task creation, lane-based routing, and KEDA autoscaling.
Let's Cut Your AI Bill
Book a free 30-minute call. We'll look at what you're spending on AI today and map the fastest path to hybrid infrastructure that costs a fraction as much.
Book a Free Call