Fresh data,
fast decisions.

The feature store for real-time machine learning at scale.

TRUSTED BY TOP ENGINEERING TEAMS

FEATURE STORE

The feature store for ML engineers,
built by the creators of Uber’s Michelangelo.

Turn your raw data into production-ready features for business-critical use cases like fraud detection, risk scoring, and personalization. Tecton powers real-time decisions at scale—no pipeline rewrites required.

Automated Pipelines

Never write another data pipeline by hand.

Provision your ML data pipelines using a standardized infrastructure-as-code description. Tecton automatically builds, updates, and manages the infrastructure, so you don’t have to.

Built for Real-Time ML

Transform raw data into ML-ready features with sub-second freshness and serve them at sub-100ms latency.

Fast Iteration, Safe Deployment

Accelerate feature development with consistency from training to serving—no rewrites, no skew.

Reliable at Enterprise Scale

Proven at 100K+ QPS with 99.99% uptime for real-time ML use cases.

Use Cases

For ML engineers with real-time use cases.

# Fraud Detection

Stop fraud in milliseconds with real-time behavioral signals.

# Risk Decisioning

Make instant decisions with streaming features and up-to-date applicant data.

# Credit Scoring

Deliver accurate, real-time credit decisions with fresh behavioral and historical data.

# Personalization

Tailor every product experience instantly and dynamically in real time with contextual data.

				
					# Define
@stream_feature_view(
   source=transactions,
   entities=[user],
   online=True,
   offline=True,
   features=[Aggregate(“amount”,“mean”,td(mins=30)])
def stream_features(transactions):
   return df[[“user_id”, “timestamp”, “amount”]]

# Train
training_data = stream_features.get_features_for
_events(training_events)

				
					$ tecton workspace select prod
$ tecton apply┃

99%

Faster: Time-to-production cut from 3 months to 1 day for new features.

Growth in production ML use cases over 12 months

$20M+

Saved annually through fraud detection improvements

Key Innovations

What makes Tecton different.

Define your features once in code—then get automatic streaming backfills, flexible compute across Python, Spark, and SQL, and guaranteed training–serving consistency so your models always behave as expected.

Flexible & Unified Compute

Mix-and-match Python (Ray & Arrow), Spark, and SQL compute for simplicity and performance

Online/Offline Consistency

Feature correctness guaranteed, for data processing delays and materialization windows

Ultra-low Latency Serving

Sub-10ms latency with support for DynamoDB and Redis, built-in caching, autoscaling, and SLA-driven design

Streaming Aggregation Engine

Immediate freshness, ultra-low latency at high scale, supporting multi-year windows and millions of events

Automated Streaming Backfills

Backfills generated from streaming feature code—no separate pipelines required

Dev-Ready Declarative Framework

Pipelines deployed via code, with native support for CI/CD, version control, unit testing, lineage, and monitoring

High Performance

Proven performance and reliability at enterprise scale.

Sub-100 ms p99 latency and 99.99 % uptime keep your features fresh and your services available. Auto-scaling and smart routing between Redis and DynamoDB deliver peak performance without any manual tuning.

Always fast, always on

Sub-100ms p99 serving latency and 99.99% uptime, even at 100k+ QPS
Sub-second freshness with lifetime and time window aggregations on streaming data
Handles traffic spikes with auto-scaling and zero manual intervention

Built for scale

Tecton powers fraud, risk, and personalization models at Fortune 100 companies, making billions of decisions daily
Global deployments with disaster recovery, failover, and point-in-time restore