nthlayer 0.1.0a8

NthLayer - The Missing Layer of Reliability

NthLayer

The Missing Layer of Reliability

Reliability requirements as code.

NthLayer lets you define what "production-ready" means for a service, then generates, validates, and enforces those requirements automatically.

Define once. Generate everything. Block bad deploys.

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The Problem

For every new service, teams are expected to:

• Manually create dashboards
• Hand-craft alerts and recording rules
• Define SLOs and error budgets
• Configure incident escalation
• Decide if a service is "ready" for production

These decisions are usually made after deployment, enforced inconsistently, or revisited only during incidents.

The Solution

NthLayer moves reliability left in the delivery lifecycle:

┌─────────────────────────────────────────────────────────────────────────────┐
│ service.yaml → generate → lint → verify → check-deploy → deploy            │
│                   ↓         ↓       ↓           ↓                          │
│               artifacts   valid?  metrics?  budget ok?                     │
│                                                                            │
│ "Is this production-ready?" - answered BEFORE deployment                   │
└─────────────────────────────────────────────────────────────────────────────┘

# In your Tekton/GitHub Actions pipeline:
nthlayer apply service.yaml --lint    # Generate + validate PromQL syntax
nthlayer verify service.yaml          # Verify declared metrics exist
nthlayer check-deploy service.yaml    # Check error budget gate
# Only if all pass: deploy to production

Works with: Tekton, GitHub Actions, GitLab CI, ArgoCD, Mimir/Cortex

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🚦 Shift Left Features

Command	What It Does	Pipeline Exit Code
nthlayer verify	Validates declared metrics exist in Prometheus	1 if missing metrics
nthlayer check-deploy	Checks error budget - blocks if exhausted	2 if budget exhausted
nthlayer apply --lint	Validates PromQL syntax with pint	1 if invalid queries

Deployment Gate Example

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⚡ Quick Start

pipx install nthlayer

nthlayer apply service.yaml

# Output: generated/payment-api/
#   ├── dashboard.json       → Grafana
#   ├── alerts.yaml          → Prometheus
#   ├── slos.yaml            → OpenSLO
#   └── recording-rules.yaml → Prometheus

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What NthLayer Is

• A reliability specification that defines production-readiness
• A compiler from service intent to operational reality
• A CI/CD-native way to standardize reliability across teams

NthLayer integrates with existing tools (Prometheus, Grafana, PagerDuty) but operates before them - deciding what is allowed to reach production.

What NthLayer Is Not

• Not a service catalog
• Not an observability platform
• Not an incident management system
• Not a runtime control plane

NthLayer complements these systems by ensuring services meet reliability expectations before they are deployed.

Why NthLayer?

With NthLayer	Without NthLayer
Platform teams encode reliability standards once	Standards recreated per service
Service teams inherit sane defaults automatically	Each team invents their own
"Is this production-ready?" = deterministic check	"Is this ready?" = negotiated opinion
Reliability is enforced by default	Reliability is reactive and inconsistent

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📥 What You Put In

1. Service Spec (service.yaml)

# Minimal example (5 lines)
name: payment-api
tier: critical
type: api
dependencies:
  - postgresql

2. Environment Variables (optional)

# 📟 PagerDuty - auto-create team, escalation policy, service
export PAGERDUTY_API_KEY=...

# 📊 Grafana - auto-push dashboards
export NTHLAYER_GRAFANA_URL=...
export NTHLAYER_GRAFANA_API_KEY=...
export NTHLAYER_GRAFANA_ORG_ID=1              # Default: 1

# 🔍 Prometheus - metric discovery for intent resolution
export NTHLAYER_PROMETHEUS_URL=...
export NTHLAYER_METRICS_USER=...              # If auth required
export NTHLAYER_METRICS_PASSWORD=...

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📤 What You Get Out

Output	File	Deploy To
📊 Dashboard	generated/<service>/dashboard.json	Grafana
🚨 Alerts	generated/<service>/alerts.yaml	Prometheus
🎯 SLOs	generated/<service>/slos.yaml	OpenSLO-compatible
⚡ Recording Rules	generated/<service>/recording-rules.yaml	Prometheus
📟 PagerDuty	Created via API	Team, escalation policy, service

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📊 SLO Portfolio

Track reliability across your entire organization:

nthlayer portfolio              # Org-wide reliability view
nthlayer portfolio --format json  # Machine-readable for dashboards
nthlayer slo collect service.yaml  # Query current budget from Prometheus

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📝 Full Service Example

name: payment-api
tier: critical              # critical | standard | low
type: api                   # api | worker | stream
team: payments

slos:
  availability: 99.95       # Generates Prometheus alerts
  latency_p99_ms: 200       # Generates histogram queries

dependencies:
  - postgresql              # Adds PostgreSQL panels
  - redis                   # Adds Redis panels
  - kubernetes              # Adds K8s pod metrics

pagerduty:
  enabled: true
  support_model: self       # self | shared | sre | business_hours

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💰 The Value

Generation: 20 hours → 5 minutes per service

Task	Manual Effort	With NthLayer
🎯 Define SLOs & error budgets	6 hours	Generated from tier
🚨 Research & configure alerts	4 hours	400+ battle-tested rules
📊 Build Grafana dashboards	5 hours	12-28 panels auto-generated
📟 PagerDuty escalation setup	2 hours	Tier-based defaults
📋 Write recording rules	3 hours	20+ pre-computed metrics

Validation: Catch issues before production

Problem	Without NthLayer	With NthLayer
Missing metrics	Discover after deploy	nthlayer verify blocks promotion
Invalid PromQL	Prometheus rejects rules	--lint catches in CI
Policy violations	Manual review	nthlayer validate-spec enforces
Exhausted budget	Deploy anyway, incident	check-deploy blocks risky deploys

At Scale

Scale	Generation Saved	Incidents Prevented*
🚀 50 services	996 hours ($100K)	~12/year
📈 200 services	3,983 hours ($400K)	~48/year
🏢 1,000 services	19,917 hours ($2M)	~240/year

_{*Estimated based on 60% reduction in "missing monitoring" incidents. Value at $100/hr engineering cost.}

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🧠 How It Works

Generation

Step	What Happens
🎯 Intent Resolution	Maps "availability SLO" → best matching PromQL query
🔀 Type Routing	API services get HTTP metrics, workers get job metrics
⚡ Tier Defaults	Critical = 99.95% SLO + 5min escalation, Low = 99.5% + 60min
🏗️ Technology Templates	23 built-in: PostgreSQL, Redis, Kafka, MongoDB, etc.

CI/CD Pipeline

┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│   Generate  │───▶│   Validate  │───▶│   Protect   │───▶│   Deploy    │
│ nthlayer    │    │ --lint      │    │ check-deploy│    │ kubectl     │
│ apply       │    │ verify      │    │             │    │ argocd      │
└─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘
      │                  │                  │
      ▼                  ▼                  ▼
  artifacts         exit 1 if          exit 2 if
  to git            invalid            budget exhausted

Works with: GitHub Actions, GitLab CI, ArgoCD, Tekton, Jenkins

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🛠️ CLI Commands

Generate

nthlayer init                   # Interactive service.yaml creation
nthlayer plan service.yaml      # Preview what will be generated
nthlayer apply service.yaml     # Generate all artifacts
nthlayer apply --push           # Also push dashboard to Grafana
nthlayer apply --push-ruler     # Push alerts to Mimir/Cortex Ruler API

Validate

nthlayer apply --lint           # Validate PromQL syntax (pint)
nthlayer validate-spec service.yaml  # Check against policies (OPA/Rego)
nthlayer verify service.yaml    # Verify metrics exist in Prometheus

Protect

nthlayer check-deploy service.yaml  # Check error budget gate (exit 2 = blocked)
nthlayer portfolio              # Org-wide SLO health
nthlayer slo collect service.yaml   # Query current budget from Prometheus

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🔮 Coming Soon

Feature	Description	Status
💰 Error Budgets	Track budget consumption, correlate with deploys	✅ Done
📊 SLO Portfolio	Org-wide reliability view across all services	✅ Done
🚦 Deployment Gates	Block deploys when error budget exhausted	✅ Done
✅ Contract Verification	Verify declared metrics exist before promotion	✅ Done
📝 Loki Integration	Generate LogQL alert rules, technology-specific log patterns	🔨 Next
🤖 AI Generation	Conversational service.yaml creation via MCP	📋 Planned

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📦 Installation

# Recommended
pipx install nthlayer

# Or with pip
pip install nthlayer

# Verify
nthlayer --version

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🌐 Live Demo

See NthLayer in action with real Grafana dashboards and generated configs:

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📚 Documentation

Full Documentation - Comprehensive guides and reference.

Quick Links
🚀 Quick Start	Get running in 5 minutes
🔧 Setup Wizard	Interactive configuration
📊 SLO Portfolio	Org-wide reliability view
🔌 18 Technologies	PostgreSQL, Redis, Kafka...
📖 CLI Reference	All commands
🤝 Contributing	How to contribute

Build docs locally

pip install -e ".[docs]"
mkdocs serve  # Opens at http://localhost:8000

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🤝 Contributing

git clone https://github.com/rsionnach/nthlayer.git
cd nthlayer
make setup    # Install deps, start services
make test     # Run tests (84 should pass)

See CONTRIBUTING.md for details.

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📄 License

MIT - See LICENSE.txt

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🙏 Acknowledgments

Core Dependencies

• grafana-foundation-sdk - Dashboard generation SDK (Apache 2.0)
• awesome-prometheus-alerts - 580+ battle-tested alert rules (CC BY 4.0)
• pint - PromQL linting and validation (Apache 2.0)
• conftest / OPA - Policy validation (Apache 2.0)
• PagerDuty Python SDK - Incident management integration (MIT)

Architecture Inspiration

• autograf - Dynamic Prometheus metric discovery
• Sloth - SLO specification and burn rate calculations
• OpenSLO - SLO specification standard

CLI & Documentation

• Rich - Terminal formatting and styling (MIT)
• Questionary - Interactive CLI prompts (MIT)
• MkDocs Material - Documentation theme (MIT)
• VHS - Terminal demo recordings (MIT)
• Nord Theme - Color palette inspiration (MIT)

Tooling

• Shields.io - Badges
• Slidev - Presentation framework