Lab Engineering

Custom Cloud Labs

Demo environments, POCs, and training labs — designed for your cloud and AI stack. Stop spending hours on environment setup.

What we build

Three lanes, one engineering practice

Same underlying skill — IAM scoping, Terraform lifecycle, sandbox isolation — applied to three buyer profiles.

Cloud Training Labs

Multi-tenant student sandboxes for AWS, Azure, and Databricks cohorts.

We design and ship IAM-scoped sandboxes that survive 30 simultaneous students hitting the same AWS account. Terraform-managed, region-locked, idempotent — the policy is the contract that prevents one student from touching another.

Buyer

L&D leaders, training providers, certification programs

What ships
  • Custom IAM policies scoped per learner
  • Terraform modules to spin up + tear down per cohort
  • Instructor walkthrough + student handout, ready to deliver
  • Cleanup tooling so the bill stays predictable

AI & GPU Labs

H100 / A100 + JupyterLab for AI training cohorts at neo-cloud prices.

Most lab platforms can't give 30 students each their own H100. We wire neo-cloud GPUs (Lambda Labs, Shadeform, RunPod) into cohort lifecycles, pre-baked with PyTorch / vLLM / Ollama / CUDA — 30–70% cheaper than hyperscaler GPU pricing and provisioned in under a minute per learner.

Buyer

AI training programs, fine-tuning workshops, vendor partner enablement

What ships
  • Per-learner GPU provisioning across neo-cloud providers
  • JupyterLab / VS Code Server with notebooks pre-loaded
  • Cost dashboards so finance sees what cohort #2 will run you
  • Auto-shutdown so a forgotten H100 doesn't burn the budget
Pricing
Contact us for pricing

Sales Demo Environments

Disposable, polished demo accounts your SEs spin up in five minutes.

Your Sales Engineers shouldn't be hand-building cloud accounts the night before a customer call. We build reusable demo environments — pre-loaded with the right data, the right narrative, and the right tear-down — so every SE can stand up the same demo in five minutes and break it without consequence.

Buyer

VPs of Sales Engineering, Partner Programs, Pre-Sales leadership

What ships
  • Reproducible demo environments per product / persona
  • Sample data + narrative scripts SEs walk through
  • One-click reset between demos so state never leaks
  • Optional: gated public POC links for prospect self-serve
Pricing
Contact us for pricing
Capability matrix

What you get in each lane

CapabilityCloud TrainingAI & GPUSales Demos
IAM / RBAC scoping per learner
Terraform-managed lifecycle
Multi-tenant on a single cloud account
Neo-cloud GPU integration
On request
Pre-loaded sample data + narrative
Optional
Optional
One-click reset between sessions
Cost guardrails + auto-shutdown
Instructor walkthrough + student handout
Why Lab Engineering

Different from a lab platform — on purpose

Skillable, Instruqt, and Strigo sell platforms. We engineer the labs your stack actually needs and hand them off — your team owns them.

We've built this in production, not in a deck

The IAM policies, Terraform modules, and PySpark ETL behind these labs are open-source on GitHub. You can read the code before signing anything.

Vendor-neutral across cloud and neo-cloud

AWS, Azure, GCP, Databricks, Lambda Labs, Shadeform, RunPod. We pick what makes economic sense for your cohort, not what makes our partner directory listing happy.

Built once, owned by you

Every engagement hands off Terraform, policies, and runbooks your team owns and re-runs. We're not a SaaS — there's no platform lock-in to walk away from.

Scales from 5 students to 500

Same architecture handles a private 5-person workshop or a 500-seat partner enablement program. Cleanup is the same regardless of cohort size.

Case studies

The work behind the pitch

Open source where possible. Anonymized when client confidentiality requires.

Cloud Training

AWS Data Lake — student sandbox for 30 simultaneous learners

Per-student IAM scoping on a single AWS account, with Glue ETL and Athena queries that survive cohort-scale concurrency.

Buyer

Fortune 500 corporate L&D team onboarding engineers to AWS

Read case study
Cloud TrainingComing soon

Databricks Text-to-SQL Agent — workshop lab on a shared workspace

Per-cohort Databricks workspace with Unity Catalog scoping, db-agent installed, ready for a 2-hour text-to-SQL workshop.

Buyer

Databricks training cohort + private corporate workshops

AI & GPUComing soon

30× H100 cohort — JupyterLab on neo-cloud for an AI fine-tuning workshop

Per-student H100 instance with JupyterLab and pre-loaded notebooks, provisioned across Lambda Labs and Shadeform for cost optimization.

Buyer

AI training programs, vendor partner enablement, fine-tuning workshops

More case studies in progress. Open-source companion code at github.com/becloudready/quick-labs.

FAQ

Common questions

How is this different from Skillable, Instruqt, or Strigo?+
Those are SaaS lab platforms — you rent infrastructure and content slots. Lab Engineering is custom-built infrastructure your team owns and re-runs. We use Terraform you keep, IAM policies tailored to your stack, and (for AI workloads) neo-cloud GPUs that platform incumbents don't natively support. Buy a platform when you need 100 off-the-shelf labs; hire us when you need labs that match your actual product or training program.
Can you deliver the training too, or just build the lab?+
Both. We design and ship the lab, and you can either run it yourself with the instructor walkthrough we hand off, or use our trainer network for delivery. Most engagements hand off the lab so the customer's team owns repeat delivery.
What's a typical timeline?+
Two to four weeks from scoping to first dry-run for a single-cohort training lab. Six to eight weeks for AI/GPU programs that involve neo-cloud provisioning. Sales demo environments often ship faster — one to three weeks.
Do you do small projects or just enterprise?+
Both. We've shipped scoped sales-demo templates as well as full multi-cohort training programs with custom IAM scoping. Book a discovery call and we'll scope what makes sense for your team — or fork our open-source repo if you'd rather self-serve.
Why neo-cloud GPUs instead of AWS / Azure / GCP?+
Cost. Neo-cloud H100s run 30–70% cheaper than hyperscaler equivalents, and for cohort training where each student needs an hour of GPU time, the math is decisive. We use hyperscalers when your IT team requires it for security or compliance — neo-cloud is the default when economics drive the decision.
Can the open-source code on GitHub be used directly?+
Yes — that's why it's open. The quick-labs repo has working examples of IAM policies, Terraform modules, and PySpark ETL. The companion case studies on this page walk through the design decisions. You can fork it. We just charge when you want it tailored, hardened, and delivered for your specific stack and cohort size.
Discovery call

Tell us what your team needs to learn or demo

30-minute call to scope the lab. We’ll tell you whether to hire us, fork the open-source repo, or use a SaaS platform — whichever actually fits.

Pick a time directly

Grab a 30-min slot on our calendar. Come with rough scope — we’ll work it into a concrete lab plan during the call.

  • 30-min scoping call
  • Honest read on whether to build, fork, or buy
  • Indicative timeline + pricing range by the end
Book a 30-min discovery call

Or send us details

Tell us about your stack and we’ll get back within one business day.

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