Real-time
Change Data Capture
Power agents and applications with streaming CDC for Snowflake, ClickHouse, Databricks and more.
CORE WORKLOADS
Fast, reliable data pipelines
Change Data Capture
Stream inserts, updates, and deletes from your databases as they happen. No batch windows, no polling, no stale copies.
Explore CDCStream Processing
Transform, filter, join, enrich, and mask data in flight with SQL, Python, or JavaScript before it reaches its destination.
Explore Stream ProcessingStreaming Delivery
Move fresh, reliable data to warehouses, lakes, applications, and event systems with the connectors your stack already uses.
Explore the Platform- Latency
- Sub-second
- Connectors
- 60+
- Uptime SLA
- 99.99%
- Lower Cost at Scale
- 14x
WHY STREAMKAP
A simpler path from change to action
Streamkap gives data teams a managed foundation for CDC, streaming, and real-time processing — without running Kafka or Flink yourself.
Capture changes once
Reliable log-based CDC for Postgres, MySQL, MongoDB, SQL Server, Oracle, and more — with schema evolution built in.
Process data in motion
Apply real-time transformations, joins, filters, and routing as data flows through the platform.
Deliver with confidence
Managed infrastructure, observability, governance, and 99.99% uptime keep your pipelines moving without the operational overhead.
Deploy anywhere: SaaS • BYOC • Snowflake Marketplace
Book a DemoPLATFORM OVERVIEW
Move data from source to action
Databases
Postgres 
Oracle 
MongoDB 
SQL Server Events & Files
Kafka 
Files 
Deployment Options
Agents
Destinations
Snowflake 
Databricks 
BigQuery 
Redis S3 
Kafka 
ClickHouse Postgres AGENTIC DATA WORKLOADS
Make real-time data available to agents
When agents need live context, Streamkap gives them the same governed, processed streams that power your applications and analytics.
Your Sources
Transform, enrich, filter, join
Your Agents
Current context
Give agents access to database changes as they happen instead of relying on stale snapshots.
Enriched, not raw
Use the same stream processing layer to transform, filter, join, and mask data before it reaches an agent.
Fits your architecture
Deliver agent context through APIs, event streams, or the MCP Server — without creating a second data platform.
One more destination
Agents join warehouses, applications, caches, and event systems as another destination for trusted streaming data.
DEVELOPER WORKFLOW
Build and operate pipelines in minutes
Use the interfaces your team already knows to create, manage, and monitor real-time data pipelines.
For teams that want to bring governed Streamkap data into agent workflows:
claude mcp add --transport http \
--header "X-Streamkap-Client-ID: $ID" \
--header "X-Streamkap-Client-Secret: $SECRET" \
streamkap https://mcp.streamkap.com/mcpInstall, authenticate, and verify your pipeline setup:
npm install -g @streamkap/tools
export STREAMKAP_CLIENT_ID=...
export STREAMKAP_SECRET=...
streamkap doctorManage environments and pipelines as code with the Streamkap provider:
provider "streamkap" {
client_id = var.client_id
secret = var.secret
}Connect Streamkap to internal services and operational workflows:
curl -X POST https://api.streamkap.com/auth/access-token \
-d '{"client_id":"$ID","secret":"$SECRET"}'
curl https://api.streamkap.com/topics/details \
-H "Authorization: Bearer $TOKEN"Reliable streaming for teams operating at scale
SpotOn case study
Realtime
Sub-second latency
14x Lower Cost at Scale
Lower total cost of ownership
TESTIMONIALS
Why our customers love Streamkap
Great technology and a great team
Streamkap was 4x faster and had 3x lower total cost of ownership than our previous solution
Marcin Migda
Staff Data Engineer
Streamkap provides that speed at a cheaper cost, and combined with the other tools, we can build and raise the sophistication of the work that we can deliver. Without Streamkap, that was very difficult. That's really what it comes down to.
Dai Renshaw
Head of Data
Streamkap is a big part of our stack now because these data products that we're releasing heavily rely on the data that Streamkap is producing to Snowflake. If there's ever any issues, Streamkap can recover itself. Compared to our old tool, if one small thing happened, it would just completely break. On top of that, the cost that we have in Snowflake now for loading data on Streamkap is like next to nothing.
John Michael Mizerany
Senior Software Engineer
The old pipeline had a lot of overhead. Our old data pipeline was not running in near real-time and was very limited in scope. As I got to know the Streamkap platform, I decided — we should implement it, full steam ahead. From that set-up and configuration perspective, I don't even think we spent even a day. Then we did a cost-benefit analysis, and cost-wise, it was just a no-brainer to move to Streamkap.
Vikram Chauhan
Head of Data Engineering
The migration to Streamkap has resulted in clear and predictable billing, reducing unexpected costs. Success metrics include a 54% reduction in data-related costs. GCP Datastream lacked reliable support channels for issue resolution, but Streamkap provides prompt assistance through Slack, making it easy to consult and resolve problems quickly.
Kohei Hasegawa
CTO
Latest articles
CDC Cost Optimization for Streaming Destinations: Transparent Credit Math and Trade-Offs
Navigate per-row vs micro-batch pricing models for Snowflake, BigQuery, and Redshift CDC sinks. Learn how to forecast streaming costs before adoption and avoid bill surprises.
Silent CDC Failures and Timeout Detection: Building Durable Alerting
Most CDC pipelines alert on crashes but miss the slow failures that cost you most. Learn to detect latency creep early, recover from checkpoint without a full re-snapshot, and route alerts to the tools your on-call team already watches.
CDC from Multi-Tenant Databases with Sub-Second Latency
How Streamkap handles CDC at scale across multi-tenant databases with thousands of schemas, delivering sub-second latency without managing Kafka or Flink.