Startups > DataStax

DataStax

DataStax helps enterprises build real-time AI applications with distributed cloud databases.

Private Equity $418M total Founded 2010 Santa Clara 722 employees

DataStax provides Astra DB, a cloud database-as-a-service built on Apache Cassandra, along with DataStax Enterprise for on-premises deployments and Astra Streaming for event streaming. It solves the operational complexity of managing distributed databases at scale by automating deployment, scaling, backup, and recovery across multiple cloud regions. The platform enables enterprises to build real-time AI applications with low-latency data access and multi-model support (tabular, search, graph) through a serverless, pay-as-you-go architecture.

Problem solved

Enterprises struggle with operational complexity, downtime, and latency when deploying and managing distributed database clusters at scale across multiple cloud regions.

Target customer

Enterprise software companies and large-scale data-driven organizations requiring distributed, low-latency databases; companies building real-time AI/ML applications with complex data requirements across multiple regions.

Website LinkedIn Crunchbase

Founders

Jonathan Ellis

Founder

Systems architect at Rackspace Hosting; previously Principal Engineer at Berkeley Data Systems and software engineer at multiple startups including Digital Technology International.

Matt Pfeil

Founder

Computer Science graduate from Virginia Tech; joined Rackspace after working at an email hosting startup, where he later co-founded DataStax with Ellis.

Funding history

Seed $Unknown April 2010 Led by Rackspace · Unknown

Series A $2.7M October 2010 Led by Unknown · Unknown

Series B $11M 2011 Led by Crosslink Capital · Unknown

Series C $25M 2012 Led by Unknown · Unknown

Series D $45.3M 2013 Led by Unknown · Unknown

Series E $106M 2014 Led by Unknown · Unknown

Series F $37.6M 2021 Led by Unknown · Unknown

Series G $115M May 2021 Led by Goldman Sachs Investment Partners · Unknown

Total raised: $418M

Industries

Cloud Data Services Database Big Data Enterprise Software Open Source

Pricing

Pay-as-you-go serverless model for Astra DB with no upfront infrastructure costs. DataStax Enterprise typically involves higher setup costs and initial infrastructure requirements. Specific pricing tiers not publicly detailed.

Notable customers

Verizon, Audi, Capital One, ESL Gaming, ACI Worldwide, SkyPoint, Bud Financial

Integrations

Apache Cassandra, Apache Pulsar, Langflow (acquired April 2024), multi-cloud support (AWS, Google Cloud, Azure)

Website

datastax.com/

Competitors

MongoDB

MongoDB dominates the NoSQL market (45.41% share) but focuses primarily on document-based data; DataStax excels at distributed, real-time analytics with multi-model support (tabular, search, graph).

Amazon DynamoDB

DynamoDB is AWS-proprietary and simpler for basic key-value use cases; DataStax offers open-source flexibility, better multi-region support, and advanced analytics capabilities.

Couchbase

Couchbase provides document and search capabilities; DataStax differentiates through Apache Cassandra's superior horizontal scalability, lower-latency distributed reads, and real-time AI support.

Confluent

Confluent specializes in event streaming (Kafka); DataStax covers both streaming (via Astra Streaming/Pulsar) and transactional databases, offering a more complete data platform.

Why this matters: DataStax was acquired by IBM in May 2025, signaling major enterprise investment in open-source distributed databases for AI workloads. The company's $418M funding and 800+ enterprise customers demonstrate strong market validation, while its recent Langflow acquisition shows strategic expansion into AI/ML tooling for real-time data applications.

Best for: Large enterprises and scale-ups building real-time AI applications that require low-latency distributed databases with multi-region failover, high transaction throughput, and operational automation.

Use cases

Fraud Prevention at Scale

ACI Worldwide uses DataStax Enterprise to manage fraud detection platforms that handle massive transaction spikes with zero downtime. The distributed architecture enables real-time pattern detection across billions of transactions without latency penalties.

Real-Time AI for Healthcare

SkyPoint leveraged DataStax to analyze large health datasets in real-time, enabling senior care providers to get instant insights on patient trends. Multi-model support allowed them to combine structured health records with unstructured clinical notes.

Global Telecommunications Data

Verizon uses DataStax to manage customer data across multiple regions with guaranteed low-latency access for network optimization and customer analytics. The multi-region replication ensures data sovereignty while maintaining performance.

Alternatives

MongoDB Atlas More popular and easier to learn for document-focused applications, but less suitable for heavy analytical workloads and multi-region low-latency requirements.

Google Cloud Bigtable Google-proprietary managed service optimized for time-series and big data analytics, but lacks DataStax's multi-model capabilities and real-time AI focus.

ScyllaDB Open-source Cassandra-compatible database with superior raw performance, but requires more operational expertise; DataStax offers the managed/serverless convenience layer.

FAQ

What does DataStax do? +

DataStax provides Astra DB (cloud), DataStax Enterprise (on-premises), and Astra Streaming—all built on proven open-source technologies (Apache Cassandra and Pulsar). It automates the operational complexity of running distributed databases at scale, enabling enterprises to build real-time AI applications with multi-region, low-latency data access.

How much does DataStax cost? +

Astra DB uses a pay-as-you-go serverless pricing model with no upfront costs. Exact pricing tiers are not publicly listed; users should contact sales or check the website for current rates. DataStax Enterprise pricing requires custom quotes based on infrastructure and support needs.

What are alternatives to DataStax? +

MongoDB Atlas (document-focused, easier learning curve), Amazon DynamoDB (simpler key-value use cases, AWS-native), Google Cloud Bigtable (analytics and time-series data), and ScyllaDB (high-performance open-source Cassandra alternative).

Who uses DataStax? +

Fortune 500 enterprises including Verizon, Capital One, and Audi; companies requiring real-time data processing like ACI Worldwide (fraud detection) and SkyPoint (healthcare analytics). Target: large organizations with multi-region requirements, high transaction volumes, and real-time analytics needs.

How does DataStax compare to MongoDB? +

MongoDB (45% NoSQL market share) dominates in developer accessibility and document storage, but DataStax excels at distributed, real-time workloads requiring multi-region low-latency access and complex analytics. DataStax's multi-model support (tabular, search, graph) is superior for enterprises mixing different data types.