JUSTOPS.IO Platform, FinOps & Private AI Engineering
✔ Private Cloud AI · DevSecOps · Cloud FinOps

Scale-to-Zero Private LLM Pipelines & Cloud Cost Control

Run open-weights AI security audits, automated code reviews, and self-hosted business cloud stacks at $0.00/hr idle compute — 100% inside your own cloud VPC or bare-metal Linux server without leaking source code to third-party APIs.

Explore Interactive Architecture ↓ Inspect Sample PR Security Audit View Architecture Deck View Fixed-Scope Packages
$0.00/hr
✔ Idle Compute Cost
Dual-layer CI teardown + Kubernetes TTL ceilings scale nodes to 0 immediately.
~$0.03
▲ Per CI Pipeline Run
Ephemeral Spot GPU/TPU burst with pre-warmed RAM-disk XLA model caches.
100% VPC
✔ Zero External APIs
Proprietary commits, secrets, and customer data never leave your tenancy.
Up to 95%
▲ Cloud Bill Reduction
Eliminate idle GPU instances, orphaned OCI/AWS volumes, and per-seat SaaS bloat.
▲ Reference Architecture & Execution Flow

How Ephemeral Cloud AI & DevSecOps Pipelines Work

Click any of the 5 pipeline stages below to inspect the underlying engineering mechanics, cold-start optimizations, and FinOps billing safeguards.

Targets: GCP GKE Spot TPU/GPU · Oracle Cloud (OCI) · AWS EKS · Rootless Linux
Stage 01: Fast Static SecScan & Secret Gate (<2 Seconds)

Before spending a single cent on cloud GPU/TPU compute, a deterministic Python AST & regex gate inspects the exact Git commit range (git diff HEAD~1..HEAD). It catches committed .env files, cloud API tokens, unvalidated filepath.Join / path.join traversals, unparameterized shell commands, and missing RBAC middleware headers, feeding structured JSON context forward into the LLM stage.

  • ✔ Zero-cost pre-filter runs directly on standard GitHub/GitLab/Gitea runner
  • ✔ Verifies .gitignore & .dockerignore hygiene across monorepos
  • ⚠ Pinpoints exact file & line numbers so the LLM prompt stays compact & fast
github-runner — ephemeral-llm-secops.yml (Job #418 · Spot TPU v5e-4 · us-west1)
[09:14:02] ✔ [STAGE 1] Static scanner inspected 14 files (312 diff lines) → ⚠ 2 High-Risk Sinks Flagged
[09:14:10] ✔ [STAGE 2] Smoke probe passed (port 8084 free, image built, GET /api/health → 200 OK in 380ms)
[09:14:12] ▲ [STAGE 3] Applying gke-spot-tpu-job.yaml (activeDeadlineSeconds: 900, Spot topology 2x2)...
[09:14:48] ✔ [STAGE 3] Restored pre-warmed XLA cache (178 MB → /dev/shm/xla_cache); vllm listening on :8000
[09:15:00] ✔ [STAGE 4] Private Qwen3-Coder audit complete → Posted remediation patch to PR #142
[09:15:03] ✔ [STAGE 5] Teardown Hook (if: always()): Deleted Job qwen-tpu-serve → Active Spot Nodes: 0 ($0.00/hr)
✔ Interactive Pull Request Demo

What Your Developers See on Every Pull Request

Switch between three real-world vulnerability classes below to see how the private model combines static analysis with contextual patch generation.

✔ Pull Request #142 feat(export): add invoice archive endpoint & report runner (main ← feat/invoice-export)
✔ Private VPC Inference • Run Cost: $0.03 • Post-Run Nodes: 0 ($0.00/hr)
GitHub Actions Workflow
ephemeral-gke-llm-secops.yml
✔ 1. Static SecScan Gate
AST & regex diff inspection
1.6s
✔ 2. Container Smoke Probe
Rootless build + /api/health
8.4s
▲ 3. GKE Spot TPU Spin-Up
vllm-tpu + XLA RAM cache
38.2s
⚠ 4. Private LLM Code Audit
Critical finding patched
11.4s
✔ 5. Scale-to-Zero Teardown
if: always() → 0 active nodes
3.1s
▲ EPHEMERAL RUNNER CONFIG
Auth:GitHub Secrets (OIDC)
Compute:Spot TPU v5e (2x2)
Hard Ceiling:TTL 900s Auto-Kill
Code Privacy:100% In-Tenancy
AI private-vllm-secops ✔ Self-Hosted Model · Zero External API Calls
⚠ VERDICT: BLOCKED (CRITICAL VULNERABILITY)

⚠ [CRITICAL] Shell Command Injection & Unsanitized Path Traversal — handlers/export.go:48

User-supplied query parameter r.URL.Query().Get("archive") is joined directly to the filesystem path without filepath.Base() and interpolated into exec.Command("sh", "-c", ...). An authenticated user can escape the export directory or execute arbitrary shell commands inside the container.

Suggested Remediation Patch — handlers/export.go ✔ Drop-in Safe Replacement
func HandleInvoiceExport(w http.ResponseWriter, r *http.Request) {
- target := filepath.Join("/data/exports", r.URL.Query().Get("archive"))
- cmd := exec.Command("sh", "-c", fmt.Sprintf("tar -czf %s.tar.gz %s", target, target))
+ cleanName := filepath.Base(r.URL.Query().Get("archive"))
+ if !validArchivePattern.MatchString(cleanName) {
+ http.Error(w, "invalid archive identifier", http.StatusBadRequest)
+ return
+ }
+ target := filepath.Join("/data/exports", cleanName)
+ cmd := exec.Command("tar", "-czf", target+".tar.gz", "--", target)
if err := cmd.Run(); err != nil {
✔ Model: Qwen3-Coder-32B (vLLM) ▲ Total Pipeline Time: 62.7s ▲ Spot Compute Cost: $0.03 ✔ Cluster State: Scaled to 0 ($0.00/hr)
▲ Production Blueprints

Under the Hood: Workflow, Kubernetes & Scanner Code

Inspect the actual patterns used to enforce hard billing ceilings, restore XLA compilation caches to RAM disk, and guarantee scale-to-zero cleanup.

JustOps Reference Implementation 
# GitHub Actions Workflow: Ephemeral GKE Spot TPU/GPU Security Audit
name: Ephemeral Private LLM SecOps
on:
  pull_request:
    branches: [ main ]

jobs:
  private-ai-secops:
    runs-on: ubuntu-latest
    timeout-minutes: 15
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 2

      - name: "Stage 1: Fast Static SecScan Gate (<2s)"
        run: python3 ./scanner/secscan.py --diff HEAD~1..HEAD --json > findings.json

      - name: "Stage 2: Authenticate to Cloud Cluster via GitHub Secrets"
        uses: google-github-actions/auth@v2
        with:
          credentials_json: ${{ secrets.GCP_SA_KEY }}

      - name: "Stage 3: Spin Up Ephemeral Spot TPU/GPU Job (900s Hard TTL)"
        run: |
          gcloud container clusters get-credentials "${{ secrets.GKE_CLUSTER }}" --region "${{ secrets.GKE_REGION }}"
          kubectl apply -f k8s/gke-spot-tpu-vllm-job.yaml
          kubectl wait --for=condition=Ready pod -l app=ephemeral-vllm --timeout=180s

      - name: "Stage 4: Run Private In-VPC LLM Audit & Post PR Review"
        run: |
          kubectl port-forward job/ephemeral-vllm-audit 8000:8000 &
          python3 ./scanner/secscan.py --ai-endpoint http://127.0.0.1:8000/v1 --findings findings.json --post-pr

      - name: "Stage 5: Guaranteed FinOps Scale-to-Zero Teardown ($0.00/hr)"
        if: always()
        run: |
          kubectl delete job ephemeral-vllm-audit --ignore-not-found=true
▲ Cloud FinOps & ROI Analysis

Cut Cloud & AI Infrastructure Bills by Up to 95%

Use the estimator below to compare an always-on cloud GPU + SaaS stack against an ephemeral Spot pipeline and self-hosted Linux cloud.

⚠ Typical Unoptimized Cloud + Per-Seat SaaS Burn: $2,700 / mo
✔ Ephemeral Spot AI Compute (550 runs/mo @ $0.03): $16.50 / mo
✔ Idle GPU/TPU Compute Cost (24/7): $0.00 / hr
Estimated Annual Savings:
$32,202 / yr
⚠ Common Cloud Trap #1

Always-On Cloud GPU & Unchecked OCI/AWS Tenancy

$1,450 – $3,800/mo
Billed 24/7 even when pipelines & teams are asleep
  • ⚠ 90%+ Idle Compute Waste: Dedicated GPU/compute shapes sitting idle between jobs.
  • ⚠ Orphaned Cloud Resources: Unattached block/boot volumes, idle NAT gateways & duplicate load balancers.
  • ⚠ No Hard Kill-Switches: A single stuck job or forgotten test cluster burns thousands per month.
▼ High Fixed Burn · Zero FinOps Guardrails
⚠ Common Cloud Trap #2

External SaaS AI APIs & Per-Seat Cloud Bloat

$600 – $2,500+/mo
Per-seat SaaS licenses + unpredictable token bills
  • ⚠ Proprietary Code Leaves VPC: Source diffs, credentials & customer docs sent to 3rd-party APIs.
  • ⚠ Rate-Limit Bottlenecks: 429 quota exhaustion stalls automated security sweeps & CI builds.
  • ⚠ Recurring SaaS Tax: Paying monthly per user for files, CRM, billing & chat you could own.
▼ Compliance Risk · Endless Per-Seat Fees
✔ JustOps Architecture

Self-Hosted Private Cloud + Scale-to-Zero Spot AI

$0.00/hr Idle (~$0.03/run)
Pay only for active seconds of Spot GPU/TPU or fixed bare-metal Linux
  • ✔ 100% Private In-Tenancy AI: Open-weights vLLM / Ollama models inside your own VPC or on-site server.
  • ✔ Pre-Warmed Cold Starts: Cached model & XLA graphs boot Spot nodes in seconds, not 15 minutes.
  • ✔ Dual-Layer Billing Protection: Automated if: always() teardown + hard K8s TTL & budget alerts.
  • ✔ Hardened Rootless Containers: SSO, TLS, nightly encrypted backups & zero-downtime deploys.
✔ 100% Data Ownership · Up to 95% Lower Monthly Spend
✔ ThinkSpace Deep-Dive Documentation

Engineering Whitepapers & Architectural Notes

Select a technical brief below (rendered live from Markdown via Goldmark GFM).

Goldmark GFM ThinkSpace Engine

Solving the 15-Minute Cold-Start: Pre-Warmed XLA & Model Graphs on Spot TPUs/GPUs

Author: Justin Mecham — JustOps (justops.io)
Copyright: © 2024–2026 Justin Mecham / JustOps (justops.io). All Rights Reserved.

1. The Idle Cloud AI Billing Problem

Most engineering teams trying to adopt private open-weights LLMs (Qwen3-Coder, Llama-3.3, DeepSeek-R1) fall into one of two expensive traps:

  1. Always-On Dedicated GPU/TPU Instances ($1,450–$3,800/mo): Leaving an A100, H100, or Cloud TPU node running 24/7 when CI/CD pipelines and developers only run inference for 15–45 minutes per workday wastes over 90% of compute spend.
  2. Naive Ephemeral Spin-Up (12–18 Minute Cold Starts): Spinning up a Spot TPU or GPU node on-demand saves money, but standard vLLM / PyTorch XLA initialization compiles tensor graphs from scratch on every boot—blocking pull request pipelines for 15+ minutes.

2. Pre-Warmed XLA Graph Restoration to /dev/shm

To achieve ~$0.03 per pipeline run with sub-minute readiness, the JustOps architecture decouples graph compilation from ephemeral job execution:

Cold-Start Bottleneck Naive Ephemeral Boot JustOps Pre-Warmed Spot Pipeline
Compute Provisioning On-Demand GPU ($3.60+/hr) Preemptible / Spot TPU v5e or L4/A100 (60–75% off)
XLA / CUDA Graph Init Recompiled every boot (600–900s) Restored from compressed archive into /dev/shm (~4s)
Model Weight Load Pulled over public internet Streamed from regional VPC bucket / local NVMe cache
Post-Run Idle Billing Manual teardown (prone to leaks) Dual-layer if: always() + activeDeadlineSeconds ($0.00/hr)

Key Implementation Mechanics

# Restore pre-compiled XLA cache directly into shared memory RAM disk before launching vLLM
mkdir -p /dev/shm/xla_cache
tar -I zstd -xf /mnt/model-cache/qwen3-32b-tpu-2x2-xla.tar.zst -C /dev/shm/xla_cache
export VLLM_XLA_CACHE_PATH=/dev/shm/xla_cache

exec vllm serve Qwen/Qwen3-32B-AWQ \
  --tensor-parallel-size=4 \
  --max-model-len=32768 \
  --port=8000

Because /dev/shm is backed by host RAM, vLLM bypasses disk I/O contention during graph deserialization and begins serving OpenAI-compatible (/v1/chat/completions) and Anthropic-compatible (/v1/messages) requests immediately.

✔ Fixed-Scope Consulting & Implementation

Engage Directly via Fiverr

Delivered by a Staff Platform Software Engineer & SRE with 10+ years operating 2,000+ node enterprise Oracle Cloud (OCI), Linux, and Kubernetes environments.

AI Implementation & DevSecOps3 – 21 Days

Private AI Server & Scale-to-Zero Cloud GPU/TPU Pipelines

Deploy private open-weights LLMs (Ollama, vLLM, Qwen3-Coder, Llama, DeepSeek) on-site, on a dedicated GPU box, or as an ephemeral scale-to-zero CI/CD pipeline in your cloud VPC.

Workstation AI: $400 Team AI / CI Pipeline: $1,500 Full FinOps + Deploy: $5,000
View Private AI Gig on Fiverr ↗
Oracle Cloud (OCI) FinOps3 – 21 Days

Cut Your Oracle Cloud (OCI) Bill by Removing Waste & Duplicates

Read-only tenancy audit and cleanup of idle compute shapes, unattached block/boot volumes, duplicate load balancers, oversized DB systems, and automated stop/start Terraform FinOps guardrails.

Cost Audit: $500 Audit + Cleanup: $1,500 Full FinOps Setup: $4,000
View OCI Cost Audit Gig on Fiverr ↗
Self-Hosted Linux Cloud3 – 21 Days

Self-Hosted Company Cloud on Your Own Linux Server

Replace recurring per-seat SaaS bills with a hardened rootless Podman/Docker stack on your own server: Nextcloud files, Authentik SSO, Invoice Ninja billing, EspoCRM, Vaultwarden, mail, TLS, and nightly encrypted backups.

Starter Cloud: $600 Business Cloud: $1,800 Full Company Cloud: $5,000
View Self-Hosted Cloud Gig ↗
Turnkey Managed Cloud3 – 14 Days

Managed Private Company Cloud with Billing, CRM & Files

A fully isolated, monitored, and backed-up private company cloud environment hosted and maintained for your team — complete with SSO, files, invoicing, CRM, password vault, and zero-downtime updates.

3 Months Hosted: $900 6 Months Hosted: $2,400 12 Months Hosted: $6,000
View Managed Cloud Gig ↗