Continuous operational visibility across your AI environment — cost by model, token consumption, error rates, and usage trends — built directly on the AWS Bedrock invocation data your own account already generates.
The continuous observation of AI systems in production — so problems surface before they affect outcomes or budgets.
AI monitoring is the continuous observation of AI systems in production — tracking cost, usage, errors, performance, and operational behavior over time so that problems are identified and addressed before they affect outcomes or budgets.
For organizations running AI on AWS Bedrock, monitoring covers the full operational picture: which models are running, what they cost, how tokens are being consumed, where errors are occurring, and how all of those signals are changing over time.
Visibility is not the goal. It is the prerequisite.
AI monitoring is where evidence originates. Without it, cost optimization efforts are targeted at symptoms. Model selection decisions lack operational grounding. Prompt improvements are made without confirmation that they hold in production.
Model Optimizer connects monitoring directly to the decisions it is meant to support. Every cost trend, every token issue, and every error pattern is evidence — evidence that informs intelligence, and intelligence that drives better decisions.
AI Monitoring in Model Optimizer is built on AWS Bedrock Model Invocation Logging data delivered to your own S3 bucket.
That data provides the operational record behind every monitoring view — model calls, token counts, cost estimates, regions, accounts, error states, and prompt pattern activity. Model Optimizer turns that invocation data into monitoring views so teams can track the operational health of their AI environment without building custom reporting pipelines.
The operational areas that most directly affect AI cost, reliability, and performance. Together, these monitoring surfaces provide a continuous operational view of your AI environment — from individual model behavior to organization-wide cost and reliability trends.
See which models are driving spend, call volume, input tokens, and output tokens across your environment.
Track operational metrics such as cost, API calls, total tokens, average daily cost, and context window overruns over time so usage spikes and directional changes are easier to identify.
Identify models and prompt patterns approaching or hitting token limits before those issues affect output quality, completion behavior, or cost.
See which prompt patterns consume the most tokens and which are associated with higher failure rates.
Track total errors, error rates, error types, and regional error patterns — including Access Denied, Internal Server Error, Resource Not Found, Service Unavailable, and Validation Error.
Monitor cost, call volume, token usage, and error states across AWS regions and accounts.
AI optimization is not finished when a model, prompt, or cost decision is made.
Workloads evolve. Prompts change. Models improve. Costs shift. Error patterns emerge. Monitoring keeps pace.
AI Monitoring is not a one-time setup. It is the ongoing evidence layer that keeps the rest of the methodology grounded in what is actually happening — before optimization decisions are made, during implementation, and after.
Your AWS Bedrock environment is already generating the operational data. Model Optimizer connects to it, monitors it continuously, and turns it into the evidence behind every model, prompt, and cost decision — starting with a free account. No code changes. Read-only access via IAM role.
Get Started FreeAI monitoring is the continuous observation of AI systems in production — tracking cost, usage, errors, performance, and operational behavior over time. For organizations running AI on AWS Bedrock, it covers which models are running, what they cost, how tokens are being consumed, where errors are occurring, and how those signals change over time.
Model Optimizer monitors cost by model, token usage and token issues, error rates and error types, prompt pattern behavior, usage trends, and activity across AWS regions and accounts — all built on AWS Bedrock Model Invocation Logging data from your own S3 bucket.
Once your S3 data source is connected, Model Optimizer ingests new invocation logs hourly. AWS delivers Bedrock logs to S3 within roughly 5–10 minutes of each call, so new activity typically appears in your dashboards within an hour of setup.
Cost by Model shows which models are driving spend at a point in time — call volume, input tokens, output tokens, and associated cost by model. Trends tracks those metrics over time so directional changes, usage spikes, and cost trajectories are easier to identify and act on.
Yes. Model Optimizer monitors cost, call volume, token usage, and error states across AWS regions and accounts.