Kafka Use Cases: AI, Logs, CDC, Analytics

AI & ML Data Pipelines

Feed training data, telemetry, and model outputs through Kafka-compatible pipelines with full data ownership in your cloud.

Log Aggregation & Observability

Centralize logs from distributed services at scale. Eliminate inter-AZ fees and simplify ingestion with stateless agents.

Real-Time Analytics

Stream events into analytics warehouses and dashboards in near real-time. Reduce batch lag from hours to seconds.

Change Data Capture (CDC)

Stream database changes to downstream consumers. Infinite retention means you never lose historical state.

Data Lake Ingestion

Materialize Kafka topics as Iceberg tables with Tableflow. Fully managed compaction, schema evolution, and maintenance.

Event-Driven Microservices

Decouple services with durable, ordered event streams. Drop-in Kafka protocol means zero application changes.

Built for every streaming workload, from startups to enterprise scale

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Why Switch?

Company

Character.AI

Analytics

AI and Machine Learning

Dish Network

Telemetry

Telecommunications

Robinhood

Logging / Metrics

Financial Services

Cursor

Standard Messaging

AI and Machine Learning

thirdweb

Crypto

OrderMentum

Standard Messaging

E-commerce

Elisa

Cipher Owl

Crypto

Yunex Traffic

Standard Messaging

Transportation

Uplift Games

Video Games

SoundCast

Ad Technology

Shopflo

E-commerce

Sophos

Cybersecurity

pubX

AI and Machine Learning

PriceShape

Price Optimization Software

Plugo

WAITING ON GTM TEAM

E-commerce

Pixel Federation

Logging / Metrics

Video Games

Paramount Commerce

Financial Services

Omeda Studios

Video Games

Mendix

Standard Messaging

Enterprise Software and SaaS

Ctrl Hub

Enterprise Software and SaaS

Superwall

Financial Services

Pixis

AI and Machine Learning

ShareChat

ML Inference Logging

Social Media

PostHog

Standard Messaging

Product Analytics

Goldsky

Performant Tiered Storage

Crypto

Laurel

AI and Machine Learning

Liftoff

Standard Messaging

Ad Technology

Zomato

Logging / Metrics

Food Delivery

Securonix

Telemetry

Cybersecurity

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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.

WarpStream BYOC deployments give you full data sovereignty and keeps all of your data in your own cloud account. 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 Storage

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

"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

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Log Aggregation & Observability

Traditional Kafka clusters for logging are expensive due to inter-AZ replication and storage costs, 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 and cheap commodity object storage. 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

"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."

Ethan Chen — Robinhood Software Engineer

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Real-Time Analytics

Batch-oriented ETL pipelines introduce hours of lag. 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

"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

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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 — support hundreds of thousands of partitions without provisioning

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

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

"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

View Case Study

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