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Prasad P
Prasad P

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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)
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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
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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:

  1. Intelligent Tiering: Recent data hot, old data cold automatically
  2. Columnar Compression: Store only what you query frequently
  3. S3 Economics: Leverage cloud storage pricing (pennies per GB)
  4. 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]
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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:

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
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Early Access Program

We're onboarding 10 enterprise teams for our beta program before Q1 2025 public launch.

Ideal for teams that:

  • Manage 50+ environments
  • Spend $50K+/year on observability
  • Want to accelerate Terraform workflows with AI
  • Need SOC 2 / ISO 27001 compliance-ready architecture

Contact:

What's Next?

Q1 2025 Roadmap:

  • Google Vertex AI and AWS Bedrock support (BYOK)
  • Advanced Terraform plan analysis
  • Multi-region agent support
  • Prometheus metrics export

Q2 2025:

  • Azure AKS and GCP GKE native support
  • Agent auto-update mechanism
  • Advanced RBAC for agent tools
  • Cost optimization recommendations

Why We're Sharing This

Platform engineering is hard. Environment management shouldn't be.

We believe the future of infrastructure management is:

  1. AI-assisted (but with human oversight)
  2. Cost-optimized (observability doesn't need to be expensive)
  3. Integrated (stop duct-taping 5 tools together)
  4. Compliance-ready (security from day one, not bolted on)

If you're struggling with environment chaos, observability costs, or Terraform workflows, we'd love to hear from you.

Try Realm9: https://realm9.app

Star our repos: https://github.com/realm9-platform

Join the discussion: Leave a comment below!

Prasad P. - Founder, Realm9
Building tools for platform engineers, by platform engineers.

Top comments (3)

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aarthi_kapoor_5d37748b4be profile image
aarthi kapoor • • Edited

This post is the perfect high-level intro to Realm9—love how it zooms out from the deep-dive architecture article to show the full "why" behind solving those enterprise pains like booking wars and observability black holes.

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himansh_raj_7e3a8b5052c76 profile image
HIMANSH Raj •

Excited to test and integrate into our workflow

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uravgkarthik profile image
Karthik Ravishankar •

Sounds really cool. Excited to get my hands on realm9!