Logging & Observability Stack Comparison

AI observability is a rapidly evolving space. Unlike traditional application monitoring, AI systems need to track semantic quality, token costs, and model behaviour alongside standard operational metrics. Here is how the major options compare.

Langfuse (Open Source)

What it does well: Trace-level logging of LLM calls with cost tracking, latency measurement, and evaluation scoring. Self-hostable, which matters for data-sensitive deployments. Strong integration with LangChain, LlamaIndex, and the OpenAI SDK.

Limitations: Requires self-hosting for full control. The hosted version has usage limits. Dashboard customisation is more limited than general-purpose tools.

Best for: Teams that want detailed AI-specific observability with the option to self-host. Strong choice for startups with moderate traffic.

Langsmith (LangChain)

What it does well: Deep integration with the LangChain ecosystem. Excellent for debugging complex chains and agents. Built-in evaluation and dataset management.

Limitations: Tightly coupled to LangChain. If you are not using LangChain, the value proposition weakens significantly. Hosted only.

Best for: Teams already using LangChain who want integrated debugging and evaluation.

Helicone

What it does well: Proxy-based architecture means zero code changes — just change your API base URL. Strong cost tracking, rate limiting, and caching features built in.

Limitations: The proxy model adds a network hop (small latency overhead). Less detailed tracing than Langfuse for complex pipelines.

Best for: Teams that want quick setup with minimal code changes, especially for cost monitoring.

Datadog AI Integrations

What it does well: Unifies AI monitoring with your existing infrastructure monitoring in a single pane. If your team already uses Datadog, adding LLM traces is straightforward.

Limitations: AI-specific features are less mature than purpose-built tools. Cost can be high at scale (Datadog pricing applies to AI traces too).

Best for: Teams already invested in Datadog who want a single observability platform.

Custom Solutions

What it does well: Complete control over what you log, how you store it, and what dashboards you build. Can be tailored precisely to your needs.

Limitations: Significant engineering investment to build and maintain. Most teams underestimate the ongoing cost.

Best for: Large teams with unique requirements that off-the-shelf tools do not address. Not recommended for most startups.

Our Recommendation

Start with Langfuse or Helicone. They are purpose-built for AI, quick to set up, and cover the metrics that matter most. Layer your existing infrastructure monitoring (Datadog, Grafana, CloudWatch) underneath for operational metrics. Migrate to a custom solution only when your scale and requirements genuinely outgrow the available tools.