Engineering perspectives on LLM observability
Articles on tracing, prompt drift, token cost management, and debugging RAG pipelines from the Spanloom team.
Token Cost Monitoring: What Goes Wrong and How to Fix It
Most token cost dashboards look at aggregate daily spend. The problem is that the bugs that hurt you most are not in the aggregate, they are in individual trace outliers that average out to invisibility. Here is how to find them.
How Prompt Drift Breaks Production LLM Apps (and How to Detect It)
Prompt drift is silent, slow, and routinely blamed on "model updates" by teams that have not instrumented their pipelines properly.
Diagnosing RAG Retrieval Failures: A Span-Level Debugging Guide
When your retrieval pipeline returns the wrong chunks, the LLM confidently answers with the wrong context. Here is how to trace the failure to its source.
Span-Level Tracing for LLM Pipelines: A Practical Introduction
Traditional request tracing was designed for deterministic services. LLM pipelines are neither deterministic nor simple. Here is what good span design looks like for AI workloads.
LLM Telemetry Beyond Latency: The Metrics That Actually Matter
Latency is the metric everyone watches first. It is also the one that lies the most. Here are the five metrics that correlate with real application quality.
Observing Multi-Step LLM Workflows Without Losing Your Mind
Agent pipelines with ten steps are not ten times as complex as a single LLM call. They are a hundred times harder to debug without the right instrumentation. Here is a framework that works.
Token Budget Strategies That Actually Reduce LLM Costs
Token budgets are not just about capping costs. Done right, they improve output quality and reduce hallucinations caused by context stuffing.
The Hidden Cost of Context Window Stuffing
Filling the context window to capacity feels safe. In practice it inflates costs by 3x and degrades output quality in ways that are hard to attribute without span-level tracing.
Why OpenTelemetry Is the Right Foundation for LLM Observability
Every proprietary AI observability SDK is a bet that the current vendor will own your infrastructure for the next five years. OpenTelemetry is the hedge.
Building an LLM Health Dashboard from Scratch (What We Learned)
When we built the first version of Spanloom's dashboard, we made every classic mistake. Here is what we rebuilt and why.
Five Signals Every AI Engineering Team Should Monitor
Most teams monitor one: latency. Here are the four others that would have prevented their last three production incidents.
Why We Built Spanloom
We ran an LLM application in production for six months before realizing we had no idea what it was actually doing. This is the story of building the tool we needed.