Tracing vs. Logging: Why Tracing is Critical for GenAI Systems
January 7, 2025 • 7 min read
In the evolving world of Generative AI (GenAI), building reliable, agentic systems demands robust observability. A critical piece of this observability puzzle is tracing.
While tracing may sound similar to traditional logging, it offers far greater insight into the complex workflows that define GenAI systems.
Let’s explore how tracing compares to logging and why it’s indispensable for these AI-powered systems.
What is Observability in GenAI Systems?
Observability refers to the ability to understand a system’s internal state by examining its outputs and instrumentation.
For GenAI systems, observability is about:
Debugging: Identifying errors and their root causes.
Performance Optimization: Understanding bottlenecks and improving system efficiency.
Cost Management: Monitoring and forecasting expenses, especially when working with APIs like large language models (LLMs).
At the heart of observability lies tracing, a practice that ensures every request’s journey through the system is well-documented and understandable.
How Does Tracing Compare to Logging?
Similarities
Both tracing and logging aim to record information about a system’s behavior.
They capture:
- System Events: Like errors, execution times, or function calls.
- Metadata: Such as timestamps, inputs, and outputs.
- Debugging Information: Helping developers diagnose issues.
How Tracing Differs from Logging
1. Structure and Granularity
Logging:
- Logs are typically independent, flat entries that record discrete events.
- Logs don’t inherently show relationships between different events.
Tracing:
Tracing connects events into a hierarchical structure (trace and spans) to show the end-to-end flow of a request.
Traces represent workflows, with spans capturing specific steps or components.
2. Focus on End-to-End Workflows
Logging:
- Logs are great for tracking isolated events or general system health.
- Example: A log records that the database query failed.
Tracing:
- Traces focus on the journey of a single request or operation through the entire system.
- Example: A trace shows the query's lifecycle: input embedding → vector search → context retrieval → LLM inference.
3. Error Localization
Logging:
- Error logs capture when something goes wrong, but they might not give the context of how that error propagates.
Tracing:
- Tracing pinpoints where in the workflow the error occurred (e.g., embedding step vs. vector database lookup), including timing and dependencies.
4. Performance Insights
Logging:
- Logs can include timestamps, but they don’t inherently provide metrics like latency or total execution time.
Tracing:
Traces capture start time, end time, and duration for each step, allowing performance bottlenecks to be identified.
Example: If embedding queries take consistently longer than expected, tracing highlights this.
5. Distributed Context
Logging:
- Logs are local to specific components and may not connect well across a distributed system.
- Example: Logs in one service might not reveal that a delay occurred in another service.
Tracing:
Traces are designed for distributed systems and include context propagation, linking spans across services.
Example: A trace shows that a delay in Service A caused a bottleneck in Service B.
Why Tracing Is Essential for GenAI Systems
GenAI systems like RAG workflows have multiple interconnected steps (e.g., embedding, retrieval, LLM calls), making end-to-end visibility crucial.
Traces provide a complete picture of each request, while logs would only show isolated snapshots.
Aspect
Logging
Tracing
Structure
Independent, flat log entries
Hierarchical, connected workflows (traces)
Focus
Discrete events
End-to-end workflows
Error Localization
Captures isolated errors
Pinpoints errors within a request’s context
Performance Insights
Requires manual analysis
Captures latency, bottlenecks, and duration
Distributed Context
Limited in distributed systems
Tracks requests across services
Why Tracing is Critical for GenAI Systems
GenAI systems, like Retrieval-Augmented Generation (RAG) workflows, involve multi-step pipelines. Here’s why tracing is crucial:
1. Complexity of Workflows
GenAI systems often chain together tasks, such as embedding queries, retrieving context from vector databases, and making LLM calls.
Errors can happen at any step—tracing provides visibility into each component’s role in the overall workflow.
2. Performance Bottlenecks
Tracing captures execution times for every step (or span) in the system.
Example: If the embedding step consistently takes longer than expected, tracing highlights the issue for optimization.
3. Cost Analysis
API calls to LLMs depend on token usage (input and output size).
Tracing records token counts and cost estimates at each step, enabling precise expense tracking and forecasting.
4. Non-Deterministic Behavior
GenAI systems are probabilistic—the same input can yield different outputs.
Tracing allows span-level evaluation, making it easier to fine-tune individual components rather than re-engineering the entire system.
5. System Drift
Over time, models and retrieved data can become less relevant.
Tracing helps monitor performance trends and detect drift by capturing metrics like retrieval quality or embedding accuracy.
How Tracing Works in GenAI Systems
Key Concepts:
Trace: The end-to-end workflow of a single request, composed of smaller spans.
Span: A single step or action in the workflow, such as a database query or LLM inference. Each span captures metadata like start/end time, inputs, outputs, and performance metrics.
Example: A RAG Workflow Trace
Here’s what a trace might look like in a typical RAG system:
Query Submitted: A user sends a query to the system.
Metadata: Query content, timestamp, user ID.
Embedding: The query is converted into a vector.
Metadata: Execution time, token count, embedding model used.
Vector Search: Relevant context is retrieved from a vector database.
Metadata: Retrieved documents, relevance scores, retrieval time.
Prompt Construction: The system combines the retrieved context with a system prompt.
Metadata: Size of the prompt, truncation details.
LLM Inference: The constructed prompt is sent to the LLM to generate a response.
- Metadata: Input tokens, output tokens, API latency, cost estimate.
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor
# Setup tracing
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)
span_processor = SimpleSpanProcessor(ConsoleSpanExporter())
trace.get_tracer_provider().add_span_processor(span_processor)
# Simulate a trace
with tracer.start_as_current_span("Query Processing") as parent_span:
with tracer.start_as_current_span("Embedding Query") as embedding_span:
embedding_span.set_attribute("input_tokens", 45)
embedding_span.set_attribute("model", "embedding-v1")
with tracer.start_as_current_span("Vector Search") as search_span:
search_span.set_attribute("retrieved_documents", 10)
with tracer.start_as_current_span("LLM Inference") as llm_span:
llm_span.set_attribute("input_tokens", 50)
llm_span.set_attribute("output_tokens", 100)
Output (example trace):
{
"name": "LLM Inference",
"attributes": {
"input_tokens": 50,
"output_tokens": 100
}
}
This trace captures every step of the workflow, along with useful metadata, making debugging and optimization easier.
Tracing vs. Logging: An Analogy
Imagine a pizza delivery system:
1. Logs: You record discrete events like “Pizza prepared,” “Pizza baked,” and “Pizza delivered.”
2. Trace: You track the entire journey of the pizza from order placement to delivery, including timestamps, delays, and potential bottlenecks (e.g., delivery route issues).
- Tracing provides the bigger picture that logs alone can’t.
Tools for Implementing Tracing in GenAI Systems
OpenTelemetry: A standard for distributed tracing and metrics.
LangChain/LlamaIndex: Many GenAI frameworks have built-in tracing capabilities.
Custom Instrumentation: Log token counts, latencies, and quality metrics for spans.
Summary
Tracing is like an advanced, structured form of logging tailored for end-to-end workflows in complex systems, especially distributed ones like GenAI pipelines.
While logs capture events, traces capture relationships and workflows, making them essential for debugging, performance optimization, and cost management.
For GenAI systems, tracing is critical due to their multi-step, non-deterministic, and cost-sensitive nature.