Hello @d_szepietowska , I did some research on my end and found a few helpful hints/tips to help you troubleshoot. 

Let’s walk through what should be happening, and then I’ll call out the most common reasons the feature lookup DataFrame doesn’t show up in the inference table, even when tracing looks like it’s enabled.

What should happen

When inference tables are enabled on the endpoint and you set the environment variable ENABLE_FEATURE_TRACING=true, the automatic feature lookup DataFrame should be logged to the endpoint’s inference table. This does require MLflow 2.14.0 or newer on the serving side.

For endpoints created starting in February 2025, the platform can also log the augmented DataFrame (that is, the looked-up features plus function return values) into the inference table when configured this way.

Common reasons it doesn’t show up

One of the most frequent causes is endpoint age. If the endpoint was created before the feature shipped (pre-February 2025), it won’t pick up augmented DataFrame logging. In that case, recreating the endpoint (or creating a new one) is required.

Another surprisingly common issue is casing. The value must be the lowercase string “true”, not “True” and not a boolean. Several folks have hit this exact issue, and switching to lowercase “true” immediately fixed it.

Placement matters as well. The environment variable has to be set on the served entity itself (served_entities[n].environment_vars). It won’t work if it’s applied only at a higher or different level of the endpoint config, so it’s worth double-checking it’s on the correct served entity

There’s also some natural delay to account for. Inference table updates are best-effort and can take up to about an hour to appear. This is true for AI Gateway–enabled inference tables as well. If you checked right after scoring, the augmented DataFrame may simply not have landed yet.

Payload size limits can come into play too. Inference table logging has a 1 MiB cap for request, response, and traces. If the augmented DataFrame is large, it may be dropped or truncated, in which case it will show up as null and a logging error code will be set.

If you’re using legacy online tables, that can also block this. Databricks “online tables (legacy)” are deprecated. The current recommendation is Databricks Online Feature Store, which you’ll want to use instead of the legacy path.

Client and logging requirements are another checkpoint. The model needs to have been logged with FeatureEngineeringClient.log_model (or FeatureStoreClient.log_model for legacy setups), and the feature-engineering client version must be 0.3.5 or newer.

Finally, a quick note on ENABLE_MLFLOW_TRACING. That flag controls MLflow trace logging (primarily for GenAI and agent workflows) to MLflow experiments and/or inference tables. It does not, by itself, enable feature lookup DataFrame logging. That behavior is specifically controlled by ENABLE_FEATURE_TRACING. In short: ENABLE_MLFLOW_TRACING is optional and additive, not required for feature lookup logging.

Quick checks and suggested fixes

First, confirm the environment variable is applied exactly as ENABLE_FEATURE_TRACING=“true” (lowercase string) on the served entity, then redeploy the endpoint configuration.

If the endpoint predates February 2025, create a new endpoint (or fully recreate the existing one) with the same model and set ENABLE_FEATURE_TRACING=“true”, then test again.

Verify that inference tables are enabled on the endpoint and keep the log delivery window in mind (up to about an hour). If you have the option, AI Gateway–enabled inference tables are generally the better default going forward.

Lastly, make sure the model was logged with fe.log_model, that your feature-engineering client is version 0.3.5 or newer, and that your features are published to Databricks Online Feature Store rather than legacy online tables.

Here’s the SDK pattern that ensures the environment variable is placed correctly on the served entity:

from databricks.sdk import WorkspaceClient
from databricks.sdk.service.serving import ServedEntityInput, EndpointCoreConfigInput

w = WorkspaceClient()
w.serving_endpoints.create_or_update(
    name="your-endpoint",
    config=EndpointCoreConfigInput(
        served_entities=[
            ServedEntityInput(
                name="your-model",
                entity_name="your.catalog.schema.model_name",
                entity_version="1",
                workload_size="Small",
                scale_to_zero_enabled=True,
                environment_vars={
                    "ENABLE_FEATURE_TRACING": "true"
                }
            )
        ]
    )
)

Hope this helps, Louis.