Technical White Paper — v4.1 · covers DeployIQ v4.8.5

DeployIQ: The Unified Control Plane for Infrastructure Deployment, Observability, and Cost Governance

How engineering teams eliminate tool sprawl, enforce security without friction, and connect deployment execution to cloud cost — in one self-hosted platform that works with your existing stack.

AudienceCTOs · VPs of Engineering · DevOps Architects Version4.1 — July 2026 ClassificationPublic Contactsupport@deployiq.io

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Table of Contents

Executive Summary
The Problem: Tool Sprawl Tax
Why DeployIQ Exists
The DeployIQ Approach
Architecture Deep-Dive
Deployment Safety & Reliability
Security & Tenant Isolation
FinOps Intelligence Engine
Platform Capabilities
Example Scenario & ROI
Market Comparison
No Vendor Lock-In
Conclusion

Section 1

Executive Summary

Modern DevOps teams are drowning in fragmented tools, rising cloud costs, and unreliable deployments. DeployIQ introduces a unified control plane that brings execution, visibility, security, and cost optimization into one system — without replacing your existing stack.

A scenario every engineering leader recognizes

It’s 2 AM. A deployment just failed in production.

Your on-call engineer opens Jenkins to check pipeline logs. Switches to Grafana to find the spike. Opens the AWS Console to check the instance. Checks Terraform state to understand what changed. Searches Slack for the last relevant incident. Four tools. Twenty-two minutes. Still no root cause.

This is not a tooling problem. It is a data isolation problem — and it costs your organization every time a deployment goes wrong.

DeployIQ connects to your existing Jenkins, Kubernetes clusters, Terraform workspaces, Ansible inventories, and cloud accounts. It gives your team a single operational context — one place to deploy, monitor, investigate, and optimize — while your infrastructure stays exactly where it is. Where you have no tool, DeployIQ provides built-in alternatives: native CI pipelines, encrypted secret vault, and built-in observability — metrics, logs and traces.

↓ MTTR

Faster incident resolution — unified context vs. multi-tool triage

↓ Costs

Cloud waste reduced through right-sizing & idle detection

↑ Cadence

Higher deployment frequency through governed pipelines

1 Plane

One RBAC, one audit trail, one cost view — across all tools

Zero lock-in by design. Remove DeployIQ at any time — your tools keep running, your clusters keep serving traffic, your Terraform state is untouched. We earn our place every month, or we don’t deserve to be there.

Section 2

The Problem: The Tool Sprawl Tax

The modern DevOps toolchain is not a stack — it is an ecosystem of independent systems that each solve one problem and create three integration problems. A representative 10-person engineering team typically operates:

Tool	Purpose	Annual Cost	Integration Overhead
Jenkins / GitHub Actions	CI/CD pipelines	$2,400–$6,000	Maintain plugins, credentials, runners
Terraform Cloud	Infrastructure provisioning	$2,000–$8,000	Workspace management, state locking, drift
Grafana + Prometheus	Metrics & dashboards	$1,800–$4,000	Dashboard maintenance, alert routing
HashiCorp Vault	Secret management	$3,000–$12,000	Agent sidecars, lease rotation, audit
CloudHealth / Apptio	Cloud cost visibility	$2,400–$9,600	Tag governance, allocation rules
PagerDuty / OpsGenie	Incident management	$1,800–$4,800	Integration with every other tool
Total		$11,000–$67,000/yr (commercial tiers) — or $0 licences + hosting & upkeep hours if self-hosted OSS	~8–12 hrs/week maintenance

Many teams self-host all of this for $0 in licences — the tax doesn't disappear, it just moves into hosting bills and engineering hours. Section 10 deliberately models that harder, all-OSS case.

The Hidden Cost: Context Fragmentation

The monetary cost is visible. The invisible cost is context fragmentation: when a deployment fails at 2 AM, the information needed to diagnose it — pipeline logs, infrastructure state, application metrics, cost impact, recent config changes — lives in five different systems with five different authentication models. The engineer’s job becomes data archaeologist before it becomes problem solver.

The RBAC Inconsistency Problem

Every tool maintains its own access control model. A developer can be restricted in Jenkins but have broad AWS Console access. A contractor can be removed from GitHub but retain active Vault tokens for weeks. There is no unified audit trail that answers “what did this person do across our entire infrastructure on Thursday?” — a question compliance auditors ask and fragmented toolchains cannot answer.

Section 3

Why DeployIQ Exists

DeployIQ was built to solve four structural problems that no existing tool in the market addresses together:

⚠️

Tool Sprawl Creates Operational Risk

Five tools with five auth models means five attack surfaces, five audit logs that don’t talk to each other, and five places for access to be misconfigured. Integration overhead compounds as the team grows.

🔁

No Control Plane = Reactive Operations

Without a single operational context, teams context-switch instead of act. Engineers diagnose by archaeology — collecting signals from multiple systems — instead of responding to a unified incident view.

💸

Cost Is Disconnected from Engineering

Cloud cost tools see spend. CI/CD tools see deployments. Neither knows about the other. A cost spike on Tuesday cannot be linked to the deployment that caused it — unless both live in the same system.

🔒

Security Is Bolt-On, Not Built-In

SOC 2 compliance requires a unified audit trail. Fragmented tools produce fragmented audit trails. Meeting compliance means building a custom aggregation layer — or paying for one. DeployIQ produces it natively.

The gap in the market: Harness solves CI/CD. Datadog solves observability. Terraform Cloud solves IaC. None of them connect all four layers — execution, visibility, security, and cost — in one operational context. DeployIQ does.

Section 4

The DeployIQ Approach

DeployIQ is built around a single design principle: every event in the infrastructure lifecycle — deployment triggers, configuration changes, cost spikes, security alerts, auto-healing actions — should be connected to a shared operational context, queryable from one place, and actionable without switching tools.

It connects to your existing tools via credentials you control. If you run Jenkins today, DeployIQ triggers jobs against it and surfaces results alongside your K8s state and cost data — no pipeline migration required. If you don’t have Jenkins, DeployIQ’s native CI runner covers that layer. Use what you have. Fill gaps with what DeployIQ includes.

The Deployment Lifecycle as a First-Class Concept

PlanTerraform preview, gate check

ApproveRBAC-gated sign-off

DeployDocker / K8s / Ansible / TF

MonitorHealth checks, DORA

DetectAnomaly & incident engine

FixAI diagnosis, rollback

OptimizeCost & performance

Each stage produces structured data that feeds the next. A deployment failure triggers the Intelligence Engine. A cost anomaly produces an optimization recommendation. An auto-heal action creates an audit record. The lifecycle is not a metaphor — it is an implemented data model.

Multi-Tenant by Architecture, Not Configuration

Team isolation in DeployIQ is enforced at the database query layer on every API endpoint. A developer in Team A cannot enumerate Team B’s deployments, pipelines, clusters, or secrets — even with a valid authentication token — because the query itself excludes them. See Section 7 for the technical detail.

Section 5

Architecture Deep-Dive

DeployIQ is organized into four planes. Each plane has a distinct responsibility. Understanding the planes is the fastest way to assess whether DeployIQ fits your existing infrastructure model.

The Four-Plane Architecture Model

Control PlaneAPI Gateway · RBAC Engine · Policy Engine · Audit System · Team Isolation · Rate Limiting

↓ Authenticated, scoped request passed to execution or data plane

Execution PlaneCI/CD Runner · Terraform Executor · Ansible Runner · K8s Operator · Deployment Engine · Self-Healing Engine

↓ Execution events and outcomes written to data plane

Data PlaneOperational record (deploys, incidents, audit, cost) · Analytics path · Metrics · Logs · Traces · Profiles · Local AI inference

↓ Queries and control signals sent to client infrastructure

Integration LayerAWS · Azure · GCP · Kubernetes clusters · Jenkins servers · GitHub · Slack · PagerDuty · Ansible inventories · Terraform state backends

Control Plane — The Decision Layer

Every request enters through the control plane. TLS and rate limiting are enforced at the edge, and the authenticated user’s identity, role, and team membership are resolved before any handler logic runs. RBAC decisions, audit records, and team isolation are applied here — before the request reaches execution or data logic. A compromised execution engine cannot bypass access controls because access control is upstream of it.

Execution Plane — Where Work Happens

Deployment Engine: Docker, Kubernetes (rolling, blue-green, canary), health-check gating, automatic failure detection and state tracking
Terraform Executor: Plan → review → apply pipeline with workspace isolation, state history, and drift detection
Ansible Runner: Job template library, inventory management, vault credential integration, execution history
CI/CD Runner: Native pipelines with shell, HTTP check, deploy, and sleep step types — plus Jenkins integration via remote API
Self-Healing Engine: Continuous anomaly detection, incident classification, automated or approval-gated remediation

Data Plane — Shared Operational Context

All engines write structured execution data to one shared operational record. This is the architectural decision that makes unified context possible: deployment logs, cost anomalies, audit events, and incident records share a data model. A cost spike can be linked to the deployment that caused it — not discovered separately hours later.

Separate analytics path: Observability queries, DORA metrics, cost history, and global search are served separately — so heavy reporting never slows a deployment down.
Local AI inference: Deployment logs and incident data used for AI analysis never leave the customer’s instance. Designed for organizations with data residency requirements.
Sessions page scripts cannot read: the session token is never exposed to JavaScript, so a malicious script cannot steal it.

Integration Layer — Your Existing Infrastructure

The integration layer connects DeployIQ to the client’s actual infrastructure. Credentials are stored in the encrypted secret vault and used by execution engines at runtime. The client’s Jenkins server, Kubernetes API server, Terraform state backend, and cloud accounts remain in the client’s control. DeployIQ connects to them — it does not host them.

Section 6

Deployment Safety & Reliability

How a platform behaves when things go wrong is more important than how it behaves when they go right.

Deployment Failure: Step-by-Step

Failure detectedDeployment engine reports non-zero exit code or health check timeout. Status set to failed in database within one write cycle.

Execution logs capturedFull stdout/stderr stored against the deployment record. Capped at 8KB per step — retains diagnostic value, prevents storage abuse.

Immutable audit record writtenActor, resource, timestamp, and failure context recorded. Persists regardless of subsequent retries or rollbacks.

Alert triggeredConfigured channels (Slack, Teams, email, PagerDuty) receive a structured alert with deployment ID, project, environment, and a direct link to the failure log.

AI diagnosis availableThe Intelligence Engine analyzes the failure log against known patterns — root cause hypothesis, severity, and ordered remediation steps — using local inference in under 10 seconds.

Rollback or retryOne-click retry or rollback to the last successful deployment. Both create new deployment records — the failure record is never overwritten.

Deployment Strategies and Risk Mitigation

Strategy	How DeployIQ implements it	Failure behaviour
Rolling	Instances updated sequentially; health checks gate each step	Stops at first unhealthy instance; remaining instances serve traffic
Blue-Green	Idle slot provisioned and validated before traffic switch	Traffic switch reverted instantly; old slot untouched until confirmed healthy
Canary	Configurable traffic weight (e.g. 10%) routed to new version	Weight reduced to 0% on anomaly detection; full rollback on explicit signal

Built-In Safety Controls

Deployment gates: Pre and post-deployment health checks block progression — preventing broken deployments from reaching production.
Retry with backoff: Configurable retry count and delay handles transient infrastructure failures without human intervention.
Drift detection: Terraform workspace state tracked after every apply. Drift between declared and actual infrastructure surfaced automatically.
Approval workflows: Production deployments, infrastructure changes, and auto-remediation actions can require explicit sign-off before execution — with full audit trail.
State persistence: Deployment and CI/CD job records persisted to DB before execution begins. If the backend restarts mid-run, the record reflects the last known state.

AI / ML Command Center & Self-Healing

DeployIQ ships a full AI/ML Ops Brain that operates at two layers: (1) real statistical machine learning — EWMA anomaly detection and least-squares threshold forecasting — running directly on your own metrics; and (2) a language-model layer (local by default, or a model you connect) that explains findings, generates postmortems, and proposes fixes. No cloud ML service, no GPU, no data residency risk.

🎯

Health Gauge & KPI Strip

A 0–100 health score computed in real-time from active anomalies, threshold forecasts, and fix-queue depth. Five live KPI counters (anomalies, forecasts, approvals, insights, fix-queue) update without page reload.

📈

ML Anomaly Detection & Forecasting

EWMA (Exponentially Weighted Moving Average) establishes a learned baseline per metric. Z-score deviation triggers anomaly records. Least-squares linear regression forecasts threshold breaches with ETA in hours or days — all computed on your server against your own metrics data.

✅

Approval Queue & 5-Level Autonomy Dial

Every AI-proposed remediation action enters an approval queue before execution. Operators choose a level: L1 (observe only) → L2 (suggest) → L3 (approval-gated, default) → L4 (trusted, low/medium auto) → L5 (fully autonomous). Level stored in platform settings, applied to every action gate.

🤖

AI Root Cause Analysis

Analyzes failure logs, deployment context, and metric state against known patterns. Produces a root cause hypothesis, severity classification, and ordered remediation steps — using local inference in under 10 seconds. Enriched with 90-day operational memory per resource.

📋

Postmortem Generator & Config Analyzer

Incidents automatically generate structured blameless postmortems (PDF + TXT export) from the incident timeline, contributing factors, and resolution steps. The Config Analyzer reviews docker-compose, Kubernetes manifests, Terraform, Ansible playbooks, Dockerfiles, and .env files for security issues with CRITICAL/HIGH/MEDIUM/LOW classification.

🔄

Pre-Deploy Risk Score & Rollback Advisory

Before any production deployment, operators can request a 0–100 risk score based on environment (prod = higher risk), time-of-day (off-hours penalty), recent failure history, and deploy strategy. After deployment, the Rollback Advisory issues a deterministic go/no-go signal with supporting evidence.

🏆

Agent Scorecard — Proof It Works

A read-only scorecard over your own incident history: auto-heal success rate (resolved with a fix applied and no human), real measured MTTR, human-intervention rate, action success rate, and a per-severity breakdown. Turns “self-healing” from a claim into an audit-able number.

🤖

Named Domain Agents (AI SRE Team)

Six named experts — Kubernetes Doctor, Storage Medic, Network Marshal, Certificate Warden, Cost Optimizer, and Deploy Investigator — each scoped to a self-heal class. Every problem is routed to the agent that owns it, reusing the same classifier the heal engine uses so the roster and the engine can never drift.

🔗

Change-Correlation RCA & One-Click Rollback

Each incident is ranked against the deployments, CI runs, and Ansible jobs that landed just before it — scored by time-proximity, whether the change itself failed, and whether the incident text references it. Because DeployIQ owns the change history and can roll back, the top suspect deployment gets a one-click rollback. Competitors only suggest.

🧩

Unified Problems View

A single fault fans out into many signals. DeployIQ groups open incidents that share a blast radius (same server, else same panel) and time window into one problem carrying the worst severity, every underlying signal, and a noise-reduction percentage — so on-call sees problems, not alert storms.

📚

Self-Improving Fix Knowledge Base

Every operation outcome is recorded with a failure fingerprint. When the same fingerprint recurs, DeployIQ surfaces the fix that already worked — ranked by how many times it actually succeeded — through the pre-operation check. The more it heals, the faster the next identical failure resolves. Built on 90-day operational memory; no cloud, no data egress.

🚀

Regression-After-Deploy Detection

Right after a deployment lands, DeployIQ watches the window for new incidents scoped to the same project and environment. If a critical incident follows, it flags the deploy as a likely regression and recommends a one-click rollback — with the critical-action guardrail still governing auto vs. approve.

Safety constraint: All AI-proposed actions enter the Approval Queue before execution. The autonomy level (L1–L5) controls which risk tiers auto-execute vs. require human approval. Every action produces an audit record with the triggering condition, policy applied, and outcome. Secrets and log content are redacted before any inference call — even a local one.

Section 7

Security & Multi-Tenant Isolation

How Tenant Isolation Actually Works

Multi-tenancy is often implemented at the application layer — a filter that checks a team_id parameter. This is insufficient: a parameter manipulation or logic error can expose cross-tenant data. DeployIQ enforces isolation at the database query layer on every resource type.

Isolation logic — three states

None → Admin. No team filter applied. Admin sees all resources across all tenants.

Empty list [] → User has no team membership. Query returns empty set immediately — no database work performed.

[team_id_1, team_id_2] → Developer. Query filters to resources whose team_id is in this set. Resources without a team_id are filtered by creator identity.

Cross-tenant access attempts return HTTP 404 — not 403. Returning “Forbidden” confirms the resource exists, enabling enumeration attacks. 404 reveals nothing about cross-tenant resources.

Authentication Architecture

httpOnly cookie tokens: Access token (15 min) and refresh token (7 days) via httpOnly, SameSite cookies — inaccessible to JavaScript, immune to XSS exfiltration.
MFA (TOTP): Time-based one-time passwords for all accounts. MFA-pending tokens cannot access resources — only the MFA verification endpoint.
Social login: Google and GitHub OAuth with provider-verified email binding. No password stored for social accounts.

Secret Management

Secrets encrypted at rest using Fernet (AES-128-CBC + HMAC-SHA256). Encryption key stored separately from application credentials.
Secret values never returned in list endpoints — only name and key metadata exposed.
Rotation schedules and hard expiry dates enforced with automated notifications before expiry.

HTTP Security Posture

Header / Control	Value / Policy
Content-Security-Policy	Strict source directives — inline scripts blocked
Strict-Transport-Security	max-age=31536000; includeSubDomains
X-Frame-Options	DENY — clickjacking prevented
X-Content-Type-Options	nosniff
Permissions-Policy	Camera, microphone, geolocation, payment blocked
Rate limiting	Per-IP + per-user sliding window on all auth endpoints
Audit logging	All state-changing operations — immutable, team-scoped, exportable

Compliance Posture

Unified audit trail: Every deployment, config change, access grant, and secret rotation produces an immutable, team-scoped audit record — exportable for compliance review.
SOC 2 Type II: Formal certification in progress. The platform is architected for SOC 2 alignment — access control, audit logging, incident response, and availability monitoring implemented at the platform layer.
Data residency: Self-hosted deployment means all data — logs, audit records, secrets, AI inference inputs — stays on your infrastructure. Nothing leaves your instance.

Section 8

FinOps Intelligence Engine

Cloud cost overruns follow a predictable pattern: a team provisions resources, the project changes scope, the resources are forgotten. Without real-time attribution of cloud spend to the teams and projects that created it, this pattern repeats indefinitely. DeployIQ makes cost a first-class engineering signal — not an afterthought reported in a monthly spreadsheet.

📊

Cost Per Team & Project

Cloud accounts scoped to teams. Every discovered resource attributed to the owning team and project. Team leads see their spend. Admins see the organizational view. The same isolation model that governs deployments governs cost data.

🔔

Real-Time Anomaly Detection

Cost patterns monitored against historical baselines. A 40% spike on Tuesday that correlates with a 2 PM deployment is surfaced as a linked anomaly — connecting cost events to the engineering actions that caused them.

⚡

Right-Sizing Recommendations

Actionable recommendations generated from utilization data across connected cloud accounts. Apply directly from the platform where API permissions permit, or export as an action list for the team.

🗑️

Idle Resource Detection

Instances, volumes, and load balancers with no significant activity flagged automatically. Untagged idle resources are the most common source of cloud waste in growing teams — and the easiest to eliminate.

🌐

Multi-Cloud Aggregation

AWS, Azure, GCP, and OCI accounts in one cost view. Cost history, service breakdown, resource discovery, and start/stop/schedule operations work across providers — not per-provider in separate dashboards.

🔗

Deployment → Cost Correlation

Because deployments and cost data share the same database, DeployIQ can link the deployment that caused a cost event. This correlation is impossible with separate tools — it requires a unified data model.

Why FinOps Belongs in the Deployment Platform

Standalone FinOps tools (CloudHealth, Apptio, AWS Cost Explorer) show you what you spent. They cannot tell you why — because they have no visibility into the engineering events that drove the spend. DeployIQ can, because the deployment record and the cost record live in the same database. This is the FinOps capability no standalone tool can replicate.

Budget caps with threshold alerts are live as of v4.8.5. Teams can set spend limits per project and receive proactive warnings before overruns — not retrospective reports after the fact. 30-day AI-driven cost forecasting is on the active roadmap.

Section 9

Platform Capabilities

🚀

Deployment Automation

Docker, Kubernetes, Terraform, and Ansible deployments. Rolling, blue-green, and canary strategies. Deployment gates, approval workflows, and configurable retry with backoff.

⎈️

Kubernetes Management

Multi-cluster (EKS, GKE, AKS, self-hosted). Pod exec, live log streaming via WebSocket, namespace health overview, workload management — no local kubectl required.

⚙️

Native CI/CD Pipelines

Pipeline definitions with shell, HTTP check, deploy, and sleep step types. Live step log streaming, cancellation, run history, team-scoped access. Jenkins integration via remote API.

📊

Unified Observability Platform

Covers the monitoring essentials. Metrics (including AWS CloudWatch, Azure Monitor and Google Cloud monitoring), complete log management (DQL search, live log tail, log-based alerting, PII redaction, long-term storage), distributed APM tracing with tail sampling, memory & CPU profiling, DORA metrics, SLO tracking — all self-hosted, set up on install, no extra licenses.

🔒

Encrypted Secret Vault

Fernet-encrypted secret store with project and team scoping, rotation schedules, expiry alerts, and full audit trail. Values never exposed in list endpoints.

🤖

Intelligence Engine

Incident detection, AI-assisted root cause analysis, auto-remediation with approval policy, and post-mortem generation. Local inference — no data egress.

💰

FinOps & Cost Governance

Multi-cloud account aggregation, cost by team and project, anomaly detection, right-sizing recommendations, and deployment-to-cost correlation.

📋

Ansible Automation

Job template library, inventory management, credential vault integration, execution history. Team-scoped job lifecycle — launch, cancel, delete all RBAC-enforced.

🌍

Terraform IaC

Workspace management, plan preview, apply execution, state history, and drift detection. AWS, Azure, GCP, and OCI providers with encrypted credential storage.

Section 10

Example Scenario & ROI

3-Layer ROI Model: 3 Ways DeployIQ Saves You Money

Layer	Lever	What Changes	Typical Saving
Reclaim Engineer Time	Tool upkeep 8 → 1.5 hrs/week; fixes 6 → 2 hrs	One place to upgrade, one set of users and access rights, one audit trail; deploy history, logs and metrics on one screen. Engineers stop firefighting and start building — capacity, not layoffs.	~500 engineer hours a year for a 12-person team
Save on Tooling Overhead	6 admin surfaces → 1	Running OSS? Your licences are $0 — but the hosting and the hours to patch, upgrade and glue 6 tools together are not (modelled below). Paying for commercial tiers? That's $11K–$67K/year eliminated on top.	Hours + hosting saved always; $11K–$67K/yr licences if applicable
Save on Infrastructure	20–30% cloud waste eliminated	Self-healing prevents over-provisioning. Cloud Discovery finds orphaned resources. FinOps alerts catch surprise bills early.	20–30% reduction in cloud spend

About $90,000 a year for a 12-person team on free, open-source tools (the model above, after the subscription). Based on blended DevOps engineer cost, tooling overhead reduction (hours + hosting for OSS stacks; plus $11K–$67K/year in licences where commercial tiers are in use), and 20–30% cloud waste elimination. The DeployIQ subscription is a small fraction of that. Actual results vary by team size and existing toolchain.

Illustrative scenario — documented assumptions

A SaaS team running Jenkins + Grafana Cloud + Terraform Cloud

Before DeployIQ: deployments were multi-step operations coordinated across Jenkins, the AWS Console, and Terraform Cloud. Each production deploy took 45–60 minutes including manual verification. Incidents required context-switching across 4 tools to diagnose. Cloud waste was discovered only in quarterly reviews.

After connecting DeployIQ to their existing Jenkins server and AWS account — without migrating any pipelines:

Deployment coordination time reduced significantly — unified interface eliminated tool-switching overhead
Incident resolution time dropped — correlated deployment logs, metrics, and alerts in one view
Cloud waste identified in the first 30 days through idle resource detection — eliminated without additional tooling

Outcomes are directional. Results vary by team size, toolchain maturity, and cloud spend profile.

Cost Baseline — 12-Person Team Running Open-Source Everything (The Hard Case)

We deliberately model the hardest case for our ROI argument: a team that pays $0 in tool licences — self-hosted Jenkins, Terraform CLI, Grafana + Prometheus + an OSS log stack, and Vault OSS. If DeployIQ pays for itself here, it pays for itself anywhere. The real cost of a $0-licence stack is hosting + engineering hours + the waste nobody is watching:

Monthly Cost Before DeployIQ (all-OSS stack, $0 licences)

Jenkins (OSS, $0 licence) — self-hosted on 2× EC2 t3.xlarge$280/mo

Terraform CLI (OSS, $0 licence) — S3 + DynamoDB state backend + runner instance$40/mo

Grafana + Prometheus + OSS log stack ($0 licence) — 2× t3.large + EBS/S3 retention$260/mo

Vault OSS ($0 licence) — HA pair on t3.medium + backups$120/mo

FinOps tooling: none — waste surfaces only in quarterly bill reviews (see cloud waste line)$0/mo

PagerDuty (Professional, 12 users — retained either way, see assumptions)$480/mo

OSS stack upkeep: upgrades, patching, dashboards, SSO/RBAC glue across 6 admin surfaces (8 hrs/wk × $85/hr blended)$2,720/mo

Incident coordination overhead (6 hrs × 4/mo × $85)$2,040/mo

Cloud waste (untagged idle resources, no right-sizing)$9,600/mo

Total Monthly Cost$15,540/mo

After DeployIQ — 90-Day Steady State

Monthly Cost After Adoption

DeployIQ subscription (built-in CI, vault, observability & FinOps — tailored to your team)see quote

PagerDuty (retained — DeployIQ integrates, does not force replacement)$480/mo

Stack upkeep time (reduced to ~1.5 hrs/wk — one interface, one RBAC, one audit trail)$510/mo

Incident coordination (MTTR reduced — unified context; 4 incidents × ~2 hrs × $85)$680/mo

Cloud spend after right-sizing + idle elimination (conservative estimate)$6,240/mo

Total Monthly Cost$7,910/mo + subscription

Illustrative monthly savings: ~$7,600 | Illustrative annual savings: ~$91,000 before the modest DeployIQ subscription, about $90,000 after it — with zero tool licences replaced.
Every dollar above comes from engineering hours and cloud waste, not from cancelling subscriptions. Teams paying for commercial tiers (Terraform Cloud, Grafana Cloud, HCP Vault, CloudHealth: typically $11K–$67K/year for 12 seats) save that licence spend on top.

Model Assumptions

All tool licences $0 (OSS self-hosted) — the ROI does not depend on replacing any paid subscription
Blended engineering hourly rate: $85 (fully-loaded)
Stack upkeep: 8 hrs/week before, ~1.5 hrs/week after (one interface replaces six separate admin contexts)
Incident MTTR: 6 hrs before, ~2 hrs after (unified context eliminates multi-tool triage)
Cloud waste: conservative estimate — actual results depend on tagging hygiene prior to adoption
PagerDuty retained on both sides of the model — DeployIQ integrates with existing alerting, does not mandate replacement

Section 11

Market Comparison

How DeployIQ is Different

Most platforms solve one layer of the problem. DeployIQ unifies all four — execution, observability, security, and cost — in one self-hosted platform that works alongside your existing tools.

🔗

Works With Your Existing Tools

Connects to Jenkins, Kubernetes clusters, Terraform workspaces, and Ansible inventories via credentials you control. No forced migration. No rip-and-replace. No lock-in.

💰

FinOps Is Native, Not Bolt-On

Cost attribution, anomaly detection, and deployment-to-cost correlation are in the data model — not a separate product requiring another integration and another contract.

🛡️

Self-Hosted, Air-Gap Capable

Deploy on your own infrastructure with one install command. All data stays on your servers. Local AI inference — no telemetry to external services. Suitable for regulated industries.

🔒

Unified RBAC Across All Layers

One access model governs CI/CD, Kubernetes, Terraform, secrets, and cost data. One audit trail covers everything. No per-tool RBAC configuration sprawl.

Capability	Jenkins+Grafana+TF	Harness	Datadog	NewRelic / Dynatrace	DeployIQ
CI/CD pipelines	Yes	Yes	No	No	Yes
Terraform IaC execution	Yes	Add-on	No	No	Yes
Kubernetes management	kubectl only	CD only	Observe only	Observe only	Full control plane
Ansible automation	No	No	No	No	Yes
Native secret management	Vault required	Add-on	No	No	Built-in encrypted
APM / distributed tracing	No	No	Yes (cloud)	Yes (cloud)	Yes (self-hosted)
Memory & CPU profiling	No	No	Add-on	Add-on	Built-in
FinOps / cost intelligence	No	No	Cloud Cost add-on	No	Native + correlated
Self-healing / AI remediation	No	Limited	Watchdog only	Alert only	Yes
Unified RBAC + audit trail	Per-tool only	Partial	Observe only	Observe only	Platform-wide
Self-hosted / on-premises	Yes	Enterprise only	No	No	Yes
Windows agent support	No	No	Yes	Yes	Yes (PowerShell)
Works with existing tools	Is the tool	Replacement model	Observation only	Observation only	Connects to yours

Zero Lock-In

No Vendor Lock-In — By Design

⚙️

Use Your Jenkins

DeployIQ connects to your existing Jenkins server via your credentials. Your pipelines keep running. DeployIQ surfaces the results in the unified interface alongside everything else.

🌍

Use Your Terraform

DeployIQ executes against your existing Terraform workspaces and state backends. Your state files remain where they are. Your workspace structure is unchanged.

⎈️

Use Your Kubernetes

DeployIQ connects to your existing clusters via kubeconfig. EKS, GKE, AKS, or self-hosted — your clusters remain under your control. DeployIQ adds the management layer on top.

🔒

Your Cloud, Your Data

DeployIQ reads cost and resource data from your cloud accounts. Your AWS, Azure, and GCP accounts remain yours. DeployIQ never stores credentials outside your encrypted vault.

The commitment

Remove DeployIQ at any time. Your Jenkins keeps running. Your Kubernetes clusters keep serving traffic. Your Terraform state is untouched. Your cloud accounts unchanged.

DeployIQ adds control — not dependency. We earn our place every month, or we don’t deserve to be there.

Section 12

Conclusion

The fragmentation of the DevOps toolchain is not inevitable. It is a consequence of an ecosystem that optimized each problem in isolation — without considering the total cost of integration, context fragmentation, and inconsistent security enforcement across the whole.

DeployIQ is built on the conviction that a unified operational context — where deployments, infrastructure state, cost data, security events, and team access control share a coherent data model — is not just more convenient. It is materially safer, faster to operate, and less expensive than the fragmented alternative.

What You Get

Operational coherence: Every deployment event, cost anomaly, and security alert connected to the same context — queryable, auditable, and actionable from one interface.
Security without friction: Multi-tenant isolation at the query layer. httpOnly cookie auth that survives XSS. Unified audit trail across the full lifecycle.
Cost accountability from day one: Real-time cloud spend attributed to teams and projects, with anomaly detection and deployment correlation built in — not bolted on.
Resilience by design: Deployment strategies with automatic failure handling, self-healing with approval workflows, and full rollback capability with an immutable audit trail.
Zero lock-in: Your infrastructure, your tools, your data. Remove DeployIQ at any time — nothing changes for your infrastructure.

Active Roadmap

Budget caps with threshold alerts are live as of v4.8.5. Active roadmap items: 30-day AI-driven cost forecasting, one-click Terraform state rollback, SOC 2 Type II certification (in progress), Kubernetes Operator, and multi-node HA. Auto-remediation policy engine shipped in v4.7 via the AI SRE module.

A word on commitment: DeployIQ connects to your infrastructure — it does not own it. Your Jenkins pipelines, Kubernetes clusters, Terraform state, Ansible inventories, and cloud accounts remain entirely yours. Remove DeployIQ at any time. Your tools still run. Your infrastructure is untouched. We earn our place every month, or we don’t deserve to be there.

To arrange a technical deep-dive, proof-of-concept deployment, or architecture review: support@deployiq.io

DeployIQ gives modern teams the control plane they’ve been missing —
bringing execution, visibility, security, and cost optimization into one platform.

Unified · Intelligent · Secure · Cost-aware · Zero lock-in