The best LLM observability platform depends on what the team needs to observe and who owns the response. Some products specialize in traces and evaluations; others combine observability with an AI gateway or a broader operations control plane. A useful comparison starts with production workflows rather than a universal ranking.
Key takeaways
- Separate trace/evaluation depth from gateway and operations requirements.
- Prefer stable workload identity and request-level provider, model, latency, token and cost evidence.
- Evaluate a real debugging, cost and reliability workflow before choosing a platform.
Langfuse: tracing and LLM engineering depth
Langfuse is a strong candidate for teams prioritizing structured traces, evaluations, prompt management, experiments and open-source/self-hosted deployment. Its current observability stack supports OpenTelemetry and dedicated Python and JavaScript SDKs.
Helicone: gateway plus debugging and monitoring
Helicone combines unified model access and routing with request logging, agent tracing and monitoring. It is relevant when teams want gateway and observability in a closely integrated developer-oriented workflow.
Portkey: enterprise gateway and observability
Portkey combines an enterprise AI gateway with detailed request observability, tracing, cost/performance analysis, governance and guardrails. It is relevant for organizations evaluating a broad enterprise production stack.
LiteLLM: gateway-centric operations and self-hosting
LiteLLM centers on an open-source/self-hosted gateway but now documents substantial usage tracking, spend, observability, governance, routing and optimization capabilities. It is particularly relevant for platform teams that want to own the gateway infrastructure.
Clyvel: observability inside an operations control plane
Clyvel treats observability as part of the same operating model as gateway traffic, FinOps, budgets, reliability, incidents, governance, evaluations and optimization. It is relevant when the organization wants those workflows to share request and workload evidence.
How to choose without relying on a ranking
Instrument one representative production workflow and ask each candidate the same questions: can you reconstruct the trace, find the provider and model, explain latency, attribute cost, identify retries, detect a reliability issue and connect the evidence to the responsible workload? Then evaluate deployment, security and governance constraints separately.
FAQ
Common questions
What should an LLM observability platform track?
At minimum, production teams usually need trace or request identity, provider, model, status, latency, token usage, cost context and enough workflow metadata to explain agent or application behavior.
Is LLM observability the same as an AI gateway?
No. Observability captures and analyzes production behavior. A gateway intermediates model traffic. Some platforms combine both capabilities.
CLYVEL
Put the operating model into practice.
Clyvel connects production AI traffic, cost, reliability and governance in one operations layer.
Explore Clyvel ObservabilitySources and further reading
Clyvel Research uses primary technical and vendor references wherever a claim benefits from external context.
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