Prasad P

Posted on Nov 2

Introducing Realm9: Solving Enterprise Environment Chaos with AI

#devops #platformengineering #ai #terraform

Introducing Realm9: Solving Enterprise Environment Chaos with AI

After spending years working with platform engineering teams, I kept hearing the same frustrations:

"QA booked the staging environment, but dev team also needs it for a critical demo."

"We're spending $60,000/year on Datadog for just 10GB/day of logs."

"Our engineers waste 40% of their time managing Terraform changes manually."

Sound familiar? That's why we built Realm9 - an AI-powered platform that addresses all three problems in a single, integrated solution.

The Problem: Environment Management is Broken

Most enterprise organizations manage 50-200+ environments across development, testing, and production. The coordination nightmare includes:

Problem 1: Booking Conflicts

Double-bookings: Two teams book the same environment
Idle waste: Environments sit unused while teams wait in queue
No visibility: Spreadsheets and email chains don't scale
Manual approvals: Managers become bottlenecks

Problem 2: Observability Costs

Datadog: $5,000+/month for 10GB/day
Splunk: $6,000+/month
Elastic Cloud: $2,000+/month
Total: $60K-200K/year for mid-sized teams

Problem 3: Terraform Workflow Friction

Manual editing: Error-prone, slow
Context switching: Engineers lose flow
No AI assistance: Unlike modern code editors
Git complexity: PR workflows add overhead

Why Existing Solutions Fall Short

ServiceNow CMDB: Complex enterprise software, not developer-friendly. Teams revolt against using it.

Plutora / Enov8: Enterprise pricing ($50K+/year licenses), heavyweight processes that slow down agile teams.

Spreadsheets: Everyone starts here. Breaks down at 50+ environments. No API integration, no automation.

DIY Solutions: Teams build custom tools, then spend 20% of engineering time maintaining them.

The Realm9 Architecture: Three Integrated Solutions

1. Smart Environment Booking System

Key Features:

Queue Management: Automatic prioritization with fairness algorithms
Multi-level Approvals: Role-based workflows (team lead → manager → director)
Shared Environments: Multiple teams can use same environment concurrently
Auto-release: Environments automatically freed when booking expires
Real-time Dashboard: See all environments, bookings, and availability

Example Workflow:

1. Developer requests staging-us-west for 4 hours
2. System checks availability and conflicts
3. If occupied, adds to queue with priority
4. Manager approves (if policy requires)
5. Developer gets access + Slack notification
6. Auto-release after 4 hours (or manual extension)

2. Built-in Observability (RO9)

This is where we get aggressive on cost.

Architecture: Multi-Tier Storage

┌─ Hot Tier (Redis)    → Last 15 min  → Zero latency
├─ Warm Tier (NVMe)    → Last 24 hours → Sub-10ms queries
├─ Cold Tier (S3)      → Last 30 days → Sub-100ms queries
└─ Archive (Glacier)   → 7 years      → 99% cost reduction

Technology Stack:

Apache Arrow IPC: Zero-copy data transfer, 10x compression
DuckDB: Vectorized query engine for analytical workloads
Parquet Format: Columnar storage with aggressive compression (15-25:1)
Bloom Filters: Sub-millisecond filtering across billions of events

Performance Design Goals:

Targeting 200K logs/second ingestion
Sub-50ms query latency (P99)
15-25:1 compression ratio
Estimated cost: from $75/month (vs $5,000+ for Datadog)

How We Achieve the Cost Savings:

Intelligent Tiering: Recent data hot, old data cold automatically
Columnar Compression: Store only what you query frequently
S3 Economics: Leverage cloud storage pricing (pennies per GB)
Zero Marketing Budget: We pass savings to customers

3. AI Terraform Co-Pilot (BYOK Model)

The standout feature: Bring Your Own Key (BYOK) for LLM providers.

Why BYOK?

Data Sovereignty: Your infrastructure conversations stay in your LLM account
Cost Control: You manage and optimize LLM spending directly
Provider Choice: Switch between OpenAI, Anthropic, Azure OpenAI
Compliance: Meet data residency requirements

Supported LLM Providers:

OpenAI (GPT-4o, GPT-4o-mini, GPT-5)
Anthropic (Claude 4.5 Sonnet, Claude 4.1 Opus)
Azure OpenAI (all OpenAI models via Azure)
Google Vertex AI (coming Q1 2025)
AWS Bedrock (coming Q1 2025)

What It Does:

You: "Create a VPC with public and private subnets across 3 AZs"

AI: [Reads your existing terraform files]
    [Generates HCL following best practices]
    [Updates files in editor]
    [Validates configuration]
    [Creates commit with descriptive message]

You: "Add a NAT gateway to the private subnets"

AI: [Understands context from previous changes]
    [Updates only relevant files]
    [Preserves existing resources]

Architecture: Model Context Protocol (MCP)

We built the AI on Model Context Protocol, an emerging standard for AI tool access. This gives the agent 45+ tools:

Database Tools: Project details, workspace info, cloud credentials
File Tools: Terraform file operations, Git status, file tree
Execution Tools: terraform plan, terraform apply, run logs
Git Tools: Commit, push, PR creation

Security Model:

Agent cannot bypass tool interface
All queries filtered by organization (multi-tenant isolation)
Redis TTL auto-cleanup prevents data leakage
No cross-project or cross-organization access

Technical Innovations

Innovation 1: Frontend/Backend Tool Separation

Traditional AI agents execute all operations immediately. This is dangerous for infrastructure.

Our Approach:

Backend Tools: Execute server-side (database queries, file reads)
Frontend Tools: Pause agent, request UI confirmation, resume with result

Example: terraform apply is a frontend tool. Agent generates plan, shows diff in UI, waits for human approval, then executes.

Innovation 2: Redis-Centric Ephemeral State

All agent session state lives in Redis (not PostgreSQL):

Fast Access: Sub-millisecond latency
Auto-Cleanup: TTL-based (no manual garbage collection)
Horizontal Scaling: Redis Cluster for high availability
Separation of Concerns: Persistent data in Postgres, ephemeral state in Redis

Innovation 3: Polling-Based Agent Communication

For Kubernetes observability agents:

Agents Make Outbound Calls Only: No inbound firewall rules needed
No Webhooks: Backend never calls agent directly
Simple Deployment: No load balancer, ingress, certificates required
Works Everywhere: NAT, firewalls, air-gapped environments

Security & Compliance

We designed Realm9 from day one with enterprise compliance in mind. While actual certification depends on your specific deployment and audit requirements, our architecture aligns with:

SOC 2 Type II Design:

✅ Logical access controls (MFA, RBAC)
✅ Comprehensive audit logging
✅ Encryption at rest and in transit
✅ Secure development lifecycle
✅ Incident response procedures

ISO 27001 Alignment:

✅ Information security management system (ISMS) design
✅ Access control policies (A.9)
✅ Cryptography controls (A.10)
✅ Operations security (A.12)

GDPR Compliance Architecture:

✅ Privacy by design
✅ Data minimization
✅ Right to erasure (data deletion APIs)
✅ Data portability (export functions)

HIPAA Ready (Healthcare):

✅ Access controls and audit logs
✅ Encryption standards (AES-256)
✅ Transmission security
✅ Business Associate Agreement (BAA) capable

Key Security Features:

API Key Security: SHA-256 hashed storage, HTTPS-only transmission
Multi-tenant Isolation: Organization-scoped access, no cross-contamination
BYOK Model: Your LLM keys, your data sovereignty
Network Security: Agents make outbound calls only

Cost Comparison: 3-Year TCO

Here's what we're seeing with early adopters:

Cost Category	Traditional Stack	Realm9	Estimated Savings
Environment Management	$70K-90K/year (Plutora/Enov8 license)	Included	$70-90K/year
Observability	$60K-120K/year (Datadog/Splunk)	From $900/year	$59-119K/year
Terraform Cloud	$20K-40K/year (Enterprise plan)	Included	$20-40K/year
Total Annual	$150K-250K	From $50K	$100-200K/year savings
3-Year TCO	$450K-750K	From $150K	$300-600K savings

Estimates based on mid-sized organizations (50-100 engineers). Your results may vary.

Real-World Use Case: Platform Engineering Team

Before Realm9:

120 environments across 5 cloud regions
Google Sheets for booking (broke down at 80 environments)
$84,000/year Datadog bill
8 hours/week managing Terraform changes manually
2-3 environment booking conflicts per week

After Realm9:

All 120 environments in unified dashboard
Zero booking conflicts (queue management + auto-release)
~$1,200/year observability costs (estimated 98% reduction)
AI handles 80% of Terraform changes (engineers review only)
Team freed up 32 hours/week for feature work

ROI Calculation:

Annual savings: ~$82,800 ($84K Datadog → ~$1.2K RO9)
Time savings: 32 hours/week × 52 weeks × $100/hour = $166,400/year
Total value: $249,200/year
Realm9 cost: ~$50K/year (estimated)
Net benefit: $199,200/year

Getting Started

GitHub Repositories (Open Source)

All our code is on GitHub under the realm9-platform organization:

realm9 - Main platform
ro9-observability - Log analytics
realm9-ai-agent - AI system
realm9-terraform - Terraform integration
realm9-multi-cloud - Cloud management
realm9-enterprise-security - Security architecture

Self-Hosted Deployment

# Deploy with Helm
helm install realm9 oci://public.ecr.aws/m0k6f4y3/realm9/realm9 \
  --namespace realm9 \
  --create-namespace \
  --set global.domain=your-domain.com \
  --set postgresql.auth.password=your-secure-password