This content originally appeared on DEV Community and was authored by Prasad P
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
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:
- Email: sales@realm9.app
- Website: https://realm9.app
- GitHub: https://github.com/realm9-platform
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:
- AI-assisted (but with human oversight)
- Cost-optimized (observability doesn't need to be expensive)
- Integrated (stop duct-taping 5 tools together)
- 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.
This content originally appeared on DEV Community and was authored by Prasad P
Prasad P | Sciencx (2025-11-02T23:01:28+00:00) Introducing Realm9: Solving Enterprise Environment Chaos with AI. Retrieved from https://www.scien.cx/2025/11/02/introducing-realm9-solving-enterprise-environment-chaos-with-ai/
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