OpenLLMetry vs. OpenInference: The Observability Stack Your LLM App Needs

If you're building with LLMs, you've probably had this moment: your application is live, the dashboards are green, but the user feedback is... not. The model is spitting out nonsense, hallucinating facts, or just plain failing at its task. You check your logs and APM tools, but they only tell you that the API call returned a 200 OK.

This is the new frontier of debugging. Traditional observability tells you if your system is running, but it can't tell you if it's reasoning correctly.

To get that level of insight, the engineering community is rallying around OpenTelemetry (OTel), the open standard for observability. Building on this foundation, two crucial projects have emerged: OpenLLMetry and OpenInference. A common point of confusion is seeing them as competing choices. The reality is, they solve two different, complementary problems. Understanding how they work together is the key to building a truly robust observability stack for your AI application.

OpenLLMetry: The "How" of Data Collection

Think of OpenLLMetry as the instrumentation engine for your AI stack. Its job is to make the process of gathering telemetry data as painless as possible.

In a typical LLM app, you have multiple moving parts: calls to an LLM provider like OpenAI or Anthropic, queries to a vector database like Pinecone, and logic flowing through a framework like LangChain. Manually instrumenting every single one of these interactions is a tedious, error-prone task.

OpenLLMetry solves this with auto-instrumentation. With just a few lines of code to initialize its SDK, it automatically "hooks into" the popular AI libraries you're already using. It captures the rich, contextual data that traditional tools miss, such as:

• The full text of your prompts and completions.
• Key model parameters like temperature and max_tokens.
• Precise token counts for cost and performance tracking.
• Details of your vector database queries and their results.

In short, OpenLLMetry answers the question: "How do I easily get all the critical AI-specific data out of my application?" It’s the layer that does the heavy lifting of data collection for you.

OpenInference: The "What" of Data Structure

Now that you have a firehose of data from OpenLLMetry, another question arises: what does this data look like? If every tool invented its own naming scheme, you'd have chaos. One tool might log a prompt as gen_ai.prompt, while another uses llm.input_messages. This lack of standardization leads to vendor lock-in and makes it impossible for tools to interoperate.

Think of OpenInference as the semantic schema for your AI data. It’s not an SDK that collects data; it's a formal specification that provides a universal language for describing it. It defines a standard set of names (semantic conventions) for the attributes in your traces.

For example, with OpenInference:

• A model's name is always llm.model_name.
• The list of documents from a retriever is always retrieval.documents.
• The kind of operation (e.g., LLM call, tool use, retrieval) is clearly defined.

By defining this common vocabulary, OpenInference ensures your telemetry data is portable, structured, and understandable by any compatible backend, from open-source visualizers like Arize Phoenix to enterprise platforms.

In short, OpenInference answers the question: "What should my AI telemetry data be called so that it's universally understood?" It’s the layer that brings order and standardization to your data.

Better Together: The Symbiotic Relationship

This is where the "aha!" moment happens. OpenLLMetry and OpenInference are not an "either/or" choice; they are two layers of the same stack that work together beautifully.

The most powerful and future-proof approach is to use OpenLLMetry for its broad, automatic instrumentation capabilities and then configure it to format that data according to the OpenInference specification.

Here’s how the flow works:

1. Instrument: You add OpenLLMetry to your app. It automatically captures detailed traces from your OpenAI calls, LangChain agents, and Pinecone queries.
2. Standardize: You add an OpenInference "span processor." This component intercepts the data captured by OpenLLMetry and translates its attribute names to the OpenInference standard.
3. Export: The perfectly formatted, standardized trace data is then sent to any OpenTelemetry-compatible backend of your choice.

This approach gives you the best of both worlds:

• Effortless Data Collection: Leverage OpenLLMetry’s wide range of integrations to get data from your entire stack with minimal code.
• Vendor-Neutral Data: Use the OpenInference schema to ensure your data is portable and not locked into a single vendor's ecosystem.

By combining the "how" of OpenLLMetry with the "what" of OpenInference, you build an observability pipeline that is both incredibly easy to set up and robust enough for the long haul. You stop flying blind and start diagnosing issues with the clarity and precision your LLM application deserves.

Resources

1. OpenLLMetry GitHub Repository: Explore the source code, see the extensive list of integrations, and check out the documentation.
2. OpenInference GitHub Repository: Dive into the specification documents and see the instrumentor plugins for the semantic schema.
3. OpenLLMetry Official Documentation: Find quick-start guides and tutorials for getting started with instrumentation.
4. OpenInference Semantic Conventions: A detailed reference for the standardized attribute names for tracing LLM applications.
5. The Role of OpenTelemetry in LLM Observability: A great primer on why open standards are crucial for the future of LLMOps.