CHOOSELY.AI
Trace And Evaluate Llm Agents Alongside Logs Metrics And Traces
Use OpenObserve for trace and evaluate llm agents alongside logs metrics and traces when you want fast execution, medium ease of use, and high output quality.
Coding & app building
OpenObserve
By openobserve.ai
OpenObserve is a strong fit for tracing and evaluating llm/agent systems alongside logs, metrics and traces, with a profile optimized for advanced users who value medium ease-of-use and high output quality.
Best for: Tracing and evaluating LLM/agent systems alongside logs, metrics and traces
What it is
Open-source, OpenTelemetry-native observability platform that combines infrastructure telemetry (logs, metrics, traces) with LLM/agent tracing, evaluations and prompt monitoring for production debugging.
In Choosely terms, this sits in the coding & app building lane and is commonly selected for tracing and evaluating llm/agent systems alongside logs, metrics and traces and self-hostable production debugging of agent traces, prompts and token costs.
Pricing
Usage-based
Check official pricingOpen-source edition is free forever under AGPL-3.0. OpenObserve Cloud is pay-as-you-go ($0.50/GB ingested with a 30% annual-commitment discount, $0.01/GB queried) with a 14-day free trial and no card required; included retention is 15 months for metrics and 30 days for logs/traces/RUM (+$0.02/GB per extra 30 days). The self-hosted Enterprise edition is free up to 50 GB/day of ingestion, then contact-sales. Model/LLM-judge inference costs are separate.
Basis: Usage BasedConfidence: VerifiedLast checked: September 2026
Why people pick it vs where it falls short
Why people pick it
- OpenTelemetry-native; captures each prompt, tool call and response as a span with token cost and quality score in the same store as logs/metrics/traces
- AI Observability shipped first-class in the v1.0 GA release (11 Sep 2026): LLM-as-a-judge evaluations, annotation queues, datasets, experiments and an LLM Playground
- Self-hostable as a single binary (Docker/Kubernetes) so prompts and responses can stay in your own infrastructure
Where it falls short
- Core is AGPL-3.0; several capabilities (SSO, advanced RBAC, audit trails + sensitive-data redaction, federated search, online evaluations, Super Cluster) are commercial Enterprise-only, not AGPL
- Self-hosting enables data locality but is not automatically privacy-preserving; it requires S3-compatible object storage and operational setup
- It is an observability platform, not an agent builder, coding assistant, hosted model, product/website-analytics tool or BI dashboard; vendor cost/scale comparisons (e.g. vs Datadog) are vendor-measured
When it is a strong fit
A strong match when your main priority is tracing and evaluating llm/agent systems alongside logs, metrics and traces and you need an advanced-friendly starting point.
Useful when your team values medium ease of use and fast execution over heavier setup.
Best when high quality matters, but you still want a practical workflow rather than a complex implementation track.
How it compares in Choosely terms
- Speed profile: Fast. This is best when you want momentum from prompt to usable output without heavy process overhead.
- Ease profile: Medium for Advanced users. You can move quickly even if this is not your full-time specialty.
- Control profile: High. Expect practical customization, but not an infinite-control architecture.
- Pricing signal: Usage-based. Good for teams balancing capability with cost sensitivity.
Tradeoff: Core is AGPL-3.0; several capabilities (SSO, advanced RBAC, audit trails + sensitive-data redaction, federated search, online evaluations, Super Cluster) are commercial Enterprise-only, not AGPL.
Best-fit use cases
Practical ways OpenObserve fits the current Choosely catalog profile.
Self Hosted Debugging Of Agent Traces Prompts And Token Costs
Strong laneUse OpenObserve for self-hosted debugging of agent traces prompts and token costs when you want fast execution, medium ease of use, and high output quality.
Opentelemetry Observability With Llm Evaluations And Annotation Datasets
Strong laneUse OpenObserve for opentelemetry observability with llm evaluations and annotation datasets when you want fast execution, medium ease of use, and high output quality.
Production Monitoring For AI Systems And Infrastructure
Use OpenObserve for production monitoring for ai systems and infrastructure when you want fast execution, medium ease of use, and high output quality.
Alternatives
Vellum AI
AI workflow platform for building, evaluating, and operating LLM applications and agent workflows with production-minded controls.
Choose Vellum AI when your primary need is agent workflow orchestration.
PostHog
Product analytics and product-engineering suite for tracking in-app events, funnels, retention, session replays, feature flags, experiments, surveys, and product usage.
Choose PostHog when your primary need is saas product analytics.
Next step
Send OpenTelemetry traces from one agent workflow into a self-hosted or Cloud instance, correlate a failing completion with its logs and token cost, then add LLM-as-a-judge evaluations before scaling. Note: no in-catalog tool is a direct full-stack observability substitute — Vellum AI is the closest adjacent for LLM evaluation/ops, and the main general-observability incumbents (Grafana, Datadog) are outside this catalog.
Related reads
FAQ
What is OpenObserve best for?
OpenObserve is best for tracing and evaluating llm/agent systems alongside logs, metrics and traces, self-hostable production debugging of agent traces, prompts and token costs, unified infrastructure and ai observability with evaluations and annotation datasets.
Is OpenObserve beginner-friendly?
This catalog profile lists OpenObserve at advanced skill level with medium ease of use.
What should I watch out for before choosing OpenObserve?
Core is AGPL-3.0; several capabilities (SSO, advanced RBAC, audit trails + sensitive-data redaction, federated search, online evaluations, Super Cluster) are commercial Enterprise-only, not AGPL