GenAI Span Attribute Reference

The OpenTelemetry GenAI semantic-convention attributes Cloud & AI Cost Management reads from each span to price and attribute AI cost.

GenAI semantic conventions are the standard OpenTelemetry attribute names for AI calls. They are what allow Cloud & AI Cost Management (CACM) to read a span and calculate cost: the provider, the model, and the token counts all come from these fields. A span that lacks them is still a valid trace, but it cannot be priced.

This page lists the attributes CACM reads, which ones are required to price a call, and which ones improve accuracy or enable grouping.

GENAI SEMANTIC CONVENTIONS REQUIRED

CACM prices only spans that carry GenAI semantic-convention attributes. Standard HTTP, database, or function spans do not carry the model name or token counts needed for cost, so they appear in a trace but are never priced. Go to How AI Traces Work to understand how spans become cost.

---

Minimum Attributes for Pricing

If you can only send a subset of attributes, CACM needs at minimum the following to calculate cost:

Attribute	Purpose
gen_ai.provider.name	LLM provider (openai, anthropic, bedrock). The legacy gen_ai.system is also supported.
gen_ai.request.model	Model requested, matched against Harness pricing data.
gen_ai.usage.input_tokens	Input tokens (priced).
gen_ai.usage.output_tokens	Output tokens (priced).

A span missing any of these cannot be priced. If you also want cost associated with a specific agent, set gen_ai.agent.name.

---

Full Attribute Reference

Each LLM span is expected to carry the following attributes. The four pricing-critical fields are marked in Minimum attributes for pricing; the rest improve pricing accuracy or enable grouping by service, session, tenant, and user.

Attribute	Purpose
service.name	Application or service that made the call.
service.namespace	Logical grouping (domain, product area).
deployment.environment.name	Environment (production, staging, dev).
gen_ai.operation.name	Operation type (chat, embeddings, tool).
gen_ai.provider.name	LLM provider (openai, anthropic, bedrock).
gen_ai.request.model	Model requested.
gen_ai.response.model	Model that actually served the response.
gen_ai.response.id	Provider response identifier.
gen_ai.agent.name	Agent name for cost grouping. Set this to attribute cost to a specific agent in Cost Explorer.
gen_ai.conversation.id	Session or conversation grouping.
gen_ai.request.max_tokens	Requested token cap.
gen_ai.request.temperature	Sampling temperature.
gen_ai.response.finish_reasons	Why generation stopped.
gen_ai.usage.input_tokens	Input tokens (priced).
gen_ai.usage.cache_read.input_tokens	Cached input tokens read (priced at cache-read rate).
gen_ai.usage.cache_creation.input_tokens	Input tokens written to cache (priced at cache-write rate).
gen_ai.usage.output_tokens	Output tokens (priced).
gen_ai.usage.reasoning.output_tokens	Reasoning tokens (priced at reasoning rate).
gen_ai.input.messages	Raw prompt text.
gen_ai.output.messages	Raw response text.
tenant.id	Customer or tenant attribution.
user.id	End-user attribution.

---

How Attributes Map to Cost and Grouping

The attributes fall into three roles:

• Pricing inputs: gen_ai.provider.name, gen_ai.request.model, and the gen_ai.usage.* token counts. CACM prices a span as tokens times model price using these fields.

• Accuracy refinements: the cache and reasoning token counts (gen_ai.usage.cache_read.input_tokens, gen_ai.usage.cache_creation.input_tokens, gen_ai.usage.reasoning.output_tokens) allow CACM to price at the correct per-token rate rather than the standard input/output rate.

• Grouping dimensions: service.name, deployment.environment.name, gen_ai.conversation.id, gen_ai.agent.name, tenant.id, and user.id allow you to group and filter cost by service, environment, session, agent, tenant, and user in Cost Explorer.

RAW PROMPT AND RESPONSE TEXT IS OPTIONAL

gen_ai.input.messages and gen_ai.output.messages carry the raw prompt and completion. They are useful for debugging but inflate span size. Disable payload capture if spans contain sensitive data or grow too large. Go to Reduce Trace Data Volume to reduce trace data volume.

---

Next Steps

• Go to Manual Instrumentation to emit these attributes from your application.

• Go to Supported Providers and Frameworks to check which SDKs and frameworks emit these attributes natively.

• Go to How AI Traces Work to understand how spans become cost.

• Go to AI Cost Troubleshooting if traces appear without cost.

• Go to the AI Cost Management FAQ to review common questions.