Observability: Prometheus & Grafana

You can’t fix what you can’t see. And in production, “I’ll add some logs and check tomorrow” is what tomorrow’s incident is built on.

Viglet Turing ships with a complete observability stack designed to be flipped on at install time and left alone:

• Spring Boot Actuator exposes a Prometheus endpoint at /actuator/prometheus.
• Prometheus scrapes it every 5 seconds and stores the time series.
• Grafana renders four pre-built dashboards over the metrics, plus the Chat Analytics dashboard that reads from the analytics REST API.
• Micrometer emits stable metric names so dashboards survive upgrades.
• Observation API bridges metrics ↔ tracing: every LLM call, every search, every snapshot lookup is both a timer and a span.

You don’t have to write a single PromQL query to get value. The default dashboards cover 90% of operational questions out of the box. The other 10% (when something genuinely odd is happening) is where knowing the metric names pays off.

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The Stack at a Glance

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graph LR
    APP["☕ Turing<br/>Spring Boot 4"]
    ACT["📡 Actuator<br/>/actuator/prometheus"]
    MICR["📊 Micrometer<br/>+ Observation API"]
    PROM["📈 Prometheus<br/>scrape every 5s"]
    GRAF["📉 Grafana<br/>4 dashboards"]
    OTLP["🛰 OpenTelemetry<br/>(optional tracing)"]
    INF["♾ Infinity DS<br/>(Chat Analytics)"]
    API["🔌 REST API<br/>/api/system/chat-analytics"]

    APP --> MICR
    MICR --> ACT
    MICR -.optional.-> OTLP
    ACT --> PROM --> GRAF
    APP --> API --> INF --> GRAF

    classDef orange fill:#fed7aa,stroke:#EA580C,stroke-width:2px,color:#1a1a1a
    classDef blue fill:#dbeafe,stroke:#4A90D9,stroke-width:2px,color:#1a1a1a
    classDef purple fill:#ede9fe,stroke:#9B6EC5,stroke-width:2px,color:#1a1a1a

    class APP,API blue
    class MICR,ACT,OTLP purple
    class PROM,GRAF,INF orange

Component	Role	Where it runs
Spring Boot Actuator	Exposes the metrics endpoint	Inside Turing, port 2700
Micrometer	Metrics library: counters, timers, gauges	Embedded in Turing
Observation API	Unified abstraction: one observation, two outputs (metric + span)	Embedded in Turing
Prometheus	Pull-based scraper, time-series database	containers/prometheus/ (port 9090)
Grafana	Visualization, dashboards, alerts	containers/grafana/ (port 3000)

Both Prometheus and Grafana are provisioned as Docker images in the containers/ directory. A docker compose up brings them up alongside Turing.

You don’t have to use them

Observability is optional. Turing runs perfectly without Prometheus or Grafana: Actuator just exposes the endpoint, and nothing scrapes it. But once you’ve seen a real incident through these dashboards, you’ll wonder how you operated anything without them.

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What Gets Measured

Stable metric names are the contract between Turing and your dashboards. They’re documented in TurMeterNames.java and treated as a public API: they don’t change between minor versions without a migration note.

LLM Calls

Every call to a language model (chat, streaming, embedding) flows through TurLlmObservation. Both the AI Agent loop and the Chat Analytics enricher use it.

Metric	Type	Tags	What you learn
turing.llm.calls	Timer	provider, operation, status	Latency per call, error rate per provider
turing.llm.tokens	Counter	provider, model, direction	Token usage by provider/model, split into in and out

provider is the LLM vendor (anthropic, openai, gemini, azure_openai, ollama). operation is chat, stream, or embed. status is success or error.

The same series carries traffic from chat (/api/v2/llm/.../chat) and from the Chat Analytics enricher. To split them, filter by model (the enricher uses whichever model your default LLM is set to).

Search

Every call to a search engine plugin (Solr, Elasticsearch, Lucene) is observed.

Metric	Type	Tags	What you learn
turing.search.calls	Timer	engine, operation, status	Engine-side latency and error rate
turing.search.pipeline	Timer	stage, status	Java-side pipeline stages: search.total, snapshot.build, search.response, result.process, widget.build
turing.search.snapshot.cache	Counter	outcome (hit / miss / evict)	Snapshot cache hit ratio: drives latency dramatically

The split between turing.search.calls and turing.search.pipeline is deliberate: the first is the backend engine call (mostly Solr time); the second is everything Turing does around the call (snapshot lookup, result mapping, facet processing, widget assembly). When latency spikes, you’ll know whether to blame the engine or the orchestration.

Chat Analytics

The AI-on-AI pipeline emits its own observability so you can see whether it’s keeping up.

Metric	Type	Tags	What you learn
turing.chat.analytics.enrich.cycle	Timer	status (success / empty / skipped)	How long each scheduled cycle takes; whether cycles are getting skipped (e.g., LLM offline)
turing.chat.analytics.classify	Timer	outcome (classified / unclassified / error / skipped / empty_transcript)	Per-session classification latency split by outcome
turing.chat.analytics.classifications	Counter	outcome	Cumulative count by outcome: “how many sessions get classified vs. how many fail”
turing.chat.analytics.inflight.size	Gauge	n/a	Current size of the in-memory in-flight map. Slow growth here is a leak.

If the gauge starts trending up and never comes down, the periodic sweep isn’t keeping pace: investigate by looking at agent flows that don’t end cleanly.

Beyond Micrometer: the analytics REST surface

The metrics above watch the pipeline’s health. The conversation analytics themselves, the nine timeseries metrics (sessions, goal_achievement_rate, negative_sentiment_rate, avg_duration_ms, avg_tokens_out, tool_calls_per_session, tool_errors_per_session, avg_tool_latency_ms, tool_error_rate_pct), per-tool p95 latency, funnel, and sentiment trajectory, are served by the Chat Analytics REST API and rendered in the turing-chat-insights Grafana dashboard, not as raw Micrometer series. Two of those diagnostic surfaces, router decisions and slot-SSE channels, are in-memory and per-node: they show only the JVM you queried and reset on restart.

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How to Bring the Stack Up

Local Development

cd containers
docker compose up -d prometheus grafana

This starts both containers with the provided prometheus.yml and Grafana provisioning.

containers/prometheus/prometheus.yml points at the host machine:

scrape_configs:
  - job_name: turing-es
    metrics_path: /actuator/prometheus
    scheme: http
    static_configs:
      - targets:
          - host.docker.internal:2700
        labels:
          application: turing-es
          instance: turing-dev

host.docker.internal resolves to the Docker host on Windows and macOS. On Linux, ensure extra_hosts: ["host.docker.internal:host-gateway"] is added to the Prometheus service in docker-compose.yml.

Once running:

• Prometheus UI → http://localhost:9090
• Grafana → http://localhost:3000 (default admin/admin on first login)

Production

For production, Prometheus runs as part of your existing observability stack (often Kubernetes-deployed), scraping Turing via the same /actuator/prometheus endpoint. The dashboards in containers/grafana/dashboards/ are JSON files: drop them into your existing Grafana instance via Configuration → Dashboards → Import JSON.

The Infinity datasource (used by the Chat Analytics dashboard) reads from Turing’s REST API directly, no Prometheus needed for that one. It needs:

• Allow-list for the URL http://<turing-host>:2700/api/system/chat-analytics/*
• A datasource named turing-chat-analytics so the dashboards find it

Grafana provisioning files in containers/grafana/provisioning/datasources/ show the expected configuration; copy them into your environment.

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The Pre-Built Dashboards

Four dashboards ship in containers/grafana/dashboards/. Each tells a different story.

1. turing-search: Operational Search Health

The on-call dashboard. If pages are loading slowly, this is where you go.

• Search latency p95 / p99 by site
• Engine call latency broken down by engine (Solr, Elasticsearch, Lucene) and operation
• Pipeline stage breakdown: snapshot.build vs. search.response vs. result.process vs. widget.build
• Snapshot cache hit ratio: the single biggest lever on search latency
• Error rate: turing.search.calls{status="error"}

A high snapshot.build time with low search.response time means “Solr is fast, but we’re rebuilding a snapshot too often”: increase TTL or pre-warm.

2. turing-search-insights: Product Search Quality

The PM dashboard. Used to understand what users are looking for.

• Top search terms over time
• Zero-result rate: what users searched for but found nothing
• CTR per term (click-through rate from results)
• Result-position click distribution: are users clicking position 1, or scrolling to find what they want?

3. turing-search-term-detail: Drill-Down

Linked from the term-list dashboard. Pick a term, see the full picture: query volume, click-through rate, top clicked documents, time-of-day distribution.

4. turing-chat-insights: Chat Analytics

The full Chat Analytics dashboard. Reads from /api/system/chat-analytics/** via the Infinity datasource (engine-agnostic). See Chat Analytics for the panel-by-panel breakdown.

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Useful Queries

“Are we healthy right now?”

# Error rate across all LLM providers, last 5 min
sum(rate(turing_llm_calls_seconds_count{status="error"}[5m]))
/ sum(rate(turing_llm_calls_seconds_count[5m]))

If this stays below 1%, you’re healthy. Spikes usually correlate with provider rate-limiting or transient network issues.

“What’s our search p95?”

# Search engine call p95, by site
histogram_quantile(0.95,
  sum by (le, engine) (rate(turing_search_calls_seconds_bucket[5m])))

For pipeline stages (Java-side):

histogram_quantile(0.95,
  sum by (le, stage) (rate(turing_search_pipeline_seconds_bucket[5m])))

If the engine number is fast but the pipeline is slow, the bottleneck is in Turing’s orchestration (often snapshot.build).

“What’s our cache hit ratio?”

rate(turing_search_snapshot_cache_total{outcome="hit"}[5m])
/ on() rate(turing_search_snapshot_cache_total{outcome=~"hit|miss"}[5m])

Below 80% means snapshots are evicting too often or TTL is too short.

“Is the chat-analytics enricher keeping up?”

# Cycles where status != "success", the enricher skipped or failed
rate(turing_chat_analytics_enrich_cycle_seconds_count{status!="success"}[15m])

If this rises, the enricher is being skipped (LLM offline) or erroring. Cross-reference with turing.chat.analytics.classifications{outcome="error"}.

“How much are the AI Agents costing me, per provider?”

# Output tokens per second, last 1h, by provider
sum by (provider) (rate(turing_llm_tokens_total{direction="out"}[1h]))

Multiply by your provider’s per-token cost to get a real-time burn rate. Combined with the model tag, you can see which model is dominating cost.

Prefer real dollars

This PromQL is a quick burn-rate estimate. For actual USD (frozen per turn from an editable price table, broken down by agent/model/stage, with soft budget caps) use Cost Governance instead of multiplying token rates by hand.

“Is the in-flight map leaking?”

# Gauge: should oscillate, not climb
turing_chat_analytics_inflight_size

A monotonically rising line = leak. The hourly sweep should keep this stable.

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Tracing (Optional)

Micrometer’s Observation API produces both metrics and spans. If you wire an OpenTelemetry exporter (Jaeger, Tempo, Honeycomb, Datadog) every observation becomes a trace span automatically.

Enable in application.yaml:

management:
  tracing:
    sampling:
      probability: 1.0
  otlp:
    tracing:
      endpoint: http://localhost:4318/v1/traces

Sampling at 1.0 (every request) is fine for low-traffic deployments and during incident debugging. For high-traffic production, drop to 0.05–0.1.

When tracing is on, every LLM call, search, and snapshot lookup gets a span with the same low-cardinality tags as the metrics. You can pivot between “how slow is this in aggregate” (Grafana) and “why was this specific request slow” (Jaeger) without changing instrumentation.

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When to Add Custom Metrics

The shipped metrics cover the common operational questions. You might want to add custom ones when:

• A specific business KPI matters (e.g., conversion rate from a specific agent, you’d derive this from Chat Analytics, not Prometheus).
• A subsystem has its own SLO (e.g., a particular MCP server you depend on).

Pattern to follow (io.micrometer.core.instrument):

@Service
public class MyService {
    private final MeterRegistry meterRegistry;

    public MyService(@Autowired(required = false) MeterRegistry meterRegistry) {
        this.meterRegistry = meterRegistry;
    }

    public void doWork() {
        Timer.Sample sample = meterRegistry == null ? null : Timer.start(meterRegistry);
        try {
            // ...
            recordOutcome(sample, "success");
        } catch (Exception e) {
            recordOutcome(sample, "error");
            throw e;
        }
    }

    private void recordOutcome(Timer.Sample sample, String status) {
        if (meterRegistry == null || sample == null) return;
        sample.stop(Timer.builder("my.service.work")
            .tag("status", status)
            .register(meterRegistry));
    }
}

The meterRegistry == null check is important: it makes the metric optional, so unit tests run without an observation registry.

Add the metric name to TurMeterNames.java so it’s part of the public observability contract.

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Where Logs Fit

Metrics tell you something is wrong. Logs tell you what is wrong.

Turing uses standard SLF4J logging. In production, three options:

Approach	When
Standard output → log aggregator (Loki, ELK, Datadog)	Recommended. Your existing log stack already handles this; nothing Turing-specific.
MongoDB Logback appender	When operators need to read logs from the admin console without server access. See Logging.
Both	Common in production. Stdout for the platform team, MongoDB for product/support.

Metrics + logs + traces is the classic three-pillar observability story. Turing gives you all three out of the box; the only thing you have to wire is the destination.

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Common Operational Patterns

Latency spike, no error spike

Almost always a downstream provider issue. Check turing.llm.calls{provider, status}: if success count drops and latency rises, the provider is rate-limiting or the network is slow. The chat analytics enricher will also slow because it shares the LLM stack: watch turing.chat.analytics.classify for confirmation.

Token cost climbing without traffic climbing

Probably a model upgrade. Confirm by comparing yesterday’s rate(turing_llm_tokens_total{direction="out"}[1d]) to last week’s, split by model. A new model might be more verbose by default.

Search p99 high, p50 fine

A subset of queries is slow. Check turing.search.pipeline p99 vs. p50 by stage. If snapshot.build is the culprit, a specific site has cold-cached snapshots: pre-warm or raise TTL.

Chat goal-rate dropping but volume rising

This is the signal of a quality regression that won’t show in basic metrics. You’re getting more conversations, but a lower share are achieving their goal. Either:

• Marketing pulled in lower-intent traffic (look at the intentLabel distribution, EXPLORATION rising is a good sign here),
• Or your agent recently changed and started failing: drill into Chat Analytics → scorecard by agent → find the dragger.

Mongo growing, sessions not classified

Look at turing.chat.analytics.classifications{outcome} over time:

• If error is rising → something’s wrong with the classifier (default LLM offline, prompt too long, etc.).
• If unclassified is rising → the model’s JSON parsing is failing: check intentConfidence averages. Consider a stronger default LLM.
• If everything’s skipped → the default LLM in Global Settings is disabled.

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Configuration Reference

# Spring Boot Actuator: Prometheus endpoint
management:
  endpoints:
    web:
      exposure:
        include: health,info,prometheus
  metrics:
    tags:
      application: turing-es
      instance: ${HOSTNAME}
  tracing:
    sampling:
      probability: 1.0          # 1.0 in dev, 0.05 in production
  otlp:
    tracing:
      endpoint: http://localhost:4318/v1/traces   # optional: Jaeger / Tempo / etc.

The Actuator endpoint is exposed unauthenticated by default in dev. In production, secure it (Spring Security, IP allowlist, ingress restriction) or move it to a separate management port.

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Related Pages

Page	Description
Architecture Overview	Where the observability layer fits in the system
Chat Analytics	The companion analytics for chat conversations
Logging	The third pillar: application logs
Configuration Reference	All management.* and turing.* properties

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