Fresh data,
fast decisions.
The feature store for real-time machine learning at scale.
Trusted by top engineering teams
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.
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-10ms 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.
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
@batch_feature_view(
sources=[transactions_batch],
entities=[merchant],
mode='pandas',
online=True,
offline=True,
aggregation_interval=timedelta(days=1),
features=[
Aggregate(input_column=Field('is_fraud',
Int32), function='mean',
time_window=timedelta(days=1)),
Aggregate(input_column=Field('is_fraud',
Int32), function='mean',
time_window=timedelta(days=30)),
Aggregate(input_column=Field('is_fraud',
Int32), function='mean',
time_window=timedelta(days=90)),
],
feature_start_time=datetime(2022, 5, 1),
description='The merchant fraud rate over series
of time windows, updated daily.',
timestamp_field='timestamp'
)
def merchant_fraud_rate(transactions_batch):
return transactions_batch[['merchant',
'is_fraud', 'timestamp']]
$ tecton workspace select prod
$ tecton apply
Metrics that matter to your business.
Time-to-production reduced from 3 months to just 1 day
Growth in live machine learning use cases in one year
Annual savings through improved fraud prevention
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
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-100 ms P99 serving latency & 99.99 % uptime at 100 k+ QPS
Tecton delivers sub-second feature freshness, even for lifetime and sliding-window aggregations on streaming data, automatically scaling to absorb traffic spikes with zero manual intervention.
Built for scale
Billions of daily ML decisions at Fortune 100 enterprises
Tecton powers fraud, risk, and personalization models worldwide, with built-in disaster recovery, failover, and point-in-time restores to keep you up and running everywhere.
Efficient & Cost-Effective
Tuned to deliver the right latency at the best price
Tecton lets you tailor infrastructure per feature, choosing the best compute and serving for each use case. Whether it’s Redis or DynamoDB, Ray or Spark, you get full flexibility without added complexity.
The trusted choice for real-time ML applications.
Short Time to Production
Declarative Python framework and
infrastructure as code to rapidly deploy data pipelines
Incorporating Fresh Signals
Native streaming and real-time features incorporate the right signals and improve fraud and risk model quality
Online/Offline Consistency
Eliminating train-serve skew to ensure the accuracy of fraud and risk predictions
Seamless CI/CD Integration
Easy integration into your DevOps workflows
Meeting Latency Requirements at High-Scale and Availability
Reliable and efficient feature access at massive scale and low latency
Enterprise-grade Infrastructure
ISO 27001, SOC2 type 2, and PCI, meets security and deployment requirements for FSI
Trusted by top ML, risk, and data teams
“What shines about Tecton is the feature engineering experience—that developer workflow. From the very beginning, when you’re onboarding a new data source and building a feature on Tecton, you’re working with production data, and that makes it really easy to rapidly iterate.”
"When we first started building our own feature workflows, it took months—often three months—to get a feature from prototype into production. These days, with Tecton, it’s quite viable to build a feature within one day. Tecton has been a game changer for both workflow and efficiency."
"With Tecton as part of our stack, we’re really focused on understanding trends, disrupting fraud rings and delivering near-instant decisions—without having to build and maintain our own feature-freshness infrastructure."
"For credit specifically, we leveraged a roughly 50% increase in approval rate while decreasing losses by about 5%. Fraud transaction monitoring was even more extreme—4× the chance of blocking a fraud transaction while blocking 20% fewer legitimate ones."
"Prior to Tecton, our features were generated independently with individual Spark pipelines. They were not built for sharing, they were often not cataloged, and we lacked the ability to serve features for real-time inference."
"In just a week or two of work (instead of months of plumbing), we had real-time features feeding our search-ranking models, letting us A/B-test session-level personalization immediately."
Platforms at HomeToGo
