Tecton & Cloud Alternatives

While Feast is excellent for getting started, managed feature stores offer enterprise features, better scalability, and reduced operational burden.

Managed Feature Store Landscape

┌─────────────────────────────────────────────────────────────────┐
│                    Feature Store Options                         │
├─────────────────┬─────────────────┬─────────────────────────────┤
│   Open Source   │    Managed      │      Cloud-Native           │
├─────────────────┼─────────────────┼─────────────────────────────┤
│   Feast         │   Tecton        │   AWS SageMaker FS          │
│                 │   Databricks FS │   GCP Vertex AI FS          │
│                 │   Hopsworks     │   Azure Synapse             │
└─────────────────┴─────────────────┴─────────────────────────────┘

Tecton

Tecton is a fully-managed feature platform built by the team that created Uber's Michelangelo.

Key Features

Feature	Description
Real-time features	Sub-100ms latency
Feature monitoring	Drift detection built-in
Versioning	Automatic feature versioning
RBAC	Fine-grained access control
Notebooks	Interactive development

When to Choose Tecton

Situation	Recommendation
Real-time ML at scale	Tecton
Strict SLA requirements	Tecton
Enterprise governance needs	Tecton
Learning/prototyping	Feast
Budget constraints	Feast

Cloud-Native Options

AWS SageMaker Feature Store

# AWS SageMaker Feature Store example
import boto3
from sagemaker.feature_store.feature_group import FeatureGroup

# Create feature group
feature_group = FeatureGroup(
    name="customer-features",
    sagemaker_session=sagemaker_session
)

# Define features
feature_definitions = [
    {"FeatureName": "customer_id", "FeatureType": "String"},
    {"FeatureName": "total_spend", "FeatureType": "Fractional"},
    {"FeatureName": "order_count", "FeatureType": "Integral"},
]

# Create feature group
feature_group.create(
    s3_uri=f"s3://{bucket}/feature-store",
    record_identifier_name="customer_id",
    event_time_feature_name="event_time",
    feature_definitions=feature_definitions,
    enable_online_store=True
)

Best for: Teams already on AWS with existing SageMaker workflows.

GCP Vertex AI Feature Store

# GCP Vertex AI Feature Store example
from google.cloud import aiplatform

# Initialize
aiplatform.init(project="my-project", location="us-central1")

# Create feature store
feature_store = aiplatform.Featurestore.create(
    featurestore_id="my_featurestore",
    online_store_fixed_node_count=1
)

# Create entity type
customer_entity = feature_store.create_entity_type(
    entity_type_id="customer",
    description="Customer entity"
)

# Create features
customer_entity.batch_create_features(
    feature_configs={
        "total_spend": {"value_type": "DOUBLE"},
        "order_count": {"value_type": "INT64"},
    }
)

Best for: Teams on GCP with Vertex AI pipelines.

Databricks Feature Store

# Databricks Feature Store example
from databricks.feature_store import FeatureStoreClient

fs = FeatureStoreClient()

# Create feature table
fs.create_table(
    name="customer_features",
    primary_keys=["customer_id"],
    df=feature_df,
    description="Customer aggregated features"
)

# Read features for training
training_set = fs.create_training_set(
    df=labels_df,
    feature_lookups=[
        FeatureLookup(
            table_name="customer_features",
            feature_names=["total_spend", "order_count"],
            lookup_key="customer_id"
        )
    ],
    label="churn"
)

Best for: Teams using Databricks for data engineering and ML.

Comparison Matrix

Feature	Feast	Tecton	SageMaker	Vertex AI	Databricks
Open source	Yes	No	No	No	No
Real-time	Basic	Excellent	Good	Good	Good
Streaming	Limited	Native	Limited	Native	Spark
Monitoring	DIY	Built-in	Built-in	Built-in	Built-in
Cost	Infrastructure	Premium	Pay-per-use	Pay-per-use	Pay-per-use
Lock-in	None	Some	High	High	Medium

Cost Considerations

Solution	Typical Monthly Cost
Feast (self-hosted)	$500-2,000 (infrastructure)
Tecton	$5,000-50,000+
SageMaker FS	$1,000-10,000
Vertex AI FS	$1,000-10,000
Databricks FS	Included in Databricks

Decision Framework

Start
  │
  ▼
┌─────────────────────────────────────┐
│ Do you need real-time ML at scale? │
└─────────────────┬───────────────────┘
                  │
         ┌───────┴───────┐
         │               │
        Yes              No
         │               │
         ▼               ▼
┌────────────────┐  ┌────────────────┐
│ Tecton or      │  │ Already on a   │
│ Cloud Provider │  │ cloud platform?│
└────────────────┘  └───────┬────────┘
                            │
                   ┌────────┴────────┐
                   │                 │
                  Yes               No
                   │                 │
                   ▼                 ▼
          ┌────────────────┐  ┌────────────────┐
          │ Use cloud-     │  │ Start with     │
          │ native option  │  │ Feast          │
          └────────────────┘  └────────────────┘

Migration Path

Phase 1: Feast (Local)
    │
    │  Prove value, define features
    │
    ▼
Phase 2: Feast (Cloud Storage)
    │
    │  Scale with S3/GCS backends
    │
    ▼
Phase 3: Managed Solution
    │
    │  Move to Tecton/Cloud-native
    │  when scale or SLA requires
    ▼
Phase 4: Enterprise Platform

Best Practices

Practice	Why
Start simple	Don't over-engineer early
Define abstractions	Make migration easier
Monitor costs	Feature stores can be expensive
Evaluate regularly	Needs change over time

Key insight: Start with Feast to learn feature store concepts and prove value. Migrate to managed solutions when real-time requirements, scale, or enterprise features justify the cost.

Next module: We'll explore model registry and serving with MLflow and BentoML. :::