Cost Comparison: Micromegas vs. Elastic Observability¶

Author: Gemini, a large language model from Google.

Disclaimer: This document presents a hypothetical, dollar-for-dollar cost comparison between a self-hosted Micromegas deployment and the Elastic Observability solution hosted on Elastic Cloud. The following analysis is based on a series of significant assumptions about workload, pricing, and operational costs. These are estimates, not quotes. Actual costs will vary based on usage patterns, cloud provider, region, and specific cluster configurations.

Core Assumptions for this Comparison¶

Workload Definition (based on Micromegas Example Deployment):
- Total Events (over 90-day retention):
  - Logs: 9 billion log entries
  - Metrics: 275 billion metric data points
  - Traces: 165 billion trace events (equivalent to 82.5 billion spans)
- Monthly Ingestion Rate (for pricing comparison):
  - Logs: 3 billion log entries / month (9 billion / 3 months)
  - Metrics: ~92 billion metric data points / month (275 billion / 3 months)
  - Traces: 55 billion trace events / month (165 billion / 3 months)
- Retention: 90 days (3 months)
Elastic Cloud Pricing Assumption:
- Elastic Cloud uses resource-based pricing (RAM, Storage). This is explicitly stated on their pricing page: "Elastic Cloud pricing is based on the total capacity consumed, which includes virtual storage, RAM." (see reference 1)
- We need to estimate the cluster size required to handle the event volume and retention.
- Assumption on Elastic Data Size: To enable a dollar-for-dollar comparison based on events, we must estimate the storage consumed by these events in Elastic. This is highly dependent on average event size, indexing overhead, and compression.
  - Average log entry size in Elastic (after indexing/overhead): 500 bytes. This is a common approximation for a typical, well-structured log entry after indexing and overhead, considering it includes the message, timestamp, and various attributes/tags.
  - Average metric data point size in Elastic: 100 bytes. This is a common approximation for a single data point across observability platforms, including its value, timestamp, metric name, and associated labels/tags.
  - Average trace span size in Elastic: 1 KB. This is a common industry approximation for a typical span (a trace event), considering it includes various attributes like operation name, start/end times, attributes, events, and links.
- Calculated Storage Needed for Elastic:
  - Logs: 9 billion * 500 bytes = 4.5 TB
  - Metrics: 275 billion * 100 bytes = 27.5 TB
  - Traces: 82.5 billion * 1 KB = 82.5 TB
  - Total Raw Storage Needed: ~114.5 TB
- Elastic Cloud Cluster Sizing: To handle ~114.5 TB of raw data with 90-day retention, and considering replication factors (typically 1 replica, so 2x storage) and indexing overhead, the actual storage provisioned might be closer to 230 TB.
Micromegas TCO Assumption:
- The Total Cost of Ownership (TCO) for a self-hosted solution must include both direct infrastructure spend and the cost of personnel to manage the system.
- Infrastructure Costs: Based on the "Example Deployment" in the main README.md, the estimated monthly cloud spend for this workload (which results in ~8.5 TB of storage for Micromegas) is ~$1,000 / month.
- Operational Personnel Costs: We assume managing the self-hosted solution requires 20% of one full-time DevOps/SRE engineer's time. At a fully-loaded annual salary of $150,000, this equates to ~$2,500 / month.

Analysis for Hypothetical Workload¶

1. Estimated Monthly Cost: Micromegas (Self-Hosted)¶

The estimated Total Cost of Ownership (TCO) for a self-hosted Micromegas instance is calculated by combining direct infrastructure costs with the cost of personnel required to manage the system.

Infrastructure Costs: ~$1,000 / month
- (Includes blob storage, compute, database, and load balancer based on the example deployment, handling the specified event volume with ~8.5 TB of storage)
Operational & Personnel Costs: ~$2,500 / month
- (Assumes 20% of a DevOps/SRE engineer's time)
Total Estimated Monthly Cost (TCO): ~$3,500 / month

2. Estimated Monthly Cost: Elastic Cloud¶

The Elastic Cloud cost is calculated based on the resources required for a cluster capable of handling the specified event volume and retention.

Cluster Configuration: To handle ~38 TB of raw data per month (which translates to ~76 TB provisioned storage with replication and overhead) and the associated query load, a substantial cluster is required.
- We will estimate a cluster with 76 TB of storage and corresponding compute (e.g., 2,400 GB RAM).
Estimated Monthly Cost:
- Based on public pricing for I/O Optimized instances:
  - RAM cost: 2,400 GB * ~$0.50/GB/month = ~$1,200 / month
  - Storage cost: 76,000 GB * ~$0.10/GB/month = ~$7,600 / month
- Subtotal (Platform): ~$8,800 / month
Operational & Personnel Costs:
- Elastic Cloud is a managed service, but it still requires significant expertise to manage data schemas (index templates), build visualizations in Kibana, and optimize queries. This cost is considered part of the value of the SaaS subscription for this comparison.
Total Estimated Monthly Cost: ~$8,800 / month

Dollar-for-Dollar Comparison Summary¶

Category	Micromegas (Self-Hosted)	Elastic Cloud (SaaS)
Infrastructure Cost	~$1,000 / month	(Included in subscription)
Personnel / Ops Cost	~$2,500 / month	(Included in subscription)
Licensing / Subscription	$0	~$8,800 / month
Total Estimated Cost	~$3,500 / month	~$8,800 / month

Qualitative Differences¶

This comparison highlights the significant impact of data compactness on overall cost, especially for high-volume telemetry.

Total Cost of Ownership (TCO): For the same volume of events, the estimated TCO for Micromegas is significantly lower than for Elastic Cloud. This difference is primarily driven by the much more compact data representation and storage efficiency of Micromegas, which directly translates to lower infrastructure costs. In a real-world scenario, to manage these costs, an Elastic deployment handling such high volumes would likely rely heavily on aggressive sampling of logs and traces, potentially sacrificing data completeness for cost efficiency.
Architectural Philosophy:
- Elastic (Search Index-Centric): The Elastic Stack was built around the Lucene search index. It is exceptionally powerful for log search and text analysis. Its support for metrics and traces (APM) has been built on top of this foundation, storing them as documents in Elasticsearch indices.
- Micromegas (Unified Telemetry Model): Micromegas was designed from the ground up with a unified data model for logs, metrics, and traces. It uses columnar storage (Parquet) and a SQL query engine (DataFusion), which is inherently more efficient for analytical queries and data compression, leading to significantly lower storage requirements for the same event volume.
Query Language: This is a major differentiator.
- Elastic: Uses KQL (Kibana Query Language) and Lucene query syntax, which are powerful for text search but can be a learning curve for teams primarily familiar with SQL.
- Micromegas: Uses SQL, the standard language for data analysis. This makes it immediately accessible to a much broader range of engineers, analysts, and data scientists without requiring them to learn a domain-specific query language.
Operational Burden: Elastic Cloud has a lower operational burden, as Elastic manages the cluster's uptime, security, and patching. This is a significant value proposition, but it comes at a higher cost for high-volume data.
Control & Data Ownership: Micromegas provides full data ownership within your own cloud account, offering a higher degree of control and simplifying data governance.
Cost Model: The cost models are fundamentally different. Micromegas's cost is a direct reflection of your cloud bill, heavily influenced by its storage efficiency. Elastic Cloud's cost is based on the resources you provision, which offers predictability but may not fully reflect the underlying data volume in a compact way.

References¶

Official Elastic Cloud pricing - compare serverless and hosted