Tecton

Fresh data,
fast decisions.

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

Trusted by top engineering teams

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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.

Diagram of Tecton’s platform: on the left, arrows from four data sources—Real Time (Data Push, External API), Streaming (Kafka, Kinesis), Batch (Snowflake, Redshift, BigQuery, AWS Glue), and Unstructured (images, audio, logs)—feed into a central Tecton box. Inside the box are top-level layers for AI-assisted feature engineering, Tecton CLI, SDK, API, and Workspace. Below that, a Unified Compute layer (Compute Orchestration and Aggregation Engine) powers four processing modes—Streaming Ingestion, Batch Transform, Realtime Feature Computation, and Model-Generated Embeddings—and a Unified Storage layer (Online Serving and Offline Retrieval). On the right, arrows lead to three consumer categories: Training & Inference, Rules & Experimentation, and Generative & Search tools.
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.

A data pipeline diagram showing batch and streaming data sources (Snowflake, Kafka, S3) transformed into features like user click counts and ad embeddings, stored in online and offline stores, and served via feature services such as recommender and fraud detection for machine learning models like PyTorch and TensorFlow.
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Built for Real-Time ML

Transform raw data into ML-ready features with sub-second freshness and serve them at sub-10ms latency.

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Fast Iteration, Safe Deployment

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

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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
@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
				
			
Measurable Impact

Metrics that matter to your business.

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.

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Flexible & Unified Compute

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

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Online/Offline Consistency

Feature correctness guaranteed, for data processing delays and materialization windows

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Ultra-low Latency Serving

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

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Streaming Aggregation Engine

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

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Automated Streaming Backfills

Backfills generated from streaming feature code—no separate pipelines required

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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-100 ms P99 serving latency & 99.99 % uptime at 100 k+ QPS

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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

Feature store flow diagram

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

A diagram showing various feature stores

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.

Production Ready

The trusted choice for real-time ML applications.

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Short Time to Production

Declarative Python framework and
infrastructure as code to rapidly deploy data pipelines

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Incorporating Fresh Signals

Native streaming and real-time features incorporate the right signals and improve fraud and risk model quality

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Online/Offline Consistency

Eliminating train-serve skew to ensure the accuracy of fraud and risk predictions

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Seamless CI/CD Integration

Easy integration into your DevOps workflows

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Meeting Latency Requirements at High-Scale and Availability

Reliable and efficient feature access at massive scale and low latency

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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

Book a Demo

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Interested in trying Tecton? Leave us your information below and we’ll be in touch.​

Contact Sales

Interested in trying Tecton? Leave us your information below and we’ll be in touch.​

Unfortunately, Tecton does not currently support these clouds. We’ll make sure to let you know when this changes!

However, we are currently looking to interview members of the machine learning community to learn more about current trends.

If you’d like to participate, please book a 30-min slot with us here and we’ll send you a $50 amazon gift card in appreciation for your time after the interview.

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or

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Request a free trial

Interested in trying Tecton? Leave us your information below and we’ll be in touch.​

Unfortunately, Tecton does not currently support these clouds. We’ll make sure to let you know when this changes!

However, we are currently looking to interview members of the machine learning community to learn more about current trends.

If you’d like to participate, please book a 30-min slot with us here and we’ll send you a $50 amazon gift card in appreciation for your time after the interview.

CTA link

or

CTA button