Real-Time Streaming Cost Calculator: Brokers, Retention, and Egress
This free real-time streaming cost calculator sizes broker node count, storage, and egress cost for a Kafka-style event streaming platform from your peak events per second, average event size, and retention requirements, built for data platform architects planning real-time integration for IoT, ERP change events, or an AI agent event bus. Enter your event rate and retention needs, and the tool returns storage footprint, broker count, and total monthly cost including egress. The variable most teams underestimate is retention: a platform sized for a comfortable 3-day retention window becomes a very different, much larger cluster at the 30-day retention regulatory or replay requirements often actually demand.
Your numbers
Sustained peak event rate across all topics you plan to run on this streaming platform.
Serialized event payload size including headers and keys.
How long events must remain available for replay before expiring from the log.
Number of copies of each event stored across broker nodes for durability.
Usable disk capacity per broker after reserving headroom for compaction and overhead.
Compute and attached storage cost for one broker node at your chosen instance size.
Data consumed by downstream consumers outside the streaming platform's own network or region.
Cloud provider egress pricing typically runs $0.05-$0.12 per GB depending on destination and volume tier.
Your results
Planning estimate only. Actual cost also depends on compaction strategy, consumer group count, and whether you run a managed service versus self-hosted brokers. Load test against representative event volume before finalizing capacity.
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Why retention period drives cost more than event rate
Event rate determines throughput requirements, but retention period determines storage requirements, and storage is usually the larger cost driver in a streaming platform once you factor in replication. A platform handling a modest 5,000 events per second at 2KB average size generates roughly 850GB of raw daily volume; retained for 7 days with standard 3x replication, that becomes nearly 18TB of actual storage across the cluster. Extend retention to 30 days for compliance or replay requirements and the same event rate needs roughly 76TB, a four-fold jump that catches budget owners off guard when retention requirements change after the initial sizing was already approved.
- Retention period multiplies storage requirements linearly; doubling retention roughly doubles storage cost.
- Replication factor (2x, 3x, or 4x) compounds on top of raw retained volume for durability.
- Compliance or audit requirements for extended replay windows should be settled before initial sizing, not after.
- Tiered storage features in modern streaming platforms can offload older retained data to cheaper object storage.
Why egress cost surprises teams that sized only for storage
Cloud egress pricing, the cost to move data out of a provider's network to consumers elsewhere, is often an afterthought in initial streaming platform budgets, but for architectures where multiple downstream systems, partner integrations, or a multi-cloud AI pipeline consume the same event stream, egress can become a meaningful and recurring cost line. This is particularly relevant for event-driven AI architectures where an agent platform running in a different cloud region or provider needs to consume events in real time, generating sustained egress traffic that a storage-focused sizing exercise easily misses.
- Egress cost applies per consumer pulling data across a network or region boundary, and scales with consumer count.
- Multi-cloud or hybrid architectures where AI processing sits outside the streaming platform's home region incur ongoing egress cost.
- Co-locating heavy consumers in the same region or cloud as the streaming platform materially reduces egress spend.
- Budget egress separately from broker infrastructure cost; they scale on different dimensions.
Why real-time streaming is becoming core AI infrastructure
AI agents that need to react to a change the moment it happens, an inventory level crossing a threshold, a quality alert firing on the shop floor, a shipment status updating, depend on an event streaming backbone rather than periodic batch polling, since polling introduces latency and load that does not scale with the number of things an agent needs to watch. Enterprises building agentic AI on top of ERP and operational data increasingly find that a well-sized streaming platform is what makes truly reactive AI possible, versus an AI system that only ever answers questions about yesterday's data.
- Event-driven AI agents require a streaming backbone to react in real time rather than polling on a schedule.
- Polling-based architectures do not scale gracefully as the number of monitored conditions grows.
- A properly sized streaming platform is what separates a reactive AI agent from a purely conversational one.
- Size streaming infrastructure with your AI agent roadmap's event consumption needs explicitly included.
How Netray builds streaming infrastructure for manufacturers
Netray designs and sizes event streaming platforms for aerospace, defense, and electronics manufacturers building real-time integration between SyteLine, Infor LN, shop floor IoT, and AI agents through ERPray, and we size for actual retention and compliance requirements rather than a default that gets revisited expensively later. We account for egress cost across your specific cloud and consumer topology from the start, since retrofitting a network architecture to reduce egress after the fact is far more disruptive than planning for it initially. Engagements start with an event volume and retention requirements discovery phase against your actual systems.
Frequently Asked Questions
How much does extending retention from 7 to 30 days actually cost?
Roughly proportionally more storage, since retained volume scales linearly with retention days at a fixed event rate. Going from 7 to 30 days is roughly a 4.3x increase in stored volume before replication, and after standard 3x replication that difference compounds into a meaningfully larger broker node count and monthly cost. Run both retention scenarios through this calculator before committing to a compliance or replay requirement that assumes 30-day retention is a minor increment.
Should we use a managed streaming service or self-host brokers?
Managed services (Confluent Cloud, Amazon MSK, and similar) reduce operational burden significantly but typically cost more per unit of throughput and storage than self-hosted brokers on your own infrastructure. For regulated data requiring on-prem hosting under ITAR or CMMC, self-hosted is often not optional. For everything else, weigh the ongoing operational labor cost of self-hosting against the managed service premium; the crossover point depends heavily on your team's existing Kafka operations expertise.
Does egress cost apply if all our consumers are in the same cloud region?
Usually minimal or zero for same-region, same-provider traffic, since most cloud providers do not charge egress for intra-region transfer. Egress becomes significant specifically for cross-region, cross-cloud, or public-internet-facing consumers. If your AI processing or analytics platform runs in a different cloud than your streaming platform, budget for that egress cost explicitly rather than assuming it is negligible.
How do we estimate our actual peak events per second before we have built anything?
Start from known transaction volumes in source systems: ERP transaction counts, IoT sensor polling frequency, or existing application log rates, then apply a peak-to-average multiplier based on your business's actual traffic patterns, commonly 2-4x average for manufacturing operations with shift-based activity spikes. If genuinely uncertain, size conservatively for a mid-range estimate and validate with a short pilot before committing to full production capacity.
What is the biggest streaming platform sizing mistake?
Sizing storage for current retention requirements without accounting for compliance or business needs that will extend retention later, which forces an expensive and disruptive capacity expansion mid-project. The second most common mistake is ignoring egress cost entirely during initial sizing, only to discover it as a recurring surprise line item once multiple downstream consumers, especially cross-region AI pipelines, start pulling from the stream.
Get a streaming platform sizing plan that accounts for your real retention requirements and AI agent consumption patterns.
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