LLM observability goes beyond uptime dashboards. Learn what to monitor in your AI pipeline, how tracing and evals work, and where an AI gateway fits in.
Your AI pipeline returned HTTP 200. Latency was within SLA. No errors in the logs. And the answer it gave your customer was completely wrong.
This is the core problem with treating LLM applications like any other service. An LLM application can return a successful response while still producing incorrect, harmful, or low-quality output. Traditional observability tracks uptime, error rates, and latency, but these metrics only confirm that the system executed without failure. They do not indicate whether the model's response was grounded, relevant, or appropriate.
LLM observability exists because standard dashboards are structurally blind to the failures that matter most in AI systems.
LLM observability is the process of collecting real-time data from LLM models or apps about behavioral, performance, and output characteristics. A good observability solution collects metrics, traces, and logs from LLM applications, APIs, and workflows to allow developers to monitor, debug, and optimize at scale.
Worth separating two terms that get used interchangeably. LLM monitoring tracks performance metrics. LLM observability is the process that enables monitoring by providing full visibility and tracing, plus automated issue surfacing. Monitoring tells you something broke. Observability tells you why, where in the pipeline, and what to fix.
This distinction matters because unlike traditional software, LLMs generate variable outputs, making it difficult to consistently assess quality. You cannot write a unit test for "did the model hallucinate." You need a different kind of instrumentation entirely.
Consider an agentic RAG pipeline. A user asks a question. The system retrieves documents, calls a model, maybe calls it again for refinement, and returns an answer. Every step completes. Every API call returns 200. Latency is 1.2 seconds. By every traditional metric, the system is performing well.
But the retriever pulled the wrong documents. The model confidently synthesized an answer from irrelevant context. The user got a plausible, well-formatted, factually wrong response.
This is not a hypothetical edge case. Many valuable LLM apps rely on complex, repeated, chained, or agentic calls to a foundation model, and this intricate control flow makes debugging challenging because it is not always easy to pinpoint the root cause of an issue. The problem compounds because models evolve and change outside of the user's control. A prompt that works on GPT-4o today might behave differently after a silent model update next month.
Traditional observability was built for deterministic systems. LLM observability was built for systems where the output is the risk.
LLM observability addresses four categories of risk:
Hallucinations. When presented with questions they cannot answer, LLM-powered applications may provide misleading information. No error code, no timeout, no signal in your standard metrics. Just a confident wrong answer.
Performance and cost degradation. Applications built on third-party models inherit their instability. API performance fluctuates, algorithm changes introduce inconsistencies, and costs escalate with data volume.
Prompt injection. Users can manipulate LLM applications to output specified text, including incorrect or dangerous content. This is an attack surface not visible through latency or error metrics. Teams need records of inputs, outputs, and execution steps to detect and investigate incidents.
Security and data privacy. LLMs raise concerns around data breaches, output biases from training data, and unauthorized access. For teams building with AI guardrails and PII redaction, observability is how you verify those controls are working in production. The broader threat landscape is covered in depth in our piece on AI security threats.
Tracing is the backbone of LLM observability. It captures detailed execution paths within the application, allowing developers to identify bottlenecks and errors and gain insights into how different components interact.
In LLM contexts, tracing records each model call, retrieval step, tool invocation, and intermediate operation that contributes to the final response. Each step is recorded as a span. Spans are linked in a parent-child structure that reflects execution order, forming a complete trace from initial input to final output.
What does a span contain? Each span records inputs and outputs, latency and token usage, model parameters, error states, and identifying metadata such as user or session IDs. In a RAG application, it is crucial to capture the sequence of events, including events before and after LLM usage, not just the model call itself. The retrieval step that returned bad documents is as important as the generation step that hallucinated from them.
One practical challenge: traditional distributed tracing systems are built for small payloads. LLM spans often include full prompts, retrieved documents, and model outputs, which are much larger. Complex agent workflows can generate traces that grow quickly in size, and many existing APM backends are not designed to store or query this data efficiently.
The metrics that matter for LLM observability fall into three tiers.
System performance metrics: Latency (input to output response time), throughput (requests processed per time window), and error rate (failures or invalid responses). These are table stakes. They tell you the system is running.
Request and response metadata: Temperature, top_p, model name or version, and prompt details on the request side. Tokens and cost on the response side. Temperature and top_p settings directly affect output quality and cost, so tracking them per request lets you correlate parameter changes with output shifts.
Model behavior metrics: Correctness, factual correctness, user engagement, and response quality. This is where LLM observability diverges from infrastructure monitoring. Measuring whether the model's answer was right requires evaluation logic, not just metric collection.
For teams already working on token optimization, the token usage data from observability traces is the foundation. You cannot optimize what you do not measure. And if you are using prompt caching, cache hit rates become another critical signal, directly tied to both latency and cost.
Vendor lock-in is a real risk when choosing observability tooling. OpenTelemetry addresses this with its GenAI Semantic Conventions, which define a consistent schema for capturing LLM-specific data such as model name, provider, token usage, and finish reason. Instrumenting with OpenTelemetry lets teams collect tracing data once and route it to different backends without changing application code.
One architectural recommendation from the OpenTelemetry LLM Working Group: capture prompt and response details on events rather than span attributes because many backend systems struggle with those often large payloads. This is a practical decision that affects storage costs and query performance at scale.
Tracing tells you what happened. Evals tell you whether what happened was good.
LLM evals come in two forms: offline evals, run against test datasets before deployment, and online evals, run on live production traffic to catch issues in real time. Both are necessary.
Offline evals give you a baseline. Did the model improve on your test suite after a prompt change? Online evals catch the long tail. Production inputs are messier, more diverse, and more adversarial than any test set. A model that scores well offline can still fail in production when users ask questions you did not anticipate.
An LLM observability tool should support monitoring model performance, model health monitoring, debugging and error tracking, bias and safety evaluation, and interpretability. Evals are the mechanism that powers the first four of those capabilities.
LLM inference is not cheap, and the costs are not always obvious. LLM costs scale quickly for extra model calls, large context windows, repeated retries, and inefficient agent behavior. Without observability data, teams cannot see which parts of the system are driving usage or where changes would reduce spend.
An agentic pipeline might make twelve model calls to answer one question. A retry loop might silently triple your token usage. A context window stuffed with irrelevant retrieved documents burns tokens on content the model ignores. These patterns are invisible without per-request cost tracking tied to traces.
Rate limitingadds another dimension. When rate limits are hit on external APIs, applications cannot perform their essential functions. Observability that tracks rate limit events alongside cost and latency gives you the complete picture of your pipeline's economics.
For practical cost reduction, smart caching strategies can cut inference costs significantly when informed by observability data.
Every challenge described above — tracing, cost tracking, guardrail enforcement, safety checks — requires instrumentation. The question is where that instrumentation lives.
One option: instrument each application individually. Add tracing libraries, evaluation logic, cost tracking, and safety checks to every service that calls an LLM. This works for a single application. It does not scale. Manually monitoring LLMs is resource-heavy, prone to errors, and cannot scale effectively as systems expand, resulting in slower problem detection and inefficient troubleshooting.
The alternative: put the instrumentation at the gateway layer. An AI gateway that sits inline across every LLM request is the natural enforcement point. Every request and response passes through it. Tracing, token counting, cost calculation, guardrail checks, and PII redaction all happen in one place, applied uniformly, without touching application code.
Shim provides tenant-isolated policy and request accounting for its native provider routes. It is not a tracing, evaluation, or general-purpose observability platform; pair it with tooling that retains the data and analyses your workflow requires.
Platforms like Datadog have recognized this pattern too, building agent observability on tracing technology trusted by 60% of the Fortune 500 that can catch hallucinations, prompt injection attempts, and PII exposure as they happen. The market is converging on the idea that LLM observability needs purpose-built infrastructure. Where that infrastructure sits in your stack is the architectural decision that determines how much work you are signing up for.
You do not need to build a complete observability platform on day one. Start with the layer that provides the most immediate value and expand from there.
Step 1: Log every request and response. Before any analysis is possible, teams must save prompts, responses, and metadata in an accessible data store. This metadata should include references to vector resources, guardrail tagging, sentiment analysis, and model parameters. This is your raw material. Without it, nothing else works. Route traffic through a gateway or add tracing instrumentation to capture this data from day one.
Step 2: Add evals on production traffic. Once you have request/response pairs stored, run online evaluations to score output quality. Start simple: factual accuracy checks, relevance scoring, safety classification. When a significant failure occurs, an observability tool can provide useful insights to quickly identify the root cause, whether it stems from corrupted training data, poorly-designed fine-tuning, failed external API calls, or third-party provider outages.
Step 3: Build alerting and dashboards. With data flowing and evals running, set thresholds. Alert on quality score drops, cost spikes, latency outliers, and safety violations. This is where LLM observability matures from a debugging tool into an operational practice.
The Shim documentation covers how to use the supported provider-native routes and inspect the resulting request metadata.