TL;DR
LLM observability is the practice of tracing each request through an LLM application to see its speed, its cost and the quality of its answer. Classic APM confirms that the service is up, but it cannot tell when an answer is wrong. So instrument each request as one trace, with child spans for retrieval, the model call and any tool calls. Record tokens, latency, model and prompt versions, and errors on every span. Then score a sample of live answers and attach those scores, plus user feedback, to the same trace. Finally, alert on answer quality and cost as well as errors, with thresholds taken from a measured baseline.
Key Takeaways LLM observability adds answer quality, token cost and retrieval behavior to the latency and error data that APM already collects. One trace per request, with child spans for retrieval, model and tool calls, is the unit that makes a bad answer explainable. OpenTelemetry’s GenAI conventions give vendor-neutral span names, but they are still marked Development, so pin a version. Capture metadata by default and treat prompt and response text as opt-in, redacted and kept for a short window. Quality scores from sampled live traffic belong on the original trace, next to the prompt version that produced the answer. Alerts should carry example trace IDs and a named owner, and quality alerts need a minimum sample before they fire. Watch on YouTube
AI Factory Framework: How Enterprises Scale AI to Production in 2026
An episode of Kanerika’s The Digital Shift on the AI Factory framework: the four layers and the governed operating model that take enterprise AI from pilots to production.
The Dashboard Was Green and the Answers Were Wrong Every panel on the support assistant’s dashboard is green. Uptime sits at 99.9%, p95 latency is flat and the error panel reads zero. Yet the escalation queue has doubled since Monday, because the bot keeps quoting a refund policy that was retired last quarter.
Nothing on the infrastructure view is lying, either, since every request returned HTTP 200 inside its latency budget. The retriever simply pulled an outdated document, and the model wrote a fluent answer from it. No panel on that dashboard was built to notice that kind of LLM hallucination .
Gartner expects this gap to drive spending. In a March 2026 forecast , it predicted that explainable AI will push LLM observability investment to 50% of GenAI deployments by 2028. The share stood at 15% when the forecast was published. Pankaj Prasad, a senior principal analyst at Gartner, put the shift plainly. “Traditional observability is focused on speed and cost, but the priority is now moving toward deeper quality measures such as factual accuracy, logical correctness and sycophancy.”
Closing that gap is an instrumentation job. It starts with one request, traced from the question to the answer.
What Is LLM Observability? LLM observability is the ability to explain any single response from an LLM application after the fact. That means knowing which prompt version ran and which documents were retrieved. It also means knowing which model answered, what the call cost, how long each step took and whether the answer held up.
Telemetry only gets a team there when the pieces are connected. Metrics show that something changed, while traces show where in the request it changed. Quality scores then show whether the change hurt users, so the discipline comes down to joining all three on a shared request ID.
Data teams will recognize the pattern. Data observability does the same thing for pipelines, because it joins freshness, volume and schema checks on a shared table.
Why Classic APM Goes Blind on LLM Apps Application performance monitoring was built for deterministic code. The same input takes the same path and returns the same output. As long as that holds, a 200 status and a fast response are good proxies for success.
LLM applications break that assumption in three ways. First, the same question can produce different answers on two runs. Second, a response can be fast, well formed and still wrong. Third, cost scales with tokens, which vary from one request to the next, rather than with CPU time.
Question What APM reports What LLM observability adds Is it up? Uptime, HTTP status codes Provider errors, rate limits and timeouts per model Is it fast? Request latency Time to first token, plus latency per retrieval, model and tool span What did it cost? Host and CPU usage Input and output tokens, cost per request, feature and tenant Was the answer right? Nothing Grounding checks, task success, user feedback Why did it fail? Stack trace Full trace with prompt version, retrieved documents and model response
The last two rows are where most LLM incidents live, yet neither one appears in a standard APM view. When many model calls chain together inside an agent, the problem grows another layer, which our guide to AI agent observability covers.
Monitoring still has a job: it watches known numbers against known thresholds and says when to look. Observability lets an engineer answer a question nobody built a dashboard for, such as why answers about one product line got worse after Thursday’s release.
The traces and scores then show where.
The Four Questions Every LLM Team Must Answer A useful setup answers four questions for any request, at any time.
Is it healthy? Errors, timeouts and latency per model and per step.Is it right? Grounding, task success and user feedback on a sample of answers.What does it cost? Tokens and estimated spend per request, feature and customer.Why did it fail? The trace, the prompt and model versions, and the inputs that produced the answer.If a team cannot answer one of these within a few minutes, then that question is where instrumentation work should start. Most of the generative AI risks a business worries about also surface through the second and fourth questions.
Which Signals Should an LLM App Emit? LLM observability signals fall into five families. Each one answers a different question and needs a different collection method. So it pays to plan them separately before writing any code.
Signal family Example measurements What it reveals Typical first alert Speed and reliability p95 latency, time to first token, error and timeout rate Slow or failing requests p95 latency well above baseline for 15 minutes Tokens and cost Input and output tokens, cost per request, retry count Prompt bloat, retry storms, expensive features Daily spend for one feature above forecast Retrieval (RAG) Documents returned, top similarity score, empty-result rate, index version Missing or stale context Empty-result rate jumps after a re-index Answer quality Grounding score, task success, refusal rate Fluent but wrong or unhelpful answers Sampled pass rate drops below baseline User feedback Thumbs down, rephrased questions, escalations Problems users notice first Negative feedback share doubles day over day
Speed and Reliability Total latency hides most of the story in an LLM app. Time to first token shows how long a user stares at an empty box in a streaming interface. Total generation time, on the other hand, mostly tracks output length.
Splitting latency by span also shows whether the slow part was retrieval, the provider or the team’s own post-processing. Track errors by type as well, rather than as one blended rate. Rate-limit errors, timeouts, content-filter blocks and malformed tool calls each need a different fix, so a single error percentage hides which one is growing.
Tokens and Cost per Request Token counts come back with almost every provider response, so cost tracking is mostly a tagging problem. Record input and output tokens on the model span. Then apply the rate card in a downstream job instead of hard-coding prices in the app.
Tag each request with its feature, tenant and environment, because a cost spike is easy to spot but hard to explain without them. Retries deserve their own counter too, since a silent retry loop can double spend without a single user-facing error. Teams that route traffic through an LLM gateway often count tokens there as well.
Retrieval Signals for RAG Apps In a retrieval-augmented generation app, a bad answer often starts before the model runs. When the retriever returns nothing relevant, the model improvises. The response then sounds just as confident as a grounded one.
So log the number of documents returned, their IDs and similarity scores, and the index version on a retrieval span. A rising empty-result rate and a falling top score are early warnings that a re-index or a chunking change has broken recall. Our RAG pipeline guide shows where each stage tends to fail, while the RAG application guide covers the product layer around it.
Answer Quality on Live Traffic Quality is the signal APM never had, and it is the one users feel first. In production it usually comes from three sources.
Automated checks compare the answer with the retrieved text, and task outcomes show whether the user finished the job. Human reviewers, meanwhile, grade a small sample by hand.
Designing those checks is its own discipline, which our LLM evaluation framework guide covers. The observability job is narrower. It collects the scores, stores them against the trace and makes them easy to query.
Case Study
52% Higher Add-to-Cart Rate with AI Stylist Karl
A luxury jewelry and apparel retailer deployed Karl, Kanerika’s conversational AI assistant, as a 24/7 stylist across web and in-store kiosks, lifting add-to-cart rates by 52% and cutting customer service workload by 41%.
Read the Case Study → User Feedback Users notice problems before any evaluator does. Thumbs-down clicks are the obvious signal. Still, quieter ones often carry more weight, such as a question rephrased within a minute or a hand-off to a human agent.
For that reason, send feedback as an event that carries the trace ID of the answer it refers to. Without that link, feedback becomes a sentiment chart instead of a debugging tool.
How Traces and Spans Map a Single LLM Request A trace is the record of one request, from the moment it arrives until the response leaves. A span is one timed operation inside it, with a parent, a start and end time, a status and a set of attributes. Because every span shares one trace ID, a pile of logs turns into a readable story.
Anatomy of One Traced Request Here is a synthetic trace for the support assistant from the opening scenario. The numbers are illustrative, but the shape matches a well-instrumented RAG request.
trace 4bf92f3577b34da6 POST /ask 9.84 s status OK (synthetic example)
│ gen_ai.prompt.version=support-v14 app.index.version=policy-2026-10-01
├─ retrieval policy-index 0.41 s top_k=8 returned=8 top_score=0.71
├─ rerank 0.22 s kept=4
├─ chat model-a 8.96 s input_tokens=6912 output_tokens=388
│ time_to_first_chunk=7.80 s finish_reasons=["stop"]
└─ postprocess citations 0.19 s citations=3Read it top down, since the slowest span usually tells the story. The request took 9.84 seconds, and the model span accounts for almost all of it. Inside that span, the first chunk arrived after 7.8 seconds, which points at a long prompt or a busy provider rather than slow generation.
Attributes Every Model Span Should Carry OpenTelemetry’s GenAI semantic conventions define standard names for most of what a model span needs. As a result, backends can read them without custom mapping. The table lists a practical minimum, with one app-specific field marked as custom.
Attribute Example Why it matters gen_ai.operation.name chat Separates chat, embeddings, retrieval and tool spans gen_ai.provider.name the provider’s name Splits errors and latency by provider gen_ai.request.model model-a What the app asked for gen_ai.response.model model-a-2026-08 What actually answered, which can differ gen_ai.usage.input_tokens / output_tokens 6912 / 388 Cost and prompt-bloat tracking gen_ai.response.finish_reasons [“stop”] Shows truncation when the value is “length” error.type TimeoutError Groups failures by cause gen_ai.prompt.name / gen_ai.prompt.version support-answer / support-v14 Links quality changes to prompt releases app.index.version (custom) policy-2026-10-01 Links retrieval changes to re-indexing
The prompt name and version come from the conventions. The index version does not, but it earns its place anyway. Together they answer the question asked most often in an incident, which is what changed. Give custom fields like the index version an application prefix so they never collide with a future standard name.
Reading a Slow Trace Suppose p95 latency doubles overnight. First, open five slow traces and five normal ones side by side, then compare them span by span. If retrieval and post-processing look the same but time to first token grew, check input token counts next.
A jump in input tokens usually means the prompt grew, often because retrieval started returning longer chunks or more of them. That is a change in the team’s own code or data rather than the provider. And the trace proves it in minutes.
Kanerika Service
LLM Development Services
Kanerika designs, builds and runs production LLM applications, with tracing, cost tracking and quality scoring built in from the first release.
Explore LLM Development How to Instrument an LLM App, Step by Step The steps below assume a single-request application such as a chatbot or a RAG assistant. Multi-step agents use the same building blocks, only with deeper nesting.
Step 1: Map the Request Path Before Writing Code First, draw the path one request takes. A typical RAG app has an entry point, prompt assembly and a retrieval call. After that come an optional rerank step , the model call, any tool calls and post-processing such as citation formatting.
Then each box on that drawing becomes a span. Each arrow that crosses a process boundary must also carry trace context with it, which is what the W3C Trace Context traceparent header standardizes.
Step 2: Pick OpenTelemetry and Pin the GenAI Conventions OpenTelemetry is the vendor-neutral choice for the data model and transport. Spans emitted with it can go to almost any backend, so the instrumentation outlives a change of tool. The project has also written about tracing LLM applications on its own blog since 2024.
Two cautions apply, though. The GenAI conventions are still marked Development, and they recently moved into a dedicated repository .
An unreleased change there already splits the older token usage histogram into separate input and output instruments. So pin the convention version and keep a thin mapping layer.
Teams that want auto-instrumentation can start with an open-source library such as OpenLLMetry . It wraps common LLM SDKs and vector databases while still emitting standard OpenTelemetry data.
Step 3: Wrap the Model Call in a Span Start with the model call, since it carries most of the latency and all of the token cost. The sketch below uses the OpenTelemetry Python API. In it, client.chat stands in for whichever SDK wrapper the app already uses.
from opentelemetry import trace
from opentelemetry.trace import Status, StatusCode
tracer = trace.get_tracer("support-assistant")
def call_model(client, model, messages, prompt_name, prompt_version):
# span name follows the convention "{operation} {model}"
with tracer.start_as_current_span(f"chat {model}") as span:
span.set_attribute("gen_ai.operation.name", "chat")
span.set_attribute("gen_ai.provider.name", client.provider)
span.set_attribute("gen_ai.request.model", model)
span.set_attribute("gen_ai.prompt.name", prompt_name)
span.set_attribute("gen_ai.prompt.version", prompt_version)
try:
resp = client.chat(model=model, messages=messages)
except Exception as exc:
# low-cardinality cause, e.g. "TimeoutError" or "RateLimitError"
span.set_attribute("error.type", type(exc).__qualname__)
span.set_status(Status(StatusCode.ERROR))
raise
span.set_attribute("gen_ai.response.model", resp.model)
span.set_attribute("gen_ai.response.finish_reasons", [resp.finish_reason])
span.set_attribute("gen_ai.usage.input_tokens", resp.usage.input_tokens)
span.set_attribute("gen_ai.usage.output_tokens", resp.usage.output_tokens)
return respTwo details matter more than they look. The response model is recorded apart from the requested one, because providers can route an alias to a newer snapshot. The finish reason also exposes truncation, a common cause of answers that stop mid-sentence.
Step 4: Trace Retrieval and Tool Calls as Child Spans Create the retrieval span as a sibling of the model span under the same parent, named after the operation and the data source. Record the result count, document IDs, scores and the index version. Leave the document text out by default, though.
Tool calls get one span each, named execute_tool plus the tool name. Their arguments and results also stay out by default, for the same privacy reason.
Once an app chains many model and tool calls on its own, it has become an agent. At that point it needs a dedicated agent observability setup.
Step 5: Stamp Every Span With Version Metadata Most quality regressions trace back to a change. Someone edited the system prompt, a provider updated a model snapshot, or the knowledge base was re-indexed.
So put the prompt version, model snapshot, index version and app release on the root span. Then copy them to child spans wherever the backend cannot join them. Treat prompts like code, too, with the versioning habits from prompt engineering best practices .
When a quality score drops, one group-by on those fields usually names the culprit. It works the same way data lineage names the upstream table behind a broken report.
Step 6: Decide What Prompt Content to Store Prompts and responses are the most useful debugging data and also the riskiest. They can hold exactly what OWASP’s LLM02 guidance calls sensitive, such as personal data, health records, credentials and confidential business documents.
The OpenTelemetry conventions set a sensible default here. Message content, system instructions, retrieval queries and retrieved documents are all opt-in attributes. Instrumentations should not capture them unless a setting such as OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT turns capture on.
A workable policy therefore has three tiers. Capture metadata for every request and redacted content for a sample. Then keep full content only in a locked-down store with short retention and audited access.
Our guides to AI privacy and generative AI security explain the wider controls.
Step 7: Sample, Export and Prove the Telemetry Works Keep every trace that ends in an error, a negative feedback event or a quality failure, and sample the healthy ones. Tail-based sampling in an OpenTelemetry Collector makes that call after the trace finishes, so the interesting traces survive.
Then test the instrumentation the way a fire drill tests an alarm. Force a timeout, trigger a rate-limit error and send a prompt that hits the token limit. Also ask a question the knowledge base cannot answer, and check that each failure can be found from a dashboard within five minutes.
Scoring Answer Quality in Production Offline test sets catch regressions before release. Live traffic, however, catches the questions nobody thought to test. That is why production scoring belongs inside the observability loop and not only in CI.
Choosing Which Responses to Score Scoring every response is rarely affordable, especially with a model-based grader. Instead, a small random sample gives an unbiased baseline, while targeted samples add depth where risk is highest.
Responses that drew negative feedback or an escalation Requests served by a new prompt or model version in its first days Answers where retrieval returned few or low-scoring documents High-impact flows such as billing, medical or legal answers Still, keep the random slice separate in reports. Otherwise the targeted samples make quality look worse than it is.
Writing Scores Back to the Trace A score is only useful next to the request that earned it. So write each score as an event or attribute keyed to the trace ID, with the evaluator name, the evaluator version and a timestamp.
Keep the evaluator version, because graders change too. When a pass rate moves, the first question is whether the app changed or the ruler did.
Catching Regressions After a Prompt or Model Change Also compare quality by version, not only by day. A pass rate that looks stable overall can still hide a drop on one prompt version, offset by gains on another.
Watch for slower drift as well. User questions shift with seasons, launches and news, so an index that covered last quarter’s questions can fall behind without any code change.
A weekly look at questions with weak retrieval scores catches that early. It also ties back to the AI data quality work that keeps source documents current.
Alerting on LLM Behavior Without Alert Fatigue An alert is a promise that someone will act. Every LLM alert therefore needs a signal, a trigger, a first place to look and an owner. The matrix below is a starting template.
Signal Example trigger First place to look Owner p95 latency 50% above the 7-day baseline for 15 minutes Slowest spans in recent traces Platform team Provider errors Rate-limit or timeout share above 2% Error type by model and provider Platform team Cost per request Daily average 30% above forecast for one feature Input tokens and retry counts by feature Product engineering Retrieval health Empty-result rate doubles after a re-index Index version on retrieval spans Data team Answer quality Sampled pass rate down 10 points across 200+ scored answers Failures grouped by prompt and model version AI engineering User feedback Negative feedback share doubles day over day Traces linked to the feedback events Product owner
These thresholds are examples, not recommendations. Each team’s numbers should come from its own traffic.
Start Thresholds From a Measured Baseline Run the instrumentation for two to four weeks before turning on paging alerts, so the baseline reflects real traffic. During that window, measure the normal range for each signal by hour and weekday. Then set thresholds outside it, with business impact deciding how far outside.
Give Quality Alerts a Minimum Sample Quality scores are noisy, and a grader can even disagree with itself on borderline answers. So require a minimum number of scored answers before a quality alert can fire. Use a sustained window rather than one bad hour, too.
Segment baselines by use case as well. A billing assistant and a marketing copy tool should never share one quality threshold.
What a Useful Alert Carries A good alert lets the on-call engineer start investigating without opening five tools. It names the metric, the size of the change, the time window and the affected feature. It also lists the prompt and model versions in play, three to five example trace IDs and the owning team.
Checklist
Generative AI Checklist
Kanerika’s 18-action checklist for planning, deploying and governing generative AI, from data readiness and security to evaluation and governance.
Get the Checklist → From Alert to Fix: One Production Incident, Traced Here is how the pieces fit together, using the support assistant again. This is a composite example built for illustration, not a client incident.
At 10:40 a quality alert fires. The sampled grounding pass rate for refund questions has fallen from 91% to 72% across 240 scored answers. Latency and error rates, meanwhile, have stayed flat.
First, the on-call engineer groups the failing traces by version. Every failure carries index version policy-2026-10-01, published the evening before, while the prompt and model versions match the healthy traces.
Next, opening three failing traces shows the retrieval span returning the retired refund policy as its top document. It turns out the new index was built from a folder that still held last year’s policy file.
Once the data team removes the stale file and re-indexes, the engineer watches the next 200 scored answers. When the pass rate is back at baseline, the incident closes and a retrieval check joins the release checklist.
No step in that story needed a new tool.
Instead, it needed the index version on a span, a score on the trace and an alert with example IDs.
Six LLM Observability Mistakes That Cost Teams Weeks Each of these is cheap to avoid on day one and painful to fix after launch.
Watching latency without quality. Fast answers can still fail the user.Logging without shared trace IDs. Disconnected logs cannot reconstruct a failure.Capturing every prompt by default. Storage cost and privacy exposure grow together.Skipping version metadata. Without prompt and model versions, a regression cannot be pinned to a change.Treating grader scores as truth. Automated checks are noisy, so pair them with human review on a sample.Building dashboards with no owners. Detection without a named responder only moves the problem.Retrofitting any of these into a live app costs far more than designing them in. Prompt capture is the hardest to undo, because copies of sensitive text spread into every log store that touched it.
On-Demand Webinar
The Real Cost of LLM Security Risks and How to Reduce Them
An on-demand Kanerika session on where LLM applications leak data, what those risks cost, and the controls that reduce them.
Watch the Webinar → Keep the Instrumentation Independent of the Backend Whichever backend a team picks, the instrumentation described above should not change. Spans that follow the OpenTelemetry GenAI conventions can go to a self-hosted collector, an existing tracing stack or a dedicated LLM platform. Moving between them means changing exporter configuration, not application code.
One backend choice does follow straight from instrumentation, though: where captured prompt content is allowed to live. Suppose Step 6 puts full prompts in scope and residency rules keep that data in one region or tenant. Then the backend has to be self-hosted or run in-tenant, a setup common for teams running private LLMs . The rest is tool selection, which the LLMOps observability tool comparison and the roundup of AI observability tools cover.
How Kanerika Instruments LLM Applications Kanerika builds and runs LLM applications for enterprise teams through its LLM development and generative AI services. Observability goes in during the build rather than after the first incident, because retrofitting trace context into a live app is slow and risky.
Because Kanerika builds enterprise solutions on both Anthropic’s Claude and OpenAI’s models, its instrumentation has to stay provider-neutral. A model swap then shows up as a version change on the trace, not as a re-instrumentation project.
In practice, the delivery approach follows five stages.
Assess. Map each request path, the data it touches and the questions the business needs answered about it.Design. Choose the signals, span attributes, content-capture tier and retention rules, with security and compliance teams in the room.Build. Instrument with OpenTelemetry, add version metadata and wire sampled quality scores to the traces.Govern. Tie access to prompt data, audit trails and retention into the client’s AI governance framework .Enable. Hand over dashboards, the alert matrix and runbooks so the client’s own team owns the on-call rota.Tracing Built Into an AI DataOps Platform The same habits run through Kanerika’s product engineering work. For a SaaS company building an AI-powered DataOps platform , schema drift kept breaking pipelines. Worse, there was no end-to-end tracing to locate failures quickly.
So Kanerika added correlation-ID tracing across the platform, with masking for sensitive data, alongside a CI/CD pipeline and AWS infrastructure. The platform now runs with 37% lower operational costs and 65% faster customer onboarding, while manual onboarding tasks saw 10X automation.
The same principle carries over to LLM apps: design tracing and masking in from the start rather than retrofitting them after the first incident.
Case Study
65% Faster Onboarding for an AI DataOps Platform
Kanerika engineered an AI-powered DataOps platform with correlation-ID tracing and built-in masking of sensitive data, cutting operational costs by 37% and speeding customer onboarding by 65%.
Read the Case Study → Wrapping Up LLM observability answers the question uptime charts cannot, which is whether an answer was right and why. To get there, trace each request end to end and record tokens, latency, versions and retrieval results on its spans. Then write quality scores and user feedback back to the same trace, while keeping prompt content opt-in and redacted.
Finally, alert on quality and cost with thresholds from a real baseline, each alert carrying an owner and example traces. A team with that in place can explain a bad answer in minutes instead of days.
Frequently Asked Questions
What is LLM observability? LLM observability is the practice of collecting connected telemetry from an LLM application so a team can explain any response. It joins traces, token and latency metrics, retrieval details, quality scores and user feedback on one request ID. The goal is to see what an answer cost, how long it took, whether it was right and why it failed.
How is LLM observability different from LLM monitoring? Monitoring watches known metrics against known thresholds, such as latency, error rate or daily token spend, and raises an alert when one crosses a line. Observability lets an engineer investigate questions nobody planned for by drilling into traces, versions and quality scores for individual requests. Monitoring says something is wrong, and observability shows where and why.
What are LLM traces and spans? A trace is the full record of one request, from the user’s question to the final response. A span is one timed operation inside that trace, such as a retrieval call, a model call or a tool call. Each span carries a parent, timing, status and attributes like model name, token counts and finish reason.
What metrics should every production LLM application track? Start with p95 latency, time to first token, error rate by type, input and output tokens, and estimated cost per request. Add retrieval health for RAG apps, such as empty-result rate and top similarity score. Then track a sampled quality pass rate and the share of negative user feedback, segmented by feature and prompt version.
What is OpenLLMetry? OpenLLMetry is an open-source project from Traceloop, licensed under Apache 2.0, that adds LLM instrumentation on top of OpenTelemetry. It wraps common LLM provider SDKs and vector databases and emits standard OpenTelemetry traces. Because the output is standard, teams can send it to the tracing backend they already run instead of adopting a new one.
How do you monitor hallucinations in production? No single check catches every hallucination, so combine signals. Score a sample of answers for grounding against the retrieved documents, watch retrieval health for empty or low-scoring results, and track negative feedback and escalations. Attach every score to the original trace so a failed answer can be traced back to its prompt, documents and model version.
How can teams add LLM observability without logging sensitive prompts? Capture metadata such as tokens, latency, versions and document IDs for every request, and treat prompt and response text as opt-in. OpenTelemetry’s GenAI conventions already default content capture to off. When content is genuinely needed for debugging, redact it, store it separately with short retention, restrict access and audit who reads it.
Does LLM observability add latency to an application? Very little when it is set up well. Span creation is cheap compared with a model call that takes seconds, and exporters send telemetry in the background through batching. The bigger risks are synchronous logging of large prompts and grading answers inline, so run quality scoring asynchronously on a sample of traces after the response is sent.
Can LLM observability detect prompt injection or data leaks? It helps, but it is not a security control on its own. Traces show unusual tool calls, sudden prompt growth and responses that echo system instructions, which are common signs of injection. Detection rules and guardrails do the blocking, while observability gives security teams the evidence trail to investigate and confirm what happened.
How do you track LLM token costs per feature? Record input and output token counts on every model span, then tag each request with its feature, tenant and environment. A downstream job applies the current rate card, which avoids hard-coding prices in the app. Track retries as a separate counter, because silent retry loops are a common hidden source of cost growth.
How do you monitor LLM applications in real time? Stream span-level metrics such as latency, time to first token, error type and token counts into dashboards with short refresh windows. Pair them with alerts built from a measured baseline and a sustained window. Quality signals arrive with a short delay, because sampled scoring runs after the response, so alert on them with a minimum sample size.
