1. Real-Time Metrics for LLMs Performance with Datadog


    To monitor real-time metrics for large language models (LLMs) performance using Datadog, you would need to set up a Datadog account, install the Datadog agent on your servers hosting the LLMs, and then create custom metrics and monitors via the Datadog API.

    In the context of a Pulumi program, you can automate the creation of these resources for monitoring by using the Datadog provider. The key resources you would likely interact with are MetricMetadata, which defines the structure of your custom metrics, and Monitor, which defines the conditions under which you want to be alerted.

    Below is a Pulumi Python program that demonstrates how to set up custom metric metadata and a monitor in Datadog for tracking the performance of an LLM.

    import pulumi import pulumi_datadog as datadog # Create a new Datadog MetricMetadata resource to define the structure of your custom LLM performance metric. llm_metric_metadata = datadog.MetricMetadata("llmMetricMetadata", # The name of your custom metric, prefixed as per Datadog's convention. # This should match the metric name you're sending from your LLM servers. metric="llm.response_time", # The type of the metric; gauge, rate, count, etc. type="gauge", # A short description of the metric. description="The response time of the large language model", # The unit of measurement for the metric; byte, second, request, etc. unit="second", # Per_unit specifies the measurement unit for the resulting data (x per_unit per unit). per_unit="request", # Optionally, add a short name to appear in widgets. short_name="LLM Response" ) # Create a new Datadog Monitor resource to alert you when you need to pay attention to the LLM performance. llm_performance_monitor = datadog.Monitor("llmPerformanceMonitor", # The name of the monitor. name="LLM Performance Alert", # The type of the monitor; choose from metric alert, service check, etc. type="metric alert", # The query string to define the monitor's operation; # this example triggers an alert when the 5-min avg response time is > 0.3 seconds. query="avg(last_5m):avg:llm.response_time{environment:production} > 0.3", # The message sent when an alert is triggered; supports notification variables. message="Notify DevOps team: LLM response time is above the threshold.", # Tags to associate with your monitor. tags=["environment:production", "team:ai"], # Set the evaluation window. evaluation_delay=60, # Additional options for no data, delays, and thresholds can be set accordingly. ) # Export the IDs of the MetricMetadata and Monitor to access them later if needed. pulumi.export("metric_metadata_id", llm_metric_metadata.id) pulumi.export("llm_monitor_id", llm_performance_monitor.id)

    This program creates custom metric metadata for monitoring LLM response times and a monitor that alerts if the average response time goes above a specific threshold. It uses two Datadog resources:

    1. MetricMetadata: Defines the structure and description of the custom metric you will ingest into Datadog for your LLM's performance data.
    2. Monitor: Sets up an alerting rule that will trigger when the defined conditions are met based on the metric query provided.

    Remember to replace the "environment:production" tag in the monitor query with the correct environment tag suitable for your setup.

    This Pulumi program assumes that you are already sending the custom metric "llm.response_time" from your LLM servers with the appropriate tags and intervals to Datadog using one of Datadog's SDKs or the StatsD protocol. The monitor will use this metric to evaluate the alerting condition.

    Upon running this Pulumi program, you will create these resources in your Datadog account, and Pulumi will manage the state of these resources for you. If you need to adjust thresholds or other parameters, you will update the Pulumi program, and Pulumi will apply the changes to the Datadog settings accordingly.