DataDog

LiteLLM Supports logging to the following Datdog Integrations:

• datadog Datadog Logs
• datadog_llm_observability Datadog LLM Observability
• ddtrace-run Datadog Tracing

Datadog Logs

Feature	Details
What is logged	StandardLoggingPayload
Events	Success + Failure
Product Link	Datadog Logs

We will use the --config to set litellm.callbacks = ["datadog"] this will log all successful LLM calls to DataDog

Step 1: Create a config.yaml file and set litellm_settings: success_callback

model_list:
 - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
litellm_settings:
  callbacks: ["datadog"] # logs llm success + failure logs on datadog
  service_callback: ["datadog"] # logs redis, postgres failures on datadog

Datadog LLM Observability

Overview

Feature	Details
What is logged	StandardLoggingPayload
Events	Success + Failure
Product Link	Datadog LLM Observability

model_list:
 - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
litellm_settings:
  callbacks: ["datadog_llm_observability"] # logs llm success logs on datadog

Step 2: Set Required env variables for datadog

Direct API

Send logs directly to Datadog API:

DD_API_KEY="5f2d0f310***********" # your datadog API Key
DD_SITE="us5.datadoghq.com"       # your datadog base url
DD_SOURCE="litellm_dev"       # [OPTIONAL] your datadog source. use to differentiate dev vs. prod deployments

Via DataDog Agent

Send logs through a local DataDog agent (useful for containerized environments):

DD_AGENT_HOST="localhost"         # hostname or IP of DataDog agent
DD_AGENT_PORT="10518"             # [OPTIONAL] port of DataDog agent (default: 10518)
DD_API_KEY="5f2d0f310***********" # [OPTIONAL] your datadog API Key (agent handles auth)
DD_SOURCE="litellm_dev"           # [OPTIONAL] your datadog source

When DD_AGENT_HOST is set, logs are sent to the agent instead of directly to DataDog API. This is useful for:

• Centralized log shipping in containerized environments
• Reducing direct API calls from multiple services
• Leveraging agent-side processing and filtering

Step 3: Start the proxy, make a test request

Start proxy

litellm --config config.yaml --debug

Test Request

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ],
    "metadata": {
        "your-custom-metadata": "custom-field",
    }
}'

Expected output on Datadog

Redacting Messages and Responses

This section covers how to redact sensitive data from messages and responses in the logged payload on Datadog LLM Observability.

When redaction is enabled, the actual message content and response text will be excluded from Datadog logs while preserving metadata like token counts, latency, and model information.

Step 1: Configure redaction in your config.yaml

config.yaml

model_list:
 - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
litellm_settings:
  callbacks: ["datadog_llm_observability"] # logs llm success logs on datadog

  # Params to apply only for "datadog_llm_observability" callback
  datadog_llm_observability_params:
    turn_off_message_logging: true # redacts input messages and output responses

Step 2: Send a chat completion request

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ]
}'

Step 3: Verify redaction in Datadog LLM Observability

On the Datadog LLM Observability page, you should see that both input messages and output responses are redacted, while metadata (token counts, timing, model info) remains visible.

Datadog Tracing

Use ddtrace-run to enable Datadog Tracing on litellm proxy

DD Tracer Pass USE_DDTRACE=true to the docker run command. When USE_DDTRACE=true, the proxy will run ddtrace-run litellm as the ENTRYPOINT instead of just litellm

DD Profiler

Pass USE_DDPROFILER=true to the docker run command. When USE_DDPROFILER=true, the proxy will activate the Datadog Profiler. This is useful for debugging CPU% and memory usage.

We don't recommend using USE_DDPROFILER in production. It is only recommended for debugging CPU% and memory usage.

docker run \
    -v $(pwd)/litellm_config.yaml:/app/config.yaml \
    -e USE_DDTRACE=true \
    -e USE_DDPROFILER=true \
    -p 4000:4000 \
    ghcr.io/berriai/litellm:main-latest \
    --config /app/config.yaml --detailed_debug

Set DD variables (DD_SERVICE etc)

LiteLLM supports customizing the following Datadog environment variables

Environment Variable	Description	Default Value	Required
DD_API_KEY	Your Datadog API key for authentication (required for direct API, optional for agent)	None	Conditional*
DD_SITE	Your Datadog site (e.g., "us5.datadoghq.com") (required for direct API)	None	Conditional*
DD_AGENT_HOST	Hostname or IP of DataDog agent (e.g., "localhost"). When set, logs are sent to agent instead of direct API	None	❌ No
DD_AGENT_PORT	Port of DataDog agent for log intake	"10518"	❌ No
DD_ENV	Environment tag for your logs (e.g., "production", "staging")	"unknown"	❌ No
DD_SERVICE	Service name for your logs	"litellm-server"	❌ No
DD_SOURCE	Source name for your logs	"litellm"	❌ No
DD_VERSION	Version tag for your logs	"unknown"	❌ No
HOSTNAME	Hostname tag for your logs	""	❌ No
POD_NAME	Pod name tag (useful for Kubernetes deployments)	"unknown"	❌ No

* Required when using Direct API (default): DD_API_KEY and DD_SITE are required
* Optional when using DataDog Agent: Set DD_AGENT_HOST to use agent mode; DD_API_KEY and DD_SITE are not required