In the era of Gen AI and real-time analytics, businesses are competing to get insights from their data faster than ever. However, legacy streaming architectures’ hidden costs and performance bottlenecks are slowing them down and incurring expenses.
As companies migrate to the cloud, their demand for solutions aligned with cloud-native principles grows. Apache Kafka, the leading open-source distributed streaming platform, meets these needs with use cases that align perfectly with modern cloud-native architecture.

However, Kafka adoption is often challenged by high operational costs, with infrastructure alone making up over 90% of the total expense.

With the appropriate architecture and configuration, Kafka costs can be cut without compromising performance for latency-sensitive use cases. That’s where a smarter, cost-effective data streaming approach is rewriting the rules.

Continue reading the blog to learn practical strategies to optimize Kafka workloads – making them more efficient and cost-effective without compromising performance, and to transform your Generative AI applications with cutting-edge data streaming strategies.

Why Traditional Streaming Models Are Falling Short

Data is the new oil—but only if it can flow freely and efficiently. Most streaming platforms like Kafka rely on a leader-based architecture that creates hotspots, bottlenecks, and excessive cross-AZ (Availability Zone) traffic. The result? Higher latency, poor scalability, and increased infrastructure costs.
Enterprises trying to power Gen AI applications with these models face the challenge. With the rise of Generative AI, enterprises now require even larger datasets for model training and validation. Streaming and storing these datasets in Kafka can lead to exponential cost growth, making cost-efficient alternatives essential. Feeding high-quality, real-time data into Gen AI models demands a more agile and scalable solution.

Read our latest blog to gain more insights: Maximizing Cost Savings and Performance in Data Streaming: A Smarter Approach

The Calsoft + StreamNative Advantage

Recognizing this industry-wide challenge, Calsoft partnered with StreamNative to help enterprises open a new path to real-time intelligence. The StreamNative Ursa engine replaces legacy bottlenecks with a leaderless architecture and lakehouse-native storage.
This innovative model reduces inter-zone network traffic and eliminates the need for complex ETL pipelines. Businesses can now power Gen AI and advanced analytics with real-time data that is reliable and cost-efficient.
• Leaderless Design: Eliminates bottlenecks and allows dynamic load balancing
• Lakehouse-Native: Enables direct integration with data lakes for faster analytics
• Lower Network Costs: Avoids cross-AZ traffic and reduces storage overhead
• Real-Time + Batch: Simplifies infrastructure by handling both workloads without duplication

As discussed in Calsoft’s recent webinar, this approach redefines real-time streaming from a cost center to a competitive advantage.

Webinar Recording-Maximizing Cost Savings in Data Streaming

Gen AI Needs Better Data Pipes

In today’s AI-first landscape, data fuels every decision, recommendation, and predictive model. But not just any data—Gen AI needs high-throughput, trusted data in real time.
Here’s where the new architecture shines:

• Streamlines process pipelines, so Gen AI tools don’t suffer from outdated or inconsistent data.
• Cutting down cloud costs, making scalable AI adoption more viable.
• Reduces engineering effort, freeing up teams to innovate instead of handling infrastructure issues.

Real-World Impact: From Strategy to Results

The collaboration between Calsoft and StreamNative has already delivered measurable value to enterprises aiming to modernize their streaming stack.

Download our whitepaper, which compares StreamNative Cloud and AWS MSK performance under different workloads. It offers data-driven insights to help businesses choose the right cloud-native streaming solution and gain a competitive edge.

StreamNative Cloud Vs Amazon MSK

Customers are seeing:

• Up to 60% cost savings in network and storage
• Improved performance across multiple workloads
• Faster Gen AI implementation cycles

This indicates organizations no longer must choose between performance and cost—they can have both.

The Road Ahead: Scalable, Sustainable, Smart

With the rise of intelligent applications, Gen AI, and IoT, the pressure on data infrastructure is immense. Calsoft and StreamNative’s integrated approach offers a roadmap for any enterprise looking to scale smartly. This is a strategic shift toward a sustainable, future-ready data infrastructure.
By leveraging StreamNative Ursa’s next-gen architecture and Calsoft’s deep integration expertise, organizations can explore smarter alternatives to leaner, faster, and smarter streaming. Reach out to Calsoft to optimize your data streaming and reduce operational costs effectively.

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