The Monthly Active Row Has Thorns

MAR is a vestige of legacy batch ETL. Streaming is the engine for Real-Time AI.

Data Rose with Thorns

Why the "Rose" of Legacy ELT is Pricking Your Budget

💸

The $5 Connection Tax

Starting January 2026, every active connection now costs a minimum of $5/month. Low-volume connectors that were once free now line the bill.

🧠

Stale Data Kills AI

Batch ETL feeds your AI 24-hour old context. You cannot build intelligent agents on yesterday's news. Latency is the new downtime.

🗑️

Deletes are the New Revenue

Delete events now count towards paid MAR. You are literally paying to remove data you've already paid to ingest.

📉

The Death of Bulk Discounts

Pricing has shifted from connector-level isolation. More connectors now equals exponentially higher costs.

Streamkap Architecture Diagram - Real-time Data Movement

Moving to Continuous Streams

Streamkap utilizes a streaming-first architecture. Instead of micro-batches and files, we hook directly into the database transaction log.

  • Sub-second Latency: From log to warehouse in milliseconds. The heartbeat of AI agents.
  • Direct Ingestion: Bypass the file stage entirely via Snowpipe Streaming.
  • Pricing by Physics: Paid by GB volume, not abstract row counts.

Voice of the Data Engineer

"We are experiencing a huge spike (more than double) in monthly costs following the latest changes... the pricing shoots up dramatically."

r/dataengineering

"Our current priority is to dump as much Fivetran as we can as fast as possible. Just moving NetSuite off will cut our bill by 50%."

u/latro87

"It makes it really hard to estimate costs before you get the bill. Each connector is priced independently, making cost optimization harder."

r/dataengineering

The Mathematical Verdict

Metric Fivetran (Legacy) Streamkap (Modern)
Pricing Model Monthly Active Rows (MAR) Data Volume (GB)
Latency 15 min - 1 hour < 1 Second
Snowflake Load Cost High (Batch COPY INTO) Low (Snowpipe Streaming)
Data Fidelity Low (Snapshots) High (Log-based CDC)
AI Readiness Low (Stale Context) Real-Time (Instant Action)
GenAI Use Cases Historical Reporting Autonomous Agents
Security SaaS Processing BYOC (Your VPC)

AI Agents Need Now, Not Yesterday

"Monthly Active Rows" builds a legacy of stale context. Streaming builds a future of real-time action.

🚫 The Legacy Trap

Legacy ETL relies on batches. By the time your AI agent receives the data, the "active row" is already history.

Stale Context = Hallucination

An AI agent acting on 24-hour old data isn't intelligent—it's dangerous.

⚡ The Streaming Reality

Real-time context allows AI to act on intent, not just history. Capture the event the millisecond it happens.

"Our AI screening demands real-time data. If you lose one or two days, you might lose the best candidate. Streamkap ensures that as soon as a candidate applies, our AI can process them."
- Juliano Tebinka, CDO at InHire
InHire achieves sub-second latency for AI candidate screening.

Reduce TCO by 3x

By aligning pricing with physics and architecture with speed, Streamkap offers a lower Total Cost of Ownership while delivering the real-time fuel your AI needs.

40x Lower Ingestion Cost
4x Faster Freshness
1GB ≈ 5 Million MAR

Ready to stop counting rows and start streaming value?

De-thorn your stack in 3 steps. Move to the next generation of data movement.

Get Started with Streamkap