Skip to main content
Tgtoolglade.
Side-by-side
Browse all comparisons →

Laminar vs OpenObserve

LaminarOpenObserve

Bottom line: Laminar for agent developers; OpenObserve for teams running agents in production.

LaminarLaminar

Open-source, OpenTelemetry-native observability and evals built for AI agents

Visit
OpenObserveOpenObserve

OpenTelemetry-native observability for LLM calls, tools, and agent handoffs

Visit
Votes00
PricingFreemiumFreemium
CategoryLlm ObservabilityLlm Observability
Tags
llm-observabilityagent-tracingopentelemetryevalsopen-source
opentelemetryllm tracingagent observabilitytoken costopen source
Best for
  • Agent developers
  • LLM app engineers
  • Teams needing evals plus tracing
  • Teams running agents in production
  • Platform teams already using OpenTelemetry
  • Organisations needing LLM cost attribution
Pros
  • Open source under Apache 2.0
  • OpenTelemetry-native, one-line tracing
  • Fast Rust implementation
  • Plain-English Signals for agent behaviors
  • Built-in evals SDK and CLI
  • Agent traces are standard OpenTelemetry spans, not a silo
  • Per-span token cost from your own model pricing
  • Unlimited users with no per-seat charge
  • AGPL-3.0 core with a self-hosted option
  • Self-hosted enterprise free up to 50 GB per day
Cons
  • Younger and smaller than major competitors
  • Ecosystem and integrations still growing
  • Free tier has short retention
  • Best suited to agent-heavy use cases
  • Requires OpenTelemetry familiarity for advanced use
  • LLM observability is labelled preview
  • Online evaluations are an enterprise feature
  • Ingestion-based pricing needs volume forecasting
  • Self-hosting carries operational overhead
  • Payload capture raises data governance questions

Comparison generated from each tool's listing. Add or remove tools above to change it.