Tecton went to Databricks. Signals stayed neutral.
Tecton is a mature feature platform for ML teams who already have trusted data infrastructure. Signals starts a layer earlier and stays neutral about where your warehouse lives.
Same serving promise, a different starting line and a different center of gravity.
| Dimension | Signals | Tecton |
|---|---|---|
| What you bring | An SDK in your app. Behavioral events are collected and schema-validated on the way in. | Existing data sources, modeled and trusted. Collection is out of scope. |
| Where your data lives | Warehouse-neutral by design. Snowflake, BigQuery and Databricks are equal citizens, and streaming-only deployments are supported. | Part of Databricks. Neutrality toward competing warehouses is a question worth asking directly. |
| Primary consumer | Application and product surfaces first, models second. A storefront or agent reads it in a render path. | Models and training pipelines first. Built around the ML lifecycle. |
| Entity keys | Any entity: user, session, account, basket, listing, store. Identity resolution optional. | Supported, but the entity has to exist in your modeled sources first. |
| Attribute update latency | <1s from the event to the attribute being current, handled by the engine. | Depends on the pipeline and transformation mode you configure behind it. |
| Serving latency | 6ms p50, 10ms p95 in-region. One call returns a whole service. | Comparable online-serving latency once the online store is provisioned and warm. |
| Who defines a feature | A product manager in the console, or an engineer in code through CI/CD. No platform ticket per attribute. | Data scientists and ML engineers in Python, inside the feature platform's own framework. |
| Before the first value | Add the SDK, define one attribute, read it from your product. Collection is part of the product. | The sources have to be modeled and trusted first. That work sits upstream of the platform. |
Based on published vendor documentation at the time of writing · confirm the current position with Tecton or Databricks before quoting it
The behavioral event pipeline does not exist yet, or exists and nobody trusts it.
The consumer is an application surface or an agent, not only a model server.
You want to stay neutral about which warehouse you are on, or you are not on Databricks.
You need to key on things that are not users: baskets, listings, stores, sessions.
The team that wants the attribute is product, and the queue is data engineering.
Your data sources are already modeled, trusted and stable.
Databricks is where your data platform already lives and you want deeper integration with it.
Your primary consumers are training pipelines and production model servers.
Feature lineage and governance inside the ML lifecycle is the thing being bought.
Most of your model inputs come from warehouse tables rather than live behavior.
Signals can be the behavioral source Tecton was assuming you had.
Signals upstream, Tecton downstream
Collect and validate behavior with Signals, land the stream, and let the feature platform keep serving your models.
Signals for the application path only
Models keep reading Tecton. The storefront, the service desk and the agent read Signals at request time.
Signals instead, for now
Teams without an ML platform team start on Signals and add a feature platform later if the model estate grows to need one.
p50 in-region read, one call per service
Event in to attribute current, no job you run
Shared by live serving and the training set
user · session · basket · listing · store
The questions your architect will ask.
Is Signals a feature store?
It overlaps on serving and on definition parity. It differs in bringing collection and validation with it, and in being aimed at application engineers as much as ML teams.
We are already on Databricks. Does that settle it?
It makes the integration argument easier for Tecton, and it changes nothing about collection. If the behavioral events still do not exist, that project is ahead of you either way.
Can we keep Tecton and add Signals?
Yes, and it is the common shape. Signals supplies governed behavioral attributes in the session; the feature platform keeps doing the model work it was bought for.
What about point-in-time correctness?
The dataset builder computes each attribute from only the events before the moment you are predicting from, with the same definitions the live surface reads. Nothing that happened afterwards leaks into training.
Test the latency claim on your own events.
Define one attribute, read it from your product, and compare the numbers to whatever you run today.