November 2026 Summaries

Choosing an observability vendor requires comparing platforms with a weighted, evidence-based framework rather than feature lists, focusing on total cost, OpenTelemetry and open-standard support, signal coverage, scale, retention, query usability, portability, security, and deployment options. The recommended process is a 30-day proof of concept using identical production telemetry routed through the same OpenTelemetry Collector, during which teams rebuild key dashboards and alerts, replay past incidents, test query performance, and project costs under future growth. The discussion emphasizes that pricing models can combine ingestion, indexing, querying, retention, host, user, and egress fees, while proprietary agents, query languages, dashboards, and inaccessible historical data create switching costs. It presents OpenTelemetry as a way to reduce vendor lock-in by allowing instrumentation to remain unchanged when backends are switched. The article positions OpenObserve as an open-source, OpenTelemetry-native option with ingest- and query-based pricing, support for multiple telemetry signals, SQL and PromQL querying, self-hosting or managed deployment, open Parquet storage, and security and compliance features.

Observability vendors commonly charge by telemetry ingestion, monitored hosts, user seats, capacity tiers, events, or query volume, with many platforms combining several of these models. The comparison explains that ingest-based pricing generally scales with data volume and can be managed through filtering and sampling, while host-based pricing can rise with autoscaling infrastructure, user-based pricing grows with team access, and flat-rate plans offer predictable costs within usage bands but may impose overages or tier jumps. It highlights often-overlooked charges for querying, retention, log indexing, custom metrics, active series, and data egress, and compares published list prices across vendors including Datadog, Splunk, Dynatrace, New Relic, Grafana Cloud, Elastic, Honeycomb, Sumo Logic, and OpenObserve. A worked example using 50 hosts, 20 engineers, 1,300 GB of monthly telemetry, and 5,000 GB queried illustrates how different billing structures can produce substantially different totals. The piece argues that forecasting requires identifying every billing driver and applying distinct growth assumptions, and presents OpenObserve’s ingest-and-query pricing, unlimited users, lack of host fees, object-storage architecture, and self-hosted option as a comparatively predictable approach.