What is Service Catalog Observability and How Does It Work?

A service catalog gives teams a shared view of their systems—what services exist, who owns them, how dependencies are structured, and the SLAs that guide expectations. It’s an important part of development infrastructure because it helps everyone speak the same language about services.

Service catalog observability builds on that foundation. By looking at how developers use the catalog—what they search for, which services they adopt, and how documented characteristics compare with runtime telemetry—you get a clearer sense of how the catalog contributes to everyday workflows.

The goal now is to see the catalog in motion: how it supports discovery, adoption, and integration across teams. With the right signals, observability turns the catalog from a static reference into a living part of the development process.

Add Observability Beyond the Catalog Metadata

A catalog does a great job of laying out the basics: which services exist, who owns them, how they depend on each other, and the SLAs they’re meant to meet. Observability adds the other half of the story, how that map is used in practice and how it lines up with runtime reality.

Here’s what that looks like:

• A developer searches for “authentication service,” but the catalog has it under “identity management.” The service is there and documented, but naming differences make it harder to discover.
• The Payments API page gets plenty of views, yet only a handful of integrations happen. The catalog shows what the service does, but observability shows where adoption slows down.
• A service entry says P99 latency target: 100 ms, while production data shows closer to 400 ms. Or dependencies documented in the catalog don’t fully match the call graphs visible in traces.

When catalog interactions are compared with production telemetry, you get more than static records, you see how the catalog actually drives discovery, adoption, and reliability.

Signals That Directly Impact Your Catalog

When you think about observability for a service catalog, three categories of signals make the biggest difference: discovery, adoption, and accuracy.

Discovery and Search Behavior

The first layer of observability is about search and navigation. Tracking queries, result sizes, and page views gives you a sense of how easily services can be found.

Here’s an example for a Backstage setup:

function handleCatalogSearch(searchTerm, resultSet, userId) {
  const resultCount = resultSet.length;

  metrics.counter('catalog_search_total').inc({
    result_count_bucket: bucketResultCount(resultCount),
    user_team: getUserTeam(userId)
  });

  if (resultCount === 0) {
    metrics.counter('catalog_search_empty').inc();
    logger.info('empty catalog search', {
      search_term: searchTerm,
      user_id: userId,
      timestamp: Date.now()
    });
  }
}

This shows how often searches happen, how many results they return, and when nothing comes back. Zero-result searches don’t mean the service doesn’t exist—they often just reveal differences in naming (for example, authentication in the search box vs. identity management in the catalog).

Service Adoption Flow

Adoption signals connect catalog visibility with actual usage. These are the milestones that trace the journey from interest to active use.

Credential requests are a strong early indicator:

async function generateServiceCredentials(serviceId, userId, type) {
  const credentials = await createCredentials(serviceId, userId, type);

  metrics.counter('service_credentials_issued').inc({
    service_id: serviceId,
    credential_type: type,
    requesting_team: getUserTeam(userId)
  });

  return credentials;
}

First API calls are another key signal. Together, these events build an adoption funnel: service viewed → docs opened → credentials issued → first call → recurring usage.

Patterns across this funnel don’t point to wrong behavior—they highlight where more guidance or support may help teams move forward faster.

Catalog Accuracy Validation

The catalog documents expectations: ownership, service tiers, latency targets, and dependencies. Observability helps check how closely those expectations align with runtime behavior.

You can attach catalog metadata to existing telemetry, for example in Prometheus:

- job_name: 'catalog-enriched-services'
  static_configs:
    - targets: ['service-a:8080']
      labels:
        catalog_owner: 'billing-team'
        catalog_tier: 'critical'
        catalog_sla_target: '99.9'

Once that context is in place, you can explore questions like:

• Are services meeting the SLAs they’ve published?
• Do runtime traces show dependencies that aren’t listed in the catalog?

These checks don’t label anything as wrong—they simply ensure the catalog stays aligned with how services behave in production.

Get Started with Minimal Instrumentation

A few simple hooks give you immediate visibility into how the catalog is used and how it connects with runtime data.

Phase 1: Search and Discovery

The simplest place to start is with catalog searches and page views. These show how developers move through the catalog and what terms they use.

For a Backstage deployment, you can track searches like this:

export const SearchResultsPage = () => {
  const handleSearch = async (term) => {
    const results = await catalogApi.search(term);

    analytics.track('catalog_search', {
      search_term: term,
      result_count: results.length,
      user_context: getCurrentUser()
    });

    return results;
  };

  return <SearchInterface onSearch={handleSearch} />;
};

For documentation-based catalogs, simple page-view instrumentation works just as well:

function instrumentPageViews() {
  if (isServicePage(window.location.pathname)) {
    const serviceId = extractServiceId(window.location.pathname);
    analytics.track('service_page_viewed', {
      service_id: serviceId,
      referrer: document.referrer
    });
  }
}

These events help you see which services draw attention, how often searches succeed, and where terminology might be mismatched.

Phase 2: Adoption Tracking

The next step is connecting discovery with the integration activity. Adoption signals give you that link.

Credential issuance is a strong early milestone:

async function generateServiceCredentials(serviceId, userId, credentialType) {
  const credentials = await createCredentials(serviceId, userId, credentialType);

  metrics.counter('service_credentials_issued').inc({
    service_id: serviceId,
    credential_type: credentialType,
    requesting_team: getUserTeam(userId)
  });

  return credentials;
}

Now, you can build an adoption funnel: page view → docs opened → credentials issued → first call → recurring usage.

The shape of the funnel shows you where more guidance or clarity may help teams move smoothly from interest to usage.

Phase 3: Runtime Correlation

Finally, connect catalog metadata with existing telemetry so you can see whether catalog expectations line up with runtime reality.

Using Prometheus, you can attach labels like owner, tier, or SLA target:

- job_name: 'catalog-enriched-services'
  static_configs:
    - targets: ['service-a:8080']
      labels:
        catalog_owner: 'platform-team'
        catalog_tier: 'production'
        catalog_sla_target: '99.9'

This lets you segment metrics by catalog attributes. For example:

• Compare latency across tiers to see if “critical” services perform differently from “non-critical.”
• Check whether SLA targets in the catalog match actual performance.
• Break down errors by service owner to confirm ownership metadata is accurate.

With this step, the catalog reflects how services behave in production.

Key Metrics and Analysis

The below metrics show how the catalog is used, how services move from discovery to adoption, and whether catalog entries stay aligned with runtime behavior.

Search Effectiveness

Search is often the first touchpoint with a catalog. Measuring it tells you how well the catalog supports discovery.

# Search success rate over time
(
  rate(catalog_search_total[5m]) - rate(catalog_search_empty[5m])
) / rate(catalog_search_total[5m]) * 100

This query calculates the percentage of searches that return at least one result. If the number trends down, it usually means developers are searching with terms that don’t match how services are cataloged. For example, someone types auth but the catalog entry is under identity.

You can also examine which queries most often come back empty:

# Most common failed searches
topk(10,
  sum by (search_term) (
    increase(catalog_search_empty[24h])
  )
)

This isn’t just about missing services. It’s a way to see how developers think about naming and categorization. If checkout is a top failed search but you only have payment, that’s a signal to align terminology, not that anyone is searching “wrong.”

Adoption Conversion

Catalog entries don’t end at discovery. Adoption metrics show how far services progress from “seen” to “in use.”

# Service adoption funnel
sum by (service_id) (
  catalog_service_adoption{adoption_step="service_page_viewed"}
) as viewed,
sum by (service_id) (
  catalog_service_adoption{adoption_step="documentation_opened"}  
) as docs_read,
sum by (service_id) (
  catalog_service_adoption{adoption_step="api_credentials_generated"}
) as credentials_requested

This builds a funnel for each service: how many developers viewed it, read the docs, requested credentials, and eventually made calls. A drop between steps doesn’t mean something is broken, it highlights where integration takes more effort.

For instance:

• A steep decline after docs opened suggests the documentation explains the service, but may not give enough guidance on next steps.
• A gap between credentials requested and first API call may hint at longer onboarding flows.

These insights help you understand the developer journey, not point fingers.

Catalog Accuracy

The catalog sets expectations for performance and dependencies. Observability checks whether those expectations hold in practice.

SLA compliance can be tracked by comparing runtime latency against the catalog’s stated targets:

# SLA compliance by catalog tier
avg by (catalog_tier) (
  (catalog_sla_target - histogram_quantile(0.99, http_request_duration_seconds) * 1000) 
  / catalog_sla_target
) * 100

This shows, for each tier, how close services come to their documented SLA. If “critical” tier services are consistently under their latency targets, that’s good evidence the catalog entries reflect reality. If they drift, it’s a cue to adjust either the SLA or the system behind it.

Dependencies are another area where drift is common:

# Dependency documentation accuracy
count by (caller) (
  sum by (caller, callee) (service_dependency_call_total) > 100
) - count by (caller) (
  sum by (caller, callee) (service_dependency_call_total{catalog_dependency="true"}) > 100
)

This query compares runtime calls with catalog-documented dependencies. A difference here doesn’t mean the catalog is “wrong”—it just shows where services talk to each other in ways the documentation hasn’t captured yet. Updating those entries keeps the catalog useful during incident response.

5 Tools for Service Catalog Observability

Service catalog observability doesn’t require building a new stack from scratch. The key is to connect catalog metadata with the telemetry tools you already run. These five cover the end-to-end workflow: from collection, to correlation, to visualization, to validation.

1. Last9

Service catalog observability produces high-cardinality data. Every signal can include service name, owning team, tier, SLA target, and dependency links. Combined, those dimensions explode into millions of unique series. Most monitoring systems choke here—queries slow down, storage costs spike, and teams are forced to drop context.

Last9 was designed to solve this. It supports 20M+ series per metric, with streaming aggregation and cost controls to keep telemetry usable at scale. Teams at Probo, CleverTap, and Replit rely on it to correlate catalog metadata with runtime performance.

Key reasons to consider Last9:

• Handles catalog metadata (like catalog_owner or catalog_sla_target) without forcing you to pre-aggregate or drop labels.
• Integrates with Prometheus remote-write and OpenTelemetry exporters, so you don’t need new agents.
• Provides Grafana-compatible dashboards, so existing workflows still apply.
• Includes cost visibility and policy controls, which are critical when catalog observability multiplies your telemetry volume.

If you expect service catalog observability to add significant cardinality, Last9 helps you keep that data without breaking performance or budgets.

2. Prometheus

Prometheus is often the first stop for catalog-aware metrics. By enriching service metrics with labels like catalog_owner, catalog_tier, or catalog_sla_target, you turn regular telemetry into catalog-aware observability.

Example:

http_request_duration_seconds{
  service="checkout-api",
  catalog_owner="payments-team",
  catalog_tier="critical",
  catalog_sla_target="150"
}

Strengths:

• Widely adopted and battle-tested for metrics collection.
• Easy to add catalog metadata through relabeling or service discovery.
• Strong ecosystem (Alertmanager, exporters) makes it flexible.

Considerations:

• Cardinality growth is the main limitation. Adding four or five catalog labels multiplies series count quickly.
• Prometheus scales well for teams with hundreds of services, but becomes operationally heavy at very large cardinalities unless paired with a backend like Last9, Cortex, or Thanos.

If you’re already running Prometheus, adding catalog metadata is a straightforward way to start.

3. Grafana

Grafana makes catalog signals explorable. Once Prometheus or Last9 has metrics enriched with catalog labels, Grafana’s templating lets you slice data dynamically.

Here are a few use cases:

• Dashboards segmented by service tier to show how “critical” services perform compared to “best effort” services.
• Ownership-based views, so each team can filter latency, error rate, or adoption funnels for the services they own.
• Dependency complexity visualizations, where catalog metadata helps you highlight clusters of tightly coupled services.

Strengths:

• Flexible templating avoids dashboard sprawl. You don’t need one per team; one dashboard can serve all, filtered by catalog_owner.
• Rich plugin ecosystem for tracing, logs, and external data.

A few considerations:

• Grafana is only as good as the data behind it. If Prometheus or Last9 isn’t enriched with catalog metadata, dashboards won’t provide the catalog context you need.
• Templating can get complex at scale—good label hygiene is essential.

Grafana is the best way to make catalog observability visible to engineering teams.

4. Jaeger

Catalog dependency graphs describe intended relationships. Traces show actual call patterns. Comparing the two validates whether the catalog reflects reality.

What Jaeger brings:

• Distributed traces capture caller → callee relationships with timing.
• Catalog metadata can be attached to spans, letting you group or filter traces by owner, SLA tier, or service type.
• Mismatches highlight undocumented dependencies or call paths that weren’t captured in the catalog.

Strengths:

• Ground truth for dependencies—what’s really happening in production.
• Useful during incidents, where knowing about hidden dependencies shortens MTTR.
• Integrates naturally with OpenTelemetry, so catalog attributes flow into trace spans.

Limitations:

• Storage costs can rise quickly if you sample heavily.
• Jaeger UI is powerful for debugging but less suited for broad analytics (you’ll often want traces plus metrics).

If part of your goal is catalog accuracy, tracing is essential.

5. Elastic / OpenSearch

While Prometheus handles metrics and Jaeger focuses on traces, many catalog interactions show up as logs or events—API access logs, search queries, documentation visits, or onboarding errors. Elastic and OpenSearch are well suited for storing and analyzing that data at scale.

How it helps

• Collects and indexes catalog usage events (search terms, page views, credential requests).
• Supports full-text search across catalog metadata, which is useful for analyzing how developers search versus how services are categorized.
• Can correlate adoption signals in logs with runtime telemetry in Prometheus or Last9.
• Integrates with dashboards (Kibana/OpenSearch Dashboards) for visualizing adoption trends, error spikes, or unusual usage patterns.

Strengths

• Handles large volumes of log data and makes it explorable.
• Flexible schema lets you attach catalog metadata as fields (e.g., service_owner, catalog_tier).
• Useful for anomaly detection on adoption flows or catalog searches.

Considerations

• Storage costs grow quickly without lifecycle management. You’ll want to archive or roll over indices to keep usage sustainable.
• Best used alongside metrics and tracing systems, not as a standalone solution.

Elastic / OpenSearch complements Prometheus, Grafana, and Jaeger by giving you visibility into the events around catalog usage—the searches, views, and adoption steps that explain why services are or aren’t being used.

Advanced Analysis Patterns

Beyond search, adoption, and SLA checks, there are deeper patterns worth exploring. These give you a view into how services evolve, how different teams adopt them, and how documentation quality affects usage.

Dependency Drift Detection

Catalogs describe which services depend on which, but those relationships change faster than documentation. Traces and call metrics tell you the truth about current runtime traffic. Comparing the two surfaces “drift” — where actual dependencies don’t match the catalog.

# Services with undocumented dependencies
(
  count by (caller) (
    sum by (caller, callee) (
      rate(service_call_total[1h]) > 0.1  # filter to meaningful call volume
    )
  )
) > (
  count by (caller) (
    sum by (caller, callee) (
      service_call_total{catalog_dependency="documented"}
    )
  )
)

This query highlights services that make regular calls not marked as dependencies in the catalog. Drift like this is common: teams add integrations quickly, but catalog entries lag behind. Catching it early makes the catalog more trustworthy for troubleshooting and architecture reviews.

Team Adoption Patterns

Adoption isn’t uniform across teams. Larger teams may have coordination overhead; smaller teams may move faster but adopt fewer services overall. Observability lets you measure these patterns instead of guessing.

# Service adoption rates by team size
avg by (team_size_bucket) (
  rate(catalog_service_adoption{adoption_step="first_api_call"}[7d])
) group_left (team_size_bucket) (
  team_metadata{team_size_bucket=~"small|medium|large"}
)

This groups adoption rates by team size. If you notice, for example, small teams reaching first API calls quickly but large teams lagging, that’s a signal to review how onboarding scales. It might point to documentation assumptions that don’t hold when multiple sub-teams are involved.

Documentation Quality Correlation

Documentation is often the difference between “I looked at this service” and “I successfully integrated it.” You can make that link visible by correlating doc metadata with adoption outcomes.

# Adoption rate vs documentation completeness
(
  sum by (service_id) (rate(service_first_api_call[7d]))
) / (
  sum by (service_id) (rate(service_page_viewed[7d]))
) * on (service_id) group_left (doc_completeness_score) (
  catalog_service_metadata
)

This ratio shows how often a page view leads to a first API call. Overlaying it with a documentation completeness score lets you see whether services with fuller docs convert better. If they do, you’ve got data to justify investing in documentation quality. If they don’t, the bottleneck lies somewhere else (credentials, SDK availability, etc.).

These patterns move catalog observability beyond “is the catalog used” into “how is it shaping development.” Drift detection tells you if the map matches the territory. Team adoption patterns show how different groups approach integration. Documentation correlation connects catalog quality with service uptake.

Final Thoughts

Service catalog observability turns a static catalog into operational infrastructure. But the challenge is scale.

Every service, owner, tier, dependency, or CloudFormation template multiplies the telemetry series. Add Kubernetes pods, AWS resources, and load balancers, and you’re dealing with millions of high-cardinality combinations across log data, metrics, and traces.

Most tools force you to drop context.Last9 was built for this problem.

We handle high-cardinality metrics, tie catalog metadata to runtime performance, and plug straight into Prometheus, OpenTelemetry, AWS, GCP and Azure.

With streaming aggregation, cost controls, and Grafana dashboards, you get clear visibility into service health, anomalies, and SLA compliance.

For developers, this means:

• Adoption funnels that pinpoint where integrations slow.
• SLA validation that compares catalog promises with CloudWatch and Kubernetes telemetry.
• Dependency checks that identify undocumented connections across APIs, endpoints, and load balancers.
• A workspace where catalog data, observability metrics, and incident context come together.

With Last9, your service catalog is measurable, accurate, and continuously validated against production.

Start for free today!

FAQs

What is observability as a service?
Observability as a service is a managed way to collect, store, and analyze telemetry—metrics, logs, and traces—without running your own infrastructure. Platforms connect to data sources like AWS resources, Kubernetes clusters, and cloud workloads, giving DevOps teams real-time service observability and root cause analysis without siloed tooling.

What is the concept of a Service Catalog?
A service catalog is a central inventory of services within an organization. It documents ownership, dependencies, SLAs, and endpoints. Teams use it for self-service discovery and integration, reducing friction across the service life cycle.

What is the Service Catalog in Datadog?
Datadog’s Service Catalog builds automatically from telemetry. It links services to dashboards, incidents, and SLOs, making it easier to track service health, issue resolution, and performance across Kubernetes or cloud environments.

What is the difference between a Service Catalog and an incident?
A service catalog describes intended services and their metadata. An incident is a real-time event when a service deviates from expected performance. Catalogs provide the context (like owners and SLAs), while incidents trigger anomaly detection and root cause analysis.

What are the key pillars of Data Observability?
Data observability usually covers five pillars:

1. Freshness — is log data and metric data current?
2. Volume — is the expected data flowing from each source?
3. Schema — do APIs, events, or CloudWatch metrics follow the template?
4. Lineage — can you track data through its life cycle?
5. Quality — are anomalies, vulnerabilities, or sensitive data issues detected?

How do I test for multiple values in a dashboard parameter?
Most tools (Grafana, Datadog, AWS CloudWatch) allow dashboards with parameters that accept multiple values. Queries expand to match a list of endpoints, EC2 instances, or load balancers. Testing is as simple as selecting multiple options and confirming the dashboard filters correctly.

Why does the MCP Server matter in AI workflows?
The MCP Server standardizes how AI agents fetch observability data. In AI-driven workflows, it pulls service health, log data, and metrics into the workspace, enabling anomaly detection and faster issue resolution.

How can Service Catalog Observability improve IT service management?
By linking catalog metadata with cloud observability platforms like AWS CloudWatch or Microsoft Azure Monitor, IT teams can measure adoption, validate dependencies, and enforce security posture. This makes service management proactive, not reactive.

How can observability enhance the management of a service catalog?
Observability connects catalog documentation with runtime telemetry. It lets you monitor endpoints, detect anomalies, validate SLAs, and see how services behave in Kubernetes or on-demand cloud environments. The result is a service catalog that stays accurate and supports real-time decision-making.