Cost Effectiveness¶
Micromegas is designed to provide enterprise-grade observability at a fraction of the cost of commercial SaaS platforms by leveraging direct infrastructure costs rather than abstracted pricing models.
Cost Philosophy¶
Unlike traditional observability platforms that charge per GB ingested, per host, or per user, Micromegas runs on your own infrastructure. Your cost is simply the direct cost of the cloud services you consume.
Why This Matters¶
- Full transparency - See every dollar spent on your cloud bill
- No vendor margins - Pay only for actual infrastructure usage
- Predictable scaling - Costs scale linearly with resource consumption
- Data ownership - Your telemetry data never leaves your cloud account
Primary Cost Drivers¶
The infrastructure cost for Micromegas comes from standard cloud services:
Compute Services¶
- Ingestion Service (
telemetry-ingestion-srv) - Handles incoming telemetry data - Analytics Service (
flight-sql-srv) - Serves SQL queries and dashboards - Maintenance Daemon (
telemetry-admin) - Background data processing and rollups
Storage Services¶
- Database (PostgreSQL) - Stores metadata about processes, streams, and data blocks
- Object Storage (S3/GCS) - Stores raw telemetry payloads and materialized Parquet files
Supporting Infrastructure¶
- Load Balancers - Route traffic to services
- Networking - Data transfer and connectivity
Example Deployment Cost¶
Here's a real-world cost breakdown for a production Micromegas deployment:
Data Scale¶
- Retention Period: 90 days
- Total Storage: 8.5 TB in 118 million objects
- Log Entries: 9 billion
- Metric Events: 275 billion
- Trace Events: 165 billion
Monthly Infrastructure Costs¶
| Component | Specification | Monthly Cost |
|---|---|---|
| Ingestion Services | 2 instances × (1 vCPU, 2GB RAM) | ~$30 |
| Analytics Service | 1 instance × (4 vCPU, 8GB RAM) | ~$120 |
| Maintenance Daemon | 1 instance × (4 vCPU, 8GB RAM) | ~$120 |
| PostgreSQL Database | Aurora Serverless (44GB storage) | ~$200 |
| Object Storage | 8.5TB S3 Standard + requests | ~$500 |
| Load Balancer | Application Load Balancer | ~$30 |
| Total | ~$1,000/month |
Scale Perspective¶
This deployment handles:
- 449 billion total events over 90 days
- ~165 million events per day
- ~1,900 events per second average throughput
Cost Management Features¶
On-Demand Processing (Tail Sampling)¶
Micromegas supports storing all raw telemetry data in low-cost object storage and materializing it for analysis only when needed:
- Raw data stored cheaply in S3/GCS
- Processing costs only when querying specific data
- Selective materialization based on actual analysis needs
Flexible Retention Policies¶
Configure retention periods independently for:
- Raw telemetry data - Keep longer in cheap storage
- Materialized views - Shorter retention for frequently accessed data
- Metadata - Configure based on compliance requirements
Commercial Platform Comparison¶
Pricing Model Differences¶
| Aspect | Commercial SaaS | Micromegas |
|---|---|---|
| Cost Basis | Per-GB, per-host, per-user | Direct infrastructure costs |
| Transparency | Opaque vendor margins | Full cost visibility |
| Control | Limited infrastructure control | Complete infrastructure control |
| Scalability | Vendor-managed, unpredictable costs | Self-managed, predictable scaling |
| Data Ownership | Third-party hosted | Your cloud account only |
When Micromegas is Cost Effective¶
The Micromegas model is particularly advantageous when:
- High data volumes - Direct infrastructure costs scale better than per-GB pricing
- Cost predictability is critical for budgeting
- Data governance requirements favor keeping data in your environment
- Operational maturity exists to manage distributed systems
- Long-term retention is needed (cheap object storage vs. expensive SaaS retention)
Detailed Cost Comparisons¶
For in-depth, dollar-for-dollar comparisons with specific platforms:
- Micromegas vs. Datadog
- Micromegas vs. Dynatrace
- Micromegas vs. Elastic Observability
- Micromegas vs. Grafana Cloud
- Micromegas vs. New Relic
- Micromegas vs. Splunk
Getting Started with Cost Optimization¶
- Start small - Deploy minimal infrastructure and scale as needed
- Monitor usage - Use cloud billing dashboards to track costs
- Optimize retention - Balance storage costs with analysis needs
- Leverage tail sampling - Store everything, process selectively
- Right-size compute - Match instance types to actual workload demands
The goal is predictable, transparent costs that scale efficiently with your observability needs.