Checklist · Observability
Observability MVP checklist — Step by Step 2026
This checklist provides a step-by-step guide to launching your Observability platform MVP. It covers essential aspects like data ingestion, storage, query capabilities, and cost management, ensuring your platform addresses the core pain points of platform engineers, SREs, and backend teams. Focus on solving correlation, cost, and cardinality challenges from the start.
Phase 01
Data Ingestion & Instrumentation
- ingest-1critical2 weeks
Implement OpenTelemetry (OTel) support
Integrate OTel for standardized data collection across services. Essential for traces, metrics, and logs.
- ingest-2high3 weeks
Develop agents/collectors for common frameworks
Support popular frameworks like Java, Python, and Go with pre-built agents for automatic instrumentation.
- ingest-3medium1 week
Define a standardized log format
Enforce a consistent log format (e.g., JSON) for easier parsing and analysis.
- ingest-4medium2 weeks
Implement sampling strategies for traces
Control the volume of trace data by implementing head-based or tail-based sampling.
- ingest-5high2 weeks
Support for custom metrics
Allow users to define and collect custom application-specific metrics.
- ingest-6critical1 week
Secure data ingestion pipeline
Implement authentication and authorization for data ingestion endpoints.
- ingest-7medium1 week
Implement data validation
Validate incoming data to ensure data quality and prevent errors.
- ingest-8low2 weeks
Support for multiple data sources
Enable ingestion from various sources, including files, databases, and message queues.
- ingest-9medium1 week
Implement rate limiting
Protect the system from overload by implementing rate limiting on data ingestion.
- ingest-10high2 weeks
Implement buffering and retry mechanisms
Ensure data delivery by buffering data and retrying failed attempts.
Phase 02
Data Storage & Indexing
- storage-1critical2 weeks
Choose a scalable storage backend
Select a storage solution like ClickHouse or Cassandra for handling large volumes of observability data.
- storage-2high3 weeks
Design an efficient indexing strategy
Optimize indexing for fast query performance, considering common search patterns.
- storage-3medium2 weeks
Implement data partitioning
Partition data based on time or other relevant dimensions for improved scalability.
- storage-4medium1 week
Implement data compression
Reduce storage costs by compressing data before storing it.
- storage-5high1 week
Define a data retention policy
Establish a clear data retention policy to manage storage costs and comply with regulations.
- storage-6medium2 weeks
Implement data lifecycle management
Automate data lifecycle management tasks such as archiving and deletion.
- storage-7critical2 weeks
Ensure data durability and availability
Implement replication and backup strategies to ensure data durability and availability.
- storage-8critical1 week
Implement data encryption
Encrypt data at rest and in transit to protect sensitive information.
- storage-9medium1 week
Monitor storage performance
Track storage performance metrics to identify and address bottlenecks.
- storage-10medium1 week
Optimize storage costs
Continuously monitor and optimize storage costs by adjusting retention policies and compression settings.
Phase 03
Query & Analysis
- query-1critical4 weeks
Develop a performant query language
Design a query language optimized for analyzing observability data, allowing for filtering, aggregation, and correlation.
- query-2high3 weeks
Implement a user-friendly query interface
Provide a web-based interface for users to easily construct and execute queries.
- query-3high2 weeks
Support for ad-hoc queries
Allow users to perform ad-hoc queries to explore data and identify patterns.
- query-4critical2 weeks
Implement alerting based on query results
Enable users to define alerts that trigger when query results meet specific criteria.
- query-5high2 weeks
Integrate with visualization tools
Allow users to visualize query results using popular tools like Grafana.
- query-6critical1 week
Implement role-based access control
Control access to data and queries based on user roles.
- query-7medium2 weeks
Implement query optimization
Optimize query performance by caching results and using appropriate indexes.
- query-8medium1 week
Support for time-series data
Provide specialized functions for analyzing time-series data.
- query-9low1 week
Implement query history
Allow users to view and reuse previous queries.
- query-10medium1 week
Implement query cost estimation
Provide users with an estimate of the cost of running a query before it is executed.
Phase 04
Correlation & Contextualization
- correlation-1critical3 weeks
Implement trace stitching
Correlate traces across different services to understand end-to-end request flows.
- correlation-2high2 weeks
Correlate logs with traces
Link logs to specific traces to provide additional context for debugging.
- correlation-3high2 weeks
Correlate metrics with traces and logs
Integrate metrics with traces and logs to provide a holistic view of system performance.
- correlation-4medium3 weeks
Implement service maps
Automatically generate service maps to visualize dependencies between services.
- correlation-5high1 week
Support for custom tags and attributes
Allow users to add custom tags and attributes to traces, logs, and metrics for improved correlation.
- correlation-6medium2 weeks
Implement anomaly detection
Automatically detect anomalies in traces, logs, and metrics.
- correlation-7medium1 week
Integrate with incident management tools
Integrate with tools like PagerDuty or Opsgenie to automatically create incidents based on alerts.
- correlation-8low2 weeks
Implement root cause analysis tools
Provide tools to help users identify the root cause of performance issues.
- correlation-9high2 weeks
Support for distributed context propagation
Ensure that context is propagated correctly across distributed systems.
- correlation-10medium2 weeks
Implement event-based correlation
Correlate events from different sources to understand the sequence of events leading to an issue.
Phase 05
Cost Optimization & Management
- cost-1critical2 weeks
Implement data sampling and filtering
Provide options to reduce data volume through sampling and filtering, balancing data fidelity with cost savings.
- cost-2high1 week
Offer tiered storage options
Provide different storage tiers with varying performance and cost characteristics.
- cost-3medium2 weeks
Implement data aggregation and roll-up
Aggregate and roll-up data to reduce storage costs and improve query performance.
- cost-4high2 weeks
Provide cost visibility and reporting
Offer detailed cost breakdowns and reporting to help users understand their spending.
- cost-5medium1 week
Implement resource quotas and limits
Allow users to set resource quotas and limits to control spending.
- cost-6medium1 week
Optimize data retention policies
Provide guidance and tools to help users optimize their data retention policies.
- cost-7low2 weeks
Implement cost allocation
Allocate costs to different teams or projects for better cost accountability.
- cost-8medium1 week
Integrate with cloud billing APIs
Integrate with cloud billing APIs to provide real-time cost information.
- cost-9low2 weeks
Implement automated cost optimization recommendations
Provide automated recommendations to help users optimize their costs.
- cost-10low2 weeks
Implement chargeback mechanisms
Provide chargeback mechanisms to allow teams to be charged for their usage.
Pro tips
- Prioritize OpenTelemetry (OTel) adoption early for standardized data collection and vendor neutrality.
- Focus on solving the most pressing pain points first, such as correlation issues between traces, logs, and metrics, to deliver immediate value.
- Implement robust data sampling strategies to manage costs without sacrificing critical observability data.
- Design your query language with performance in mind, considering common use cases for debugging production issues.
- Provide clear and actionable insights through visualizations and alerting, enabling teams to proactively address problems.