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Amazon Bedrock model invocations (the Converse, ConverseStream, InvokeModel, and InvokeModelWithResponseStream APIs) do not emit telemetry to Bronto on their own. You collect Bedrock telemetry by instrumenting the application that calls Bedrock with OpenTelemetry and forwarding it to Bronto through an OTel Collector. This is different from log-only AWS services: there is no CloudWatch log group or Firehose stream to point at Bronto. The rich GenAI signal — model id, token usage, latency, and prompt/response content — is produced by instrumenting your code.

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Two requirements

1

Instrument your application

Add the OpenTelemetry SDK and the AWS SDK auto-instrumentation to the service that calls Bedrock. The botocore/AWS SDK instrumentation traces Converse / InvokeModel calls and, on a recent enough version, enriches them with GenAI semantic-convention attributes (token usage, model, finish reason). See Amazon Bedrock LLM observability with OpenTelemetry for the full setup.
2

Run an OTel Collector to forward to Bronto

The instrumented app exports OTLP to a Collector, which adds the Bronto credentials and forwards each signal to Bronto. On AWS the recommended Collector is ADOT, run as a sidecar (ECS / Fargate) or daemonset (EKS).
ADOT — the AWS Distro for OpenTelemetry Collector — receives OTLP from your application and forwards logs, metrics and traces to Bronto over OTLP/HTTP, with consistent service.name / service.namespace routing (see Data Organization). The same sidecar pattern works on the ECS / Fargate and EKS launch types you are likely already running your Bedrock app on. Pair it with application instrumentation for your language:

OpenTelemetry overview

How application instrumentation maps to Bronto datasets and collections.

Python

Most common runtime for Bedrock apps; richest GenAI auto-instrumentation.

Node.js

Instrument a Node.js service that calls the Bedrock APIs.

Java / Go / .NET

All OTLP-capable runtimes export to the same Collector.

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LLM observability for Bedrock

For the GenAI-specific signal — capturing token usage, model metadata, and prompt/response content following the OpenTelemetry GenAI semantic conventions — see Amazon Bedrock LLM observability under AI assisted observability.

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Alternatives

  • Self-managed OTel Collector — when you need custom processors, sampling, or fan-out to multiple backends alongside Bronto.
  • Direct export — for local development or short-lived jobs you can export OTLP straight from the SDK to Bronto with an API key; see the direct export section on any language page.
See Ingesting AWS Data into Bronto for the full service-to-method mapping.

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References