Name: WarpStream
Brand: WarpStream
Availability: InStock

AI & ML Data Pipelines

AI companies need to stream telemetry, training data, and model outputs at scale while maintaining full data sovereignty for compliance and security.

‍With WarpStream, BYOC deployment keeps all data in your S3 buckets. WarpStream has zero access to your data. Scale streaming with zero ops -- engineers focus on models, not infrastructure.

Full data ownership, data stays in your S3/GCS/Azure Blob

Zero access by design, not even WarpStream personnel can reach your data

Zero hours spent on scaling or ops

Kafka-compatible: use existing client libraries, just change the URL

View Case Study

"You don't have to worry about the relationship between storage and compute. It's just like, data go in, data go out. WarpStream is exactly the abstraction that you want out of Kafka. You pay somebody a small amount of money, you get to keep your data. It just scales up and you don't think about it. I think we've spent zero hours thinking about scaling WarpStream in the last few months. It's just like it's a solved problem.."

Alex Haugland — Software Engineer, Cursor

Log Aggregation & Observability

Traditional Kafka clusters for logging are expensive due to inter-AZ replication, hard to scale during traffic spikes, and require constant broker tuning.

‍With WarpStream, stateless Agents stream logs directly to object storage with zero inter-AZ fees. Auto-scaling matches ingest volume automatically -- no capacity planning required.

Zero inter-AZ networking costs

Auto-scaling agents match traffic spikes

Infinite retention on object storage

Drop-in replacement for existing Kafka log pipelines

View Case Study

"By switching from Kafka to WarpStream for their logging workloads, Robinhood saved 45%. WarpStream auto-scaling always keeps clusters right-sized, and Agent Groups eliminate noisy neighbors and complex networking like PrivateLink and VPC peering."

Robinhood — Case Study

Real-Time Analytics

Batch-oriented ETL pipelines introduce hours of lag. Managed Kafka solutions for real-time ingest are costly and operationally complex at scale.

With WarpStream, you can stream events into your data warehouse in near real-time using Managed Data Pipelines, powered by Bento (open-source, MIT-licensed). Simple YAML config, no extra infrastructure, running entirely inside your cloud account.

Near real-time delivery to warehouses

Managed Data Pipelines powered by Bento -- zero-code YAML config

Pipelines run inside your VPC; raw data never leaves your account

100+ ready-made integrations for sources and sinks

View Case Study

"Character.AI's journey in data management reflects our commitment to leveraging innovative technologies that enhance operational efficiency and reduce costs. By transitioning to WarpStream and adopting a real-time data approach, we have not only improved our data processing capabilities but also positioned ourselves to better serve our users."

Character.AI — Engineering Team

Data Lake Ingestion

Getting streaming data into Iceberg/data lake formats requires complex pipelines, separate compaction jobs, and ongoing maintenance.

With WarpStream Tableflow, automatically materialize any Kafka topic as an Iceberg table. Fully managed ingestion, compaction, and schema evolution, no extra infrastructure.

Works with any Kafka-compatible source, not just WarpStream

Fully managed compaction, table maintenance, and retention

Schema evolution built in (Avro and Protobuf)

Query with BigQuery, Athena, DuckDB, ClickHouse, Trino, or Glue

Explore WarpStream Tableflow

‍The easiest, cheapest, and most flexible way to convert Kafka topic data into Iceberg tables with low latency, and keep them compacted. Works with any source Kafka cluster.

WarpStream Kafka source cluster compatibility diagram

Change Data Capture (CDC)

CDC workloads produce high partition counts and require infinite retention. Traditional Kafka tiered storage is unreliable and expensive to scale to petabytes.

With WarpStream you can easily store petabytes of CDC data with infinite retention. No relationship between partition count and hardware. Historical and live reads perform identically.

Infinite retention at object storage prices

No partition-based hardware scaling, hundreds of thousands of partitions

Consistent performance for historical and live reads (not tiered storage)

Orbit for offset-preserving migration from any Kafka-compatible source

View Case Study

"The sort of stuff we put our WarpStream cluster through wasn't even an option with our previous solution. It kept crashing due to the scale of our data, specifically the amount of data we wanted to store. WarpStream just worked."

Jeffrey Ling — CTO, Goldsky

Event-Driven Microservices

Running Kafka for service-to-service eventing means managing brokers, rebalancing partitions, and overprovisioning for peak load.

With WarpStream, stateless, auto-scaling agents replace brokers entirely. Use Agent Groups to isolate workloads while sharing a single logical cluster. Change one URL and you're live.

Drop-in Kafka protocol, change one URL

Agent Groups flex across VPCs, regions, or cloud providers

No broker rebalancing, no hot spots, no partition math

Multi-region clusters with RPO=0 and automatic failover

Keep It Simple Samsa

Kafka on easy mode

Why Switch?

Pricing

Eliminates the need to overprovision for peak load by seamlessly and instantly scaling your cluster in and out. With built-in autoscaling, you can configure usage-based policies and let the system manage resources dynamically. No manual tuning required.

Because WarpStream writes directly to cloud object storage, there's no reliance on network-attached disks, dramatically cutting costs. It also entirely eliminates inter-AZ networking fees. The result: lower TCO, simplified operations, and infrastructure that just works.

Autoscalingthroughput + agents

—GiB/s

—agents