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Short answer: an observability ontology or semantic model gives telemetry a shared meaning, while a service catalog describes software entities and the organizational context around them. The first helps tools interpret and correlate traces, metrics, logs, profiles, and resources; the second helps people discover components, APIs, infrastructure, and ownership relationships.
They are complementary rather than competing systems. A telemetry record can identify the service that produced it, and an engineering portal can use that identity to open the matching catalog entry. The connection is useful, but neither model replaces the other.
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Start with the model’s primary job
The word ontology is broader than the specific OpenTelemetry material covered here. OpenTelemetry documents semantic conventions: a shared vocabulary and set of field rules for telemetry. Those conventions are a concrete observability semantic model, not a claim that one universal ontology covers every observability product or organization.
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A service catalog has a different center of gravity. It is a directory and relationship model for software and organizational entities. Backstage’s documented software-catalog model is a useful example: it represents components, APIs, resources, and organizational entities, then retrieves and displays those entities so teams can discover them.
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The distinction can be stated as two questions:
- Observability semantics: What does this piece of telemetry mean, and can different producers and consumers interpret it consistently?
- Service catalog: What software exists, how is it related, and what organizational context is attached to it?
If you need consistent metric units, attribute names, valid values, span meaning, or resource identity, begin with semantic conventions. If you need a searchable inventory of components, APIs, infrastructure, and associated people, begin with a catalog.
What an observability ontology or semantic model does
OpenTelemetry describes semantic conventions as a common set of semantic attributes that provide meaning when data is collected, produced, and consumed. The benefit is a common naming scheme that can be standardized across a codebase, libraries, and platforms. In practical terms, the model sits between raw instrumentation and the systems that analyze telemetry.
It standardizes names, types, units, and meanings
A convention is more specific than a label. It establishes what a field represents, how it is named, what type it has, what values are valid, and, where relevant, which unit applies. OpenTelemetry’s documentation covers conventions for items such as span names and kinds, metric instruments and units, and attribute names, types, meanings, and valid values.
Without that shared vocabulary, two teams can record the same concept under different names or with incompatible units. A dashboard may then treat milliseconds as seconds, a query may miss one team’s differently named attribute, or a trace-processing rule may classify equivalent operations differently. Semantic conventions reduce those interpretation problems before data reaches a backend.
It applies across multiple telemetry signals
The OpenTelemetry model is not limited to distributed traces. Its documented semantic conventions cover traces, metrics, logs, profiles, and resources. Resources are especially important because they describe the entity that produced telemetry, allowing signal types to be associated with the same service or workload.
This cross-signal scope lets a team use one vocabulary when it correlates a trace with a metric spike, a log record, or a profile. The conventions do not magically perform the correlation; they make the identifying concepts consistent enough for collectors, storage systems, and analysis tools to do so.
It gives service identity a precise shape
OpenTelemetry describes a service as a logical component that produces telemetry. Its service conventions distinguish a service namespace, the logical service, and an individual service instance. The namespace can group related services, the service identifies the logical component, and the instance distinguishes one running copy from another.
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Consider a blog-site system with an HTTP server and a database. They can be represented as distinct services even though both belong to the same broader application. Multiple running copies of the HTTP service can then be distinguished as instances. That hierarchy prevents a dashboard from collapsing every process into one ambiguous name while still allowing roll-up to a service or namespace.
It answers interpretation questions, not ownership questions
A semantic model can tell a consumer what an attribute means and how it should be interpreted. It can establish that a value is a duration, identify a metric’s unit, or define the valid values for a field. It does not, by itself, tell an engineer which team owns the service, where its source repository lives, which API it exposes, or what infrastructure supports it.
What a service catalog models
A service catalog is an inventory of entities and their relationships. In Backstage’s technical overview, the system model is based on entities and includes several important categories.
Components
Components are software pieces tracked in source control. Depending on an organization’s conventions, a component might represent a deployable service, a library, a website, or another maintained software unit. The catalog record makes that unit discoverable without requiring every engineer to know where its metadata is stored.
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APIs
APIs describe boundaries between components. Recording those boundaries lets a reader see what a component provides or consumes and how software units fit together. An API entry is a relationship and contract concept; it is not a telemetry attribute standard.
Resources
Resources are physical or virtual infrastructure needed to operate a component. Examples can include a database, queue, cluster, or other runtime dependency. The catalog’s resource relationship provides operational context around a component, while telemetry semantic conventions provide consistent descriptions of signals emitted by the component or resource.
Organizational entities
The model also includes organizational entities such as users. Those records supply the human context that a telemetry standard does not attempt to provide: who is associated with a component, which organizational structure applies, or where a team can look for responsibility information.
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Discovery is the catalog’s central outcome
The catalog retrieves entities and displays them for discovery. A developer can search for a component, inspect its APIs and resources, and find its organizational context in one place. Exact fields, ingestion workflows, and governance rules vary by implementation; Backstage is an example of a documented catalog model, not a universal service-catalog schema.
Observability semantics and service catalogs compared
| Axis | Observability semantics or ontology | Service catalog |
|---|---|---|
| Primary purpose | Consistent interpretation and correlation of telemetry data | Discovery and organization of software and organizational entities |
| Main subject | Signals, operations, resources, attributes, names, units, and values | Components, APIs, resources, and organizational entities |
| Typical users | Instrumentation authors, platform engineers, and telemetry producers and consumers | Developers and platform teams looking for software and its context |
| Typical question | What does this field mean, and can tools interpret it consistently? | What software exists, how is it related, and where is its ownership context? |
| Primary output | Telemetry that follows shared names, types, meanings, units, and values | A searchable inventory of entities and their relationships |
| Main change concern | Convention or schema changes can break consumers that depend on an earlier telemetry shape | Entity accuracy and relationship maintenance determine whether discovery information remains useful |
The overlap in the table is intentional: both models describe systems and relationships, but they do so for different consumers. A semantic model optimizes for machine interpretation of emitted data. A catalog optimizes for human and organizational discovery of software entities.
Where the two models meet
A practical integration pattern starts with service identity. An OpenTelemetry resource can identify a service with attributes such as its service name and namespace, and can distinguish an individual service instance. A catalog can contain a corresponding component with APIs, resources, and organizational context. A portal or observability interface can use the shared identity to move from a trace, metric, log, or profile to the component record.
This connection is an architectural inference from the documented roles of the two models. OpenTelemetry’s semantic-conventions documentation and Backstage’s catalog documentation do not, by themselves, prescribe one integration product or workflow. The value comes from designing a mapping that your organization governs.
A step-by-step identity mapping
- Choose the canonical logical service identifier. Decide which catalog component represents the service that emits telemetry. Avoid using a short-lived pod, process ID, or deployment hash as the primary logical identity.
- Emit that identity in telemetry resources. Instrumentation and collectors should attach the agreed service name and, where needed, service namespace and instance identity. Keep the logical service stable while allowing instances to change as deployments scale.
- Store the same identity in the catalog. Add the telemetry-facing identifier to the component metadata or an equivalent catalog field. If your catalog supports aliases, record legacy names during migrations rather than silently changing history.
- Resolve the relationship at query time or ingestion time. An observability UI, portal plugin, or internal service can look up the catalog entity from the telemetry identity. The lookup can be performed when a user opens a trace or when data is enriched, depending on latency, privacy, and availability requirements.
- Expose useful context, not every catalog field. Link to the component page, ownership information, API documentation, and known resources. Do not copy an entire catalog record into every telemetry event; that increases volume and creates stale snapshots.
- Monitor mapping failures. Track telemetry that has no matching catalog entity, catalog entities with no recent telemetry, and identities that map to more than one component. These are data-quality signals, not merely UI defects.
Example of the handoff
Suppose a trace shows an operation emitted by the logical service checkout in the namespace retail. The observability system can use that identity to offer a link to the catalog component representing the checkout service. On the component page, the responder can inspect its exposed APIs, required database resource, and associated organizational entity. The trace still gets its meaning from semantic conventions; the ownership and relationship details come from the catalog.
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How the models fit into an engineering architecture
Instrumentation and application libraries
Application code and instrumentation libraries produce spans, metrics, logs, profiles, and resource information. Semantic conventions belong here because producers need a stable vocabulary when they name operations, attributes, instruments, and units.
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Collectors and telemetry pipelines
Collectors and processors consume the emitted shape. They can preserve, transform, or validate fields according to the conventions and schema version in use. A collector can also add deployment or environment context, but it should not invent a second meaning for an existing standardized field.
Telemetry backends and analysis tools
Backends depend on consistent names and types to index, aggregate, alert, and correlate data. A query that expects a conventional field is portable only when producers follow the same convention. This is the machine-facing part of the architecture.
Catalog ingestion and developer portals
A catalog ingests entity definitions and relationships, then presents them for discovery. Its workflows may be repository-based, API-based, or managed through another source of truth. This is the human- and organization-facing part of the architecture.
The linking layer
The linking layer joins the two without collapsing them. It may be a portal link, a backend lookup, a catalog plugin, or an internal mapping service. Its job is to resolve identity and present context, not to redefine telemetry semantics or turn the catalog into a metrics database.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Schema evolution: the compatibility issue to plan for
OpenTelemetry’s telemetry-schema documentation warns that a source and a consumer relying on a particular telemetry shape can become incompatible when that shape changes without corresponding changes on the other side. Schema URLs identify the schema version associated with a telemetry shape.
What can break
- A field can be renamed, moved, or assigned a different meaning.
- A consumer can expect one type or unit while a producer emits another.
- A collector transformation can apply to an older shape but corrupt a newer one.
- A dashboard or alert can silently return incomplete results when it searches only for the old field.
A safer migration sequence
- Record the schema version associated with the data being emitted or transformed.
- Identify every producer, collector, storage mapping, dashboard, alert, and export that consumes the affected fields.
- Introduce the new shape with an explicit compatibility plan rather than changing the meaning in place.
- Run old and new consumers in parallel when the operational risk justifies it.
- Verify queries and alerts against both shapes, then remove the old path only after its consumers are retired.
A service catalog has a related but different evolution problem. Renaming a component, API, or resource relationship can break discovery links and ownership workflows even when telemetry remains valid. Keep catalog identifiers stable where possible, and treat renames as migrations with aliases and redirects.
When to prioritize one model, the other, or both
| Situation | Priority | Reason |
|---|---|---|
| Telemetry from several languages or teams uses inconsistent names and units | Observability semantic conventions | The immediate problem is interpretation and correlation across producers and consumers. |
| Engineers cannot find services, APIs, dependencies, or responsible people | Service catalog | The immediate problem is discovery and organizational context. |
| Incidents require jumping from a signal to ownership and dependency information | Both, with an identity mapping | Semantic telemetry identifies the emitting service; the catalog supplies context around that service. |
| Dashboards break after instrumentation changes | Schema governance first | Compatibility depends on managing telemetry shape and version changes. |
| The inventory is full of stale or duplicate services | Catalog governance first | Entity ownership, lifecycle rules, and relationship maintenance need attention before adding more integrations. |
Choose semantics first when data is the bottleneck
If teams cannot agree on what a metric or attribute means, a catalog will only organize inconsistent information. Establish naming, types, units, valid values, and resource identity before investing heavily in cross-tool discovery.
Choose a catalog first when context is the bottleneck
If telemetry is already understandable but responders do not know which component owns an endpoint or which infrastructure it depends on, a catalog addresses the missing human context. Its usefulness depends on assigning responsibility for keeping entities current.
Choose both for incident response and platform discovery
The combined design is strongest when an organization needs a path from signal to action: identify the service consistently, locate its catalog entity, inspect APIs and resources, and find the associated organizational contact. Keep each system authoritative for the information it was designed to model.
Common mistakes and how to correct them
Treating the catalog as a telemetry standard
A catalog can contain a field called latency, but that does not define the unit, aggregation semantics, or valid values of every latency metric emitted by every service. Use semantic conventions for telemetry meaning and catalog fields for entity metadata.
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A standardized service name does not identify the current team, repository, escalation path, or business owner. Those relationships belong in the catalog or another organizational system.
Using instance identity as the service identity
Instances are expected to change during scaling, rolling deployments, and failure recovery. Mapping a catalog component to an ephemeral instance creates churn and broken links. Map the logical service to the component, then retain instance identity for diagnosis.
Copying catalog data into every event
Embedding full ownership and dependency records in every span or log increases volume and creates stale data. Store a stable identity in telemetry and resolve richer context when a user needs it.
Changing a field without checking consumers
A local instrumentation change can become a platform-wide compatibility event. Use schema versions, inventory downstream consumers, test queries and alerts, and communicate the migration before removing the old shape.
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Backstage provides a documented model of entities, but organizations can choose different entity types, metadata sources, and lifecycle rules. Adopt the parts that fit your operating model and document deviations so integrations do not assume fields that your catalog does not provide.
A practical governance checklist
- Define which fields are governed by observability semantic conventions and which are catalog metadata.
- Assign an owner for the logical service identifier and document its namespace rules.
- Require every production service to publish stable identity in telemetry resources.
- Require every catalog component to expose the identity used for telemetry lookup.
- Validate that one identity maps to one logical component, with explicit exceptions.
- Record schema versions when telemetry shapes change.
- Test dashboards, alerts, exports, and enrichment jobs during schema migrations.
- Measure unmatched telemetry, duplicate catalog entities, and stale ownership records.
- Keep links from observability views to catalog pages short-lived only when the catalog identity itself is stable.
- Review the mapping whenever a service is renamed, split, merged, or retired.
The practical boundary
An observability semantic model is the language of telemetry. It makes names, attributes, units, values, resources, and operations understandable across instrumentation and analysis systems. A service catalog is the map of software and organizational context. It makes components, APIs, infrastructure, and associated people discoverable.
Use OpenTelemetry semantic conventions when the question is how data should be interpreted. Use a catalog such as the model documented by Backstage when the question is what exists and how it is related. Connect them through a governed service identity, preserve their separate responsibilities, and manage schema and entity changes as compatibility work rather than as cosmetic renaming.
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