Mike Shi delves into the rising costs of observability, advocating for open, cost-efficient architectures powered by technologies like OpenTelemetry and columnar databases. This article guides DevOps practitioners in reducing vendor lock-in and building scalable, unified observability platforms.

Open Cost-Efficient Architectures for Observability: Escaping Vendor Lock-In and Ballooning Costs

Introduction

Observability is vital for modern infrastructure reliability, but as systems scale, the costs can skyrocket—often becoming one of the largest infrastructure expenses. In his article, Mike Shi explores both the history and the present-day landscape of observability tooling, drawing lessons for teams looking to reduce costs and avoid vendor lock-in.

The Evolution of Observability

  • Early Days: Engineers managed logs with basic tools like grep and syslog—simple, but limited in scalability and capability.
  • Splunk Era: Splunk introduced scalable querying and schema-on-read, revolutionizing log analysis but also setting a precedent for charging per data ingested.
  • Open Source Search-Based Tools: Elasticsearch (ELK stack) lowered costs but brought trade-offs: less user-friendly interfaces and scaling challenges for high-cardinality, large volumes.
  • Cloud Solutions: Datadog delivered the best user experience but popularized pricing models (per host/APM/traces) that made costs unpredictable and often prohibitive at scale.
  • Specialized Datastores: Prometheus, Loki, and Tempo handled metrics, logs, and traces efficiently when siloed, but limited comprehensive analysis and created fragmented data landscapes.

The State of Observability Today

Teams struggle between:

  • Expensive, all-in-one platforms (threatening budget sustainability)
  • Fragmented point-solutions, which complicate correlation and restrict exploratory workflows

Neither extreme helps DevOps teams achieve core observability goals of confidence and actionable insight.

OpenTelemetry: A Foundation for Flexibility

OpenTelemetry (OTel), as a widely adopted CNCF project, allows organizations to standardize how they capture and export observability data. Incremental adoption enables gradual migration between tools and reduces the risk of vendor lock-in. Solutions must, however, remain compatible with both OTel and other data formats.

Requirements for Modern Observability

According to Shi, successful architectures must:

  • Unify logs, metrics, and traces for seamless correlation
  • Ensure efficient performance and scalability (handling high-ingest, high-cardinality data at low cost)
  • Prioritize affordable long-term retention
  • Offer flexible schema handling (schema-on-read and schema-on-write)
  • Enable expressive querying through SQL and other familiar languages
  • Support OTel natively while remaining open to diverse data types
  • Allow both open source deployment and managed cloud options

Columnar Databases: The Data-Driven Path Forward

Columnar databases massively improve observability architectures by offering:

  • High compression and storage efficiency (cost reduction at scale)
  • Fast aggregations and selective filtering (support for high-cardinality analytics)
  • Support for JSON and wide events (schema flexibility)
  • Expressive SQL queries and parallel execution
  • Separation of storage and compute (allowing affordable, scalable data retention)

Major technology firms such as Tesla, Anthropic, OpenAI, and Netflix build observability platforms on these foundations, but custom solutions require significant engineering investment.

The Role of Developer Interfaces

While tools like Grafana provide access to columnar databases, they often require deep SQL knowledge. To make observability genuinely accessible, dedicated, open source, developer-friendly UIs are needed—abstracting complexity while preserving the power for expert users.

Conclusion

Columnar databases and open-source UIs represent a sustainable, scalable, and cost-effective future for observability. By moving beyond vendor silos and embracing open architectures (with OpenTelemetry as standardization glue), organizations can empower their teams and keep infrastructure budgets under control.

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Written by Mike Shi for DevOps.com

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