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Start with the workload and its service objectives
Before choosing brokers, partitions or shards, describe what the pipeline must carry and what it must guarantee. Capture the workload over time, not just as one average or peak figure.
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- Traffic: average and peak events per second and bytes per second, maximum record size, peak duration and burst frequency.
- Producers: producer count, batching and compression behavior, and the expected distribution of records across keys.
- Readers: number of consumer groups, each group’s read rate, and the parallelism required to meet its processing target.
- Service objectives: acceptable processing latency, availability and recovery time.
- Storage: retention period and the storage needed at the workload’s actual encoded size.
- Change and recovery: expected growth, likely disruptions, the backlog that can accumulate during an interruption, and how quickly it must be drained afterward.
These inputs matter because managed Kafka capacity models account for replication, reads and infrastructure bottlenecks—not only producer ingress. Kinesis designs also depend on record rates, bytes and read behavior. Google Cloud’s Managed Service for Apache Kafka sizing guidance and AWS’s MSK right-sizing guidance both frame capacity as a workload-dependent estimate.
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Estimate the work imposed on the service, including writes, replicas and reads. Keep the assumptions visible: two systems with the same producer rate may need very different capacity when their replication factors, consumer fan-out, batch sizes or latency objectives differ.
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Managed Kafka: include replication and consumer traffic
Google Cloud’s documented method calculates total write bandwidth from the produce rate multiplied by the replica count. It also includes consumer reads and replica synchronization in total read bandwidth, then derives a write-equivalent rate to estimate vCPU and memory. Its current planning guidance uses an estimated 20 MB/s per vCPU for a single-zone cluster and 4 GiB of memory per vCPU. These are service planning assumptions, not guaranteed throughput; validate them with the actual workload. Google Cloud also notes that batches smaller than 10 KB can deliver less throughput per CPU than its benchmark assumptions. See its cluster-sizing documentation.
Amazon MSK: find the narrowest sustained ceiling
For MSK, compare the relevant limits on storage throughput, broker-to-storage (EBS) network throughput and EC2 broker network throughput. The smallest applicable sustained ceiling constrains the cluster. Replication factor and consumer-group count affect the network and storage work, so an ingress-only calculation can materially understate load. AWS describes its formula as a theoretical upper bound; latency-sensitive or compute-intensive workloads may sustain less. Do not treat a calculated maximum as a promise. The assumptions and method are in AWS’s MSK right-sizing article.
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Kinesis: use the current shard mode and service limits
For shard-based designs, account for both record-rate and byte-rate constraints using the current Kinesis mode and documentation. A 2019 AWS scaling article gives an example of provisioned shards supporting up to 1 MB/s or 1,000 records/s for writes, and up to 2 MB/s and five read transactions per second for shared reads; enhanced fan-out provides dedicated consumer throughput. These are dated examples, not a substitute for checking current service limits before sizing or implementation. Consult AWS’s Kinesis scaling article.
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Choose parallelism—and test for skew
Partition or shard count needs to support both how producers distribute writes and how many consumers must work concurrently. A high aggregate capacity estimate is not enough if a small number of partitions or shards receive most of the traffic.
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- Use peak consumer parallelism as one input to partition or shard planning.
- Check whether producers can distribute records across the available partitions or shards at the expected rate.
- Measure key distribution. A heavily used key can create a hot partition or shard while other capacity sits idle.
- Account for ordering requirements before changing the key strategy to spread load.
AWS’s current MSK partition guidance explains how consumer parallelism and producer throughput inform partition count; it does not establish a universal count that suits every topic. Validate the choice with the intended topic, cluster and key distribution. Read AWS’s MSK partition guidance.
Set headroom for peaks and recovery
Provision beyond the expected steady flow for growth, brief peaks, deployments, network interruptions and consumer recovery. Recovery capacity deserves its own calculation: after an interruption, consumers must process new events and drain the accumulated backlog. Estimate whether the design can do both within the recovery objective.
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Published headroom figures are context-specific, not interchangeable rules. Google Cloud recommends starting with a 50% target vCPU utilization when traffic shape is unknown; if the shape is known, its guidance says to set the target based on average write-equivalent bandwidth relative to peak bandwidth. AWS’s 2022 MSK right-sizing article, reviewed and updated in November 2025, recommends actual production throughput at 80% of theoretical sustained throughput for its described calculation. A 2019 AWS Kinesis article illustrates adding 25% headroom. Each figure reflects a different provider model or example, so choose a margin based on your own peak duration, volatility and recovery needs, then test it. Sources: Google Cloud sizing guidance, AWS MSK sizing guidance and AWS Kinesis scaling guidance.
Check storage, network, quotas and scaling constraints
A design is not operationally viable just because its nominal service capacity is sufficient. Verify the resources and limits needed to provision, run and scale it in the intended region and account.
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- Storage: estimate retained data using the actual record size and retention period, and check the platform’s storage-throughput ceiling.
- Network and compute: include broker networking, broker-to-storage traffic and compute capacity in the bottleneck analysis.
- Service limits: check applicable partition, shard and replica limits for the selected service and configuration.
- Quotas: confirm regional and project or account quotas, including compute quota required for autoscaling. Google Cloud notes that insufficient Compute Engine quota can prevent Dataflow jobs from starting or autoscaling; its planning guidance also covers project-level Pub/Sub quotas and quota increases. See Google Cloud’s Dataflow planning guidance.
- Client controls: Apache Kafka supports broker-enforced client quotas for network bandwidth and request-rate resource use. Review the limits relevant to your clients in the Apache Kafka 3.5 quota design documentation.
Validate the estimate with a representative test
Provider formulas give a starting point, not proof that a production workload will meet its latency and recovery objectives. Test the complete workload shape, including the factors that affect distribution and infrastructure load.
- Reproduce realistic traffic: use representative payload sizes, peak rates and burst durations, plus the expected key distribution.
- Use production-like client behavior: match batching, compression, producer count, replication, consumer groups and processing logic.
- Include retention and recovery: test the storage pattern and a consumer interruption or backlog catch-up when those are part of the service objective.
- Measure the result: confirm the target peak is sustained while latency and availability remain within the required objectives.
- Identify the bottleneck: monitor CPU, storage and network saturation, throttling, consumer lag and hot partitions or shards.
- Repeat after material changes: retest when traffic shape, client configuration, broker type or topology changes.
Google Cloud recommends testing with the real workload for the most accurate sizing; AWS likewise advises performance testing to verify and tune a sizing estimate. Sources: Google Cloud Managed Service for Apache Kafka, AWS MSK right-sizing and AWS Kinesis scaling.
Compare platforms using the same workload
Managed Kafka and shard-based services expose different sizing units and scaling mechanics. Compare candidate designs against one workload and the same service objectives rather than comparing a nominal shard figure directly with a broker or partition figure.
| Comparison area | What to measure or verify |
|---|---|
| Sustained ingress | Peak bytes and records per second, including burst duration. |
| Read fan-out | Read throughput under the planned consumer groups or consumers. |
| Replication | Additional storage and network work for the chosen replication configuration. |
| Parallelism and skew | Consumer concurrency, write distribution and behavior under hot keys or shards. |
| Infrastructure ceilings | Storage, broker-to-storage network and broker network limits relevant to the design. |
| Service outcomes | Latency at target load and the ability to recover and drain backlog. |
| Operations and cost | Quota and scaling behavior, plus the cost at the margin needed to meet peak and recovery requirements. |
Size for the measured workload and its failure-and-recovery behavior; then treat the estimate as a hypothesis to prove under representative load, not a capacity guarantee.
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