Infrastructure as code.
Energy as truth.
Every container build, every deployment, every scaling event — measured in joules. See the physical reality behind your cloud bill.
Your cloud bill is a
black box.
You pay for hours, not for work. A $/hr price tag tells you nothing about the actual energy consumed by your infrastructure. Two identical workloads can cost the same but consume 10x different energy. Traditional infrastructure tools optimize for cost. We optimize for physics.
2025
energy consumed
by 2030
Every format. One graph.
Native parsing, validation, and energy metering for every infrastructure format in your stack.
Your infrastructure is a graph.
We store it as one.
JouleDB stores every resource, dependency, and relationship as a queryable graph. Ask questions traditional tools cannot answer: blast radius, drift paths, dependency chains.
Dependencies
Every resource knows what it depends on and what depends on it. Traverse the full chain.
Blast Radius
Before you change anything, see exactly which services, pods, and endpoints are affected.
Drift Detection
Continuous comparison of declared state vs. live state. Drift is flagged with energy cost delta.
Every CI/CD stage.
Measured in joules.
Build, test, lint, deploy — each stage reports its energy cost. Track regressions. Optimize the expensive stages. Ship the energy receipt with every release.
stages:
- name: build
energy: 1,247 mJ # cargo build --release
- name: test
energy: 892 mJ # 412 tests
- name: lint
energy: 34 mJ # clippy + fmt
- name: deploy
energy: 156 mJ # rolling update
# Total pipeline: 2,329 mJ $/hr tells you nothing.
Joules tell you everything.
Translate opaque cloud pricing into physical energy units. See which instance types deliver more compute per joule. Compare regions by energy efficiency, not just latency.
Instance Efficiency
Region Carbon Intensity
Layer-by-layer
energy cost.
See exactly which Dockerfile instruction consumes the most energy. Multi-stage builds analyzed end-to-end. Base image selection guided by energy efficiency.
FROM rust:1.94 AS builder
WORKDIR /app
COPY . .
RUN cargo build --release
FROM gcr.io/distroless/cc
COPY --from=builder /app/target/release/app /
CMD ["/app"]
# Layer energy: base 12 mJ | deps 340 mJ | binary 2 mJYour role. Your data.
Platform Engineering
Internal developer platforms with energy budgets per team. Golden paths that are also green paths.
SRE
SLOs measured in energy, not just latency. Incident response with blast radius visualization.
Cloud Architecture
Design infrastructure with energy as a first-class constraint. Right-size by joules, not by guess.
FinOps
Translate cloud spend into physical energy. Allocate costs by actual consumption, not tags.
Green IT
Scope 3 carbon reporting backed by real energy data. No estimates, no proxies — measured joules.
Security
Secret detection, policy enforcement, and supply chain integrity — all in the same graph.
Regulation is coming.
Be ready.
EU CSRD, SEC climate disclosure, ISO/IEC 21031 SCI — all require actual energy data. DevOps generates compliance-ready reports from real measurements.
Software Carbon Intensity (SCI)
Per-unit carbon intensity calculated from measured energy, marginal carbon, and functional unit.
Scope 3 Emissions
Category 11 (use of sold products) backed by real energy telemetry, not industry averages.
GHG Protocol
Greenhouse gas emissions quantified from infrastructure energy data with location-based and market-based methods.
PUE Tracking
Power Usage Effectiveness tracked per workload. Compare your PUE against facility-reported averages.
Plan with energy.
Every terraform plan reports energy cost.
Every apply logs actual joules consumed.
Drift detection includes the energy delta.
resource "compute_instance" "web" {
machine_type = "e2-medium"
zone = "us-central1-a"
boot_disk {
image = "ubuntu-2404-lts"
}
}
# Energy: 2.4 mJ to plan, 847 mJ to applyapiVersion: apps/v1
kind: Deployment
metadata:
name: web-api
spec:
replicas: 3
template:
spec:
containers:
- name: api
image: registry/api:v2.1
resources:
limits:
cpu: "500m"
# Steady-state: 14.2 J/hr per replicaSteady-state energy per pod.
Know the joules-per-hour for every deployment. Scale decisions informed by energy, not just CPU utilization. HPA policies that optimize for efficiency.
Ask your infrastructure
anything.
The 16-level cascade resolves queries locally first (cache, index, graph) before ever reaching an LLM. Most infrastructure questions resolve in under 10 mJ.
$ devops query "blast radius of redis upgrade"
L0 Cache 0.001 mJ miss
L1 Index 0.12 mJ 3 dependency paths found
L2 Graph 1.4 mJ 12 affected services
L3 Analysis 8.7 mJ risk assessment complete
Total: 10.22 mJ | Confidence: 0.94Get started.
One command. Parses your existing infrastructure files. Shows you the energy truth.
# Install DevOps CLI
curl -fsSL https://devops.openie.dev/install.sh | sh
# Analyze your infrastructure
devops scan .
# Query your infra graph
devops query "what depends on redis?"
# Run pipeline with energy metering
devops pipeline run --energy