Amazon Bedrock

autotel-genai records a canonical gen_ai.* span for every AI SDK call against Amazon Bedrock. autotel-bedrock adds the Bedrock facts on top: the foundation-model id behind an inference profile or ARN, Bedrock’s own stop reason and guardrail outcome, and prices for the models that only exist behind Bedrock.

Installation

npm install autotel autotel-genai autotel-bedrock

Setup

import { init } from 'autotel';
import { registerTelemetry } from 'ai';
import { autotelTelemetry } from 'autotel-genai/observer';
import {
  bedrockCompatibility,
  bedrockProviderAttributes,
  BEDROCK_PRICING,
} from 'autotel-bedrock';

init({
  service: 'payments-bot',
  spanEnrichers: [bedrockCompatibility({ region: process.env.AWS_REGION })],
});

registerTelemetry(
  autotelTelemetry({
    pricing: BEDROCK_PRICING,
    providerAttributes: bedrockProviderAttributes,
  }),
);

Register the enricher under spanEnrichers. spanProcessors replaces the pipeline autotel builds, including the destinations you configured.

One model, three spellings

Bedrock accepts the same model as a foundation-model id, a cross-region inference profile, or an inference-profile ARN:

anthropic.claude-sonnet-4-5-20250929-v1:0
eu.anthropic.claude-sonnet-4-5-20250929-v1:0
arn:aws:bedrock:eu-west-1:123456789012:inference-profile/eu.anthropic.claude-sonnet-4-5-20250929-v1:0

Dashboards group by gen_ai.request.model and Datadog or Langfuse price by it, so a profile or ARN splits one model into several rows and drops the cost. The enricher rewrites gen_ai.request.model and gen_ai.response.model to the foundation id and keeps the rest as their own attributes:

Attribute	Value
gen_ai.request.model	anthropic.claude-sonnet-4-5-20250929-v1:0
aws.bedrock.model.id_raw	the id as the request spelled it
aws.bedrock.model.arn	the ARN, when one was used
aws.bedrock.inference_profile.region	eu, us, global…
aws.bedrock.model.vendor	anthropic, amazon, meta, zai…
cloud.provider / cloud.region	aws / the option or AWS_REGION

Pass normalizeModel: false to keep the requested spelling and add the extra attributes only.

Stop reason and guardrails

The AI SDK folds Bedrock’s stop reasons into its own (guardrail_intervened and content_filtered both become content-filter; malformed_tool_use and model_context_window_exceeded become other), and its lifecycle events omit the guardrail trace. bedrockProviderAttributes reads the model call’s result, or its stream’s finish part, and records:

Attribute	Value
aws.bedrock.stop_reason	end_turn, tool_use, max_tokens, guardrail_intervened…
aws.bedrock.guardrail.intervened	true when the guardrail stopped generation
aws.bedrock.guardrail.id	from the trace, so set guardrailConfig.trace: 'enabled'

gen_ai.response.finish_reasons keeps the AI SDK’s unified value, so a dashboard built on it keeps working. An intervention is a policy outcome: the span’s status stays unset, and guardrail.intervened / total is one query.

Bedrock-only pricing

BEDROCK_PRICING is a table for autotelTelemetry({ pricing }) covering Nova, Llama, Mistral, DeepSeek and GLM. Bedrock bills Anthropic models at Anthropic’s rates, and autotel-genai prices those by family. Keys resolve the way autotel-genai/cost does (exact, then prefix, tried again after each region and vendor prefix is peeled), so eu.amazon.nova-pro-v1:0 resolves via amazon.nova-pro.

The prices are list prices at the time of writing. Spread and override what you use:

autotelTelemetry({
  pricing: {
    ...BEDROCK_PRICING,
    'zai.glm-4.7-flash': { inputPer1M: 0.5, outputPer1M: 2 },
  },
});

Runnable example: apps/example-bedrock in the autotel repository, a Lambda handler around an AI SDK tool-loop agent, run in-process against Bedrock.

Already covered elsewhere

Signal	Comes from
gen_ai.provider.name = aws.bedrock	autotel-genai provider normalisation
AWS request id	gen_ai.response.id
Anthropic model cost on Bedrock	autotel-genai/cost family pricing
Lambda spans, cold starts, X-Ray context	autotel-aws/lambda
Datadog / Langfuse ingestion	autotel-backends, autotel-langfuse