Next-Generation Enterprise Observability Designed for Cost-Conscious Teams
RO9 is a next-generation observability platform engineered from first principles to deliver enterprise-scale log management at a fraction of traditional costs. By combining cutting-edge columnar storage, intelligent indexing, and multi-tier architecture, RO9 is designed to achieve performance metrics that can compete with industry leaders while dramatically reducing infrastructure costs.
After spending $60,000/year on Datadog for just 10GB/day of logs, we realized the observability industry was fundamentally broken. We built RO9 to prove that enterprise observability doesn't have to bankrupt your infrastructure budget.
Based on our architecture and initial testing, RO9 is designed to achieve the following ballpark metrics. Actual performance will vary based on workload characteristics, hardware configuration, and deployment environment.
| Metric | RO9 Target | Industry Average* |
|---|---|---|
| Ingestion Rate | Up to 200K/sec | 30-50K/sec |
| Query Latency (P50) | Sub-10ms goal | 100-200ms |
| Query Latency (P99) | Sub-50ms goal | 800-1500ms |
| Compression Ratio | 15-25:1 estimated | 5-8:1 |
| Storage Cost/TB | ~$5-10 | $50-120 |
| Monthly Cost (10GB/day) | From $75/month | $2,000-5,000 |
*Industry averages based on publicly available information from vendor documentation and third-party benchmarks. Your results may vary.
Our multi-tier storage design and columnar format provide several advantages:
- Ingestion: Arrow IPC enables high-throughput data streaming with minimal overhead
- Storage: Parquet columnar format with aggressive compression strategies
- Queries: DuckDB vectorized execution for analytical workloads
- Costs: Automatic tiering to S3/Glacier dramatically reduces storage expenses
┌─────────────────────────────────────────────────────────┐
│ Ingestion Layer │
│ (High-throughput via Arrow IPC) │
├─────────────────────────────────────────────────────────┤
│ Hot Tier (Redis) │
│ Last 15 minutes - Zero latency │
├─────────────────────────────────────────────────────────┤
│ Warm Tier (NVMe SSD) │
│ Last 24 hours - Parquet format │
├─────────────────────────────────────────────────────────┤
│ Cold Tier (S3/Blob) │
│ Historical data - Compressed Parquet │
├─────────────────────────────────────────────────────────┤
│ Archive Tier (Glacier) │
│ Long-term retention - 99% cost reduction │
├─────────────────────────────────────────────────────────┤
│ Query Engine (DuckDB) │
│ Distributed SQL with automatic tier routing │
└─────────────────────────────────────────────────────────┘
- Zero-copy data transfer between services
- Columnar memory format for 10x compression
- SIMD optimizations for parallel processing
- Language-agnostic data representation
- Vectorized query execution for massive parallelism
- Columnar storage with aggressive compression
- SQL interface for familiar querying
- Embedded design for minimal overhead
- Probabilistic indexing for instant filtering
- 1-bit per event memory footprint
- Sub-millisecond lookups across billions of events
- False positive rate < 0.1%
- Hot (Redis): Real-time data, instant access
- Warm (NVMe): Recent data, <10ms access
- Cold (S3): Historical data, <100ms access
- Archive (Glacier): Compliance retention, <1hr access
RO9 automatically moves data between storage tiers based on access patterns and age:
- Hot Tier: In-memory for real-time data (recent logs)
- Warm Tier: NVMe SSD for frequently accessed data (last 24 hours)
- Cold Tier: S3 Standard for historical data (recent weeks)
- Archive Tier: Glacier for long-term retention (compliance)
- Dictionary encoding for repeated strings (95% reduction)
- Delta encoding for timestamps (80% reduction)
- Run-length encoding for sparse data (90% reduction)
- Zstandard compression for final storage (60% reduction)
RO9 automatically routes queries to the most appropriate storage tier based on the time range and data characteristics, optimizing for both performance and cost.
- Automatic trace correlation across logs, metrics, traces
- Service dependency mapping without manual configuration
- Anomaly detection using statistical analysis
- Root cause analysis with ML-powered insights
- Ingest millions of events per second
- Search across years of data in milliseconds
- Complex aggregations and analytics
- Real-time alerting and monitoring
- Distributed tracing with automatic correlation
- Service mesh observability
- Database query analysis
- API performance tracking
- Real-time threat detection
- Compliance audit logging
- User behavior analytics
- Forensic investigation
- Customer journey tracking
- Revenue analytics
- Operational metrics
- Custom dashboards
# Deploy RO9 Observability
helm install ro9 oci://public.ecr.aws/m0k6f4y3/realm9/ro9-observability \
--version 1.71.0 \
--namespace ro9-system \
--create-namespace \
--set ingress.host=ro9.your-domain.comRO9 can be deployed on Kubernetes using Helm charts. The platform supports:
- High Availability: Multiple replicas with automatic failover
- Scalability: Horizontal scaling based on ingestion rate
- Storage: Integration with cloud storage providers (S3, Azure Blob, GCS)
- Monitoring: Built-in health checks and metrics endpoints
RO9 provides SDKs for popular programming languages (Node.js, Python, Go, Java) and supports standard protocols:
- OpenTelemetry: Native OTLP ingestion for logs, metrics, and traces
- REST API: Simple HTTP API for custom integrations
- SDKs: Type-safe client libraries with automatic retry and batching
Based on our multi-tier architecture and S3 storage economics, we estimate the following cost structure. Actual costs will vary based on usage patterns, retention policies, and cloud provider pricing.
| Provider | Total (est.) | Notes |
|---|---|---|
| RO9 | ~$75-150 | Estimated based on S3 storage + compute |
| Datadog | $5,000+ | Based on published pricing |
| New Relic | $4,500+ | Based on published pricing |
| Splunk | $6,000+ | Based on published pricing |
| Elastic Cloud | $2,000+ | Based on published pricing |
Our architecture aims to reduce costs through:
- Multi-tier storage: Hot data in Redis, warm on NVMe, cold on S3
- Aggressive compression: Parquet format with columnar compression
- Automatic archival: Move old data to Glacier for long-term retention
- Efficient indexing: Bloom filters reduce storage overhead
- Applications: OpenTelemetry, Jaeger, Zipkin
- Infrastructure: Kubernetes, Docker, systemd
- Cloud: AWS CloudWatch, Azure Monitor, GCP Logging
- Custom: REST API, gRPC, Kafka, AMQP
- Native: Built-in dashboards and explorer
- Grafana: Full Grafana datasource plugin
- API: RESTful API for custom integrations
- Channels: Email, Slack, PagerDuty, Webhook
- Rules: Threshold, anomaly, pattern matching
- ML-Powered: Predictive alerts before issues occur
- Encryption at rest: AES-256-GCM
- Encryption in transit: TLS 1.3
- Data masking: PII automatic detection and masking
- Access control: RBAC with fine-grained permissions
- GDPR: Right to erasure, data portability
- HIPAA: Audit logging, encryption standards
- SOC 2: Access controls, monitoring
- ISO 27001: Information security management
[Log Sources] → [Collectors] → [Arrow IPC] → [Stream Processor] → [Router]
↓ ↓ ↓
[Compression] [Enrichment] [Tier Manager]
[SQL Query] → [Parser] → [Planner] → [Optimizer] → [Executor]
↓ ↓ ↓ ↓
[Validation] [Tier Route] [Pushdown] [DuckDB Engine]
Level 0: Redis (Real-time) - 15 minutes
Level 1: NVMe SSD (Hot) - 24 hours
Level 2: S3 Standard (Warm) - 30 days
Level 3: S3 IA (Cool) - 90 days
Level 4: Glacier (Archive) - 7 years
- Use structured logging - JSON format preferred
- Enable field indexing for frequently searched fields
- Implement sampling for high-volume, low-value logs
- Use batch ingestion for better throughput
- Optimize query patterns - filter before aggregating
RO9 provides flexible configuration options for:
- Ingestion: Batch size, compression algorithms, parallelism
- Storage: Retention policies, tier thresholds, compression strategies
- Query: Cache settings, memory limits, query timeouts
- Security: Encryption, access control, audit logging
All settings can be adjusted based on your workload characteristics and performance requirements.
- Apache Arrow IPC implementation
- DuckDB query engine
- Multi-tier storage
- Distributed query execution
- Machine learning anomaly detection
- Predictive alerting
- Automatic capacity planning
- Cost optimization advisor
- Edge computing support
- Federated queries
- Custom retention policies
- Data lineage tracking
- GitHub: github.com/realm9-platform/ro9-observability
- Discord: [Contact support@realm9.app for community access](https://Contact support@realm9.app for community access)
- Documentation: docs.realm9.app/ro9
- Community: GitHub Issues
- Professional: support@realm9.app
- Enterprise: 24/7 support with SLA
Copyright © 2025 Realm9. All rights reserved.
RO9 is available under a dual license:
- Community Edition: Apache 2.0 License
- Enterprise Edition: Commercial License with support
RO9 - Observability Without the Observatory Price Tag
Part of the Realm9 Platform | Benchmarks | Architecture