In today’s data-driven world, businesses rely heavily on real-time data streaming for business operations. As companies migrate their workloads to the cloud, their expectations and understanding of cloud-native principles grow. They increasingly aim for solutions that are more closely aligned with modern cloud-native principles and capabilities. Apache Kafka has become the most popular open-source, distributed streaming platform that offers an array of use cases that align perfectly with the principles of cloud-native architecture.

However, the adoption of Kafka has been hindered by the significant operational costs of running it in the cloud, with infrastructure costs accounting for over 90% of the total expense.

Companies using Apache Kafka for data streaming often face significant cost challenges, particularly as data volumes grow exponentially. With the rise of Generative AI, enterprises need vast amounts of data to train and refine their models, further amplifying the costs associated with data movement and storage. With the appropriate architecture and configuration, Kafka costs can be cut without compromising performance for latency-sensitive use cases.

Continue reading the blog to explore the cost challenges in data streaming, and how Calsoft has worked closely with customers to help them identify optimal alternatives, significantly reducing costs and maintaining performance.

The Cost Challenge in Data Streaming

Many enterprises adopt Kafka as their go-to solution for real-time data streaming. While it is a robust and widely used platform, it can quickly become cost expensive as data volumes surge. Kafka’s strength lies in streaming data to multiple consumers across regions or services. However, each message sent to multiple consumers significantly amplifies outbound network costs. Unlike cheaper inbound traffic, outbound traffic is costly, making large-scale data dissemination an expensive challenge.

Key cost factors include:

• High Infrastructure Costs: Kafka requires extensive infrastructure to manage storage, replication, and redundancy, leading to higher operational expenses.
• Scalability Challenges: As organizations scale their data streaming needs, the cost of adding brokers and maintaining clusters increases exponentially.
• Data Storage Expenses: Companies handling vast amounts of streaming data, such as IoT-driven businesses, often find that Kafka’s storage requirements add significant costs. Storage costs in cloud-based data streaming platforms vary based on performance, availability, and access frequency.

Local storage, such as NVMe and SSDs, offers high performance but is significantly more expensive than object storage like Amazon S3. Cloud providers charge a premium for disk performance, increasing local storage costs. Object storage is more cost-effective and scalable but incurs additional costs for API requests and higher latency

• Operational Overhead: Managing Kafka clusters requires dedicated teams, adding to resource expenditures.
• High Network cost in Cloud-based data streaming: Networking is a major cost factor in cloud-hosted data streaming platforms due to continuous data movement between producers and consumers.

 Network Costs = Data Transmission Costs + Replication Costs

• Data Transmission Costs: Includes ingress and egress charges based on the volume of messages exchanged.
• Replication Costs: Incurred due to inter-availability zone replication, which varies across cloud providers.

Public networking incurs high costs due to expensive data egress charges, which escalate with increased data reads (fanout ratio), making it costly for use cases like IoT analytics. In contrast, private networking significantly reduces egress costs by 75-89% but requires complex setup and licensing fees that partially offset the savings.

As shown in the figure, Kafka ingests and stores large volumes of data from multiple sources, making network bandwidth a major cost factor in cloud environments. Its architecture replicates data across brokers for durability, tripling storage needs. Combined with long-term data retention, this redundancy makes storage a major cost factor in cloud environments.

The Rising Demand for Cost-Efficient Data Streaming

Industries such as oil and gas rely on IoT-based solutions that generate massive amounts of sensor data. These companies must stream, process, and analyse this data in real time to optimize operations, enhance safety, and improve predictive maintenance. However, Kafka’s increasing costs present a major roadblock.

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.

At Calsoft, we have worked closely with customers who faced these cost constraints and helped them identify optimal alternatives. One such solution is StreamNative, a modern data streaming platform that significantly reduces costs while maintaining high performance and scalability.

This blog explores the challenges of cost-intensive data streaming and how StreamNative can be a game-changer for businesses, especially those in IoT-heavy industries like oil and gas.

How Calsoft Helps Enterprises Optimize Costs

Our approach at Calsoft begins with an in-depth analysis of our customers’ Kafka implementations to identify areas of excessive expenditure. Our key steps include:

1. Assessing Current Streaming Costs – Evaluating storage, compute, and operational expenses associated with Kafka deployments.
2. Identifying Optimization Areas – Identifying inefficiencies, such as underutilized resources, high replication overhead, and unnecessary data retention policies.
3. Recommending alternatives and Seamless Migration Strategy – Implementing a smooth transition plan to shift from Kafka to alternatives with minimal disruption.
4. Performance Optimization – Ensuring that the new system delivers high throughput, low latency, and optimized resource utilization.

Recognizing these pain points, Calsoft’s engineering team conducted a benchmark study to evaluate StreamNative Cloud and AWS MSK across critical metrics such as maximum throughput, latency, and fault tolerance.

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

StreamNative: A Cost-Effective Alternative to Kafka

StreamNative, built on Apache Pulsar, offers significant advantages:

• Lower Storage and Infrastructure Costs: Unlike Kafka, which requires significant disk space and compute power, StreamNative uses a tiered storage approach that reduces storage costs.
• Leaderless Architecture for Scalability: Unlike Kafka’s leader-follower architecture, which demands more resources, StreamNative’ s leaderless design ensures efficient scalability without excessive infrastructure investments.
• Lakehouse-Native Storage: StreamNative integrates seamlessly with data lakehouses, allowing businesses to store vast amounts of data cost-effectively while maintaining high-speed streaming capabilities.
• Reduced Operational Overhead: With built-in automation, StreamNative simplifies data streaming operations, reducing the need for dedicated teams.

Get more insights from the blog How to Run a 5 GB/s Kafka Workload for Just $50 per Hour

The Future of Cost-Efficient Data Streaming

The exponential growth in data generation, fuelled by IoT, AI, and Generative AI, demands a cost-effective approach to real-time data streaming. With AI and data analytics continuing to shape business strategies, companies must adopt cost-efficient, scalable, and high-performance data streaming solutions. Kafka, while widely adopted, often leads to unsustainable costs.

StreamNative, with its leaderless architecture, Lakehouse-native storage, and efficient resource utilization, presents a compelling alternative to Kafka, especially for enterprises dealing with vast data volumes.

If your organization is struggling with high Kafka costs, it’s time to explore smarter alternatives. Get in touch with Calsoft to optimize your data streaming strategy and maximize cost savings today!