What’s the fastest way to run a production POC on Redpanda and measure latency and TCO vs our current Kafka/Confluent setup?

Most teams ask this question the moment Kafka costs spike or agent workloads start hammering their clusters: how do we run a real production POC on Redpanda, fast, and prove the latency and TCO story vs our existing Kafka/Confluent setup?

This guide lays out a practical, engineer-first path—from “let’s try Redpanda” to a production-grade proof you can take to your SRE lead and your CFO. We’ll stay grounded in what matters: Kafka compatibility, end-to-end latency, throughput, and total cost of ownership (TCO) under real production load.

Quick Answer: Use Redpanda as a drop-in Kafka-compatible cluster for a single, well-bounded production workload, mirror traffic from your existing Kafka/Confluent deployment, and measure end-to-end latency and cost with the same tooling you use today. Focus on one high-volume or latency-sensitive service, run for at least one traffic cycle (days → weeks), and compare: p99 latency, hardware footprint, and operational overhead.

The Quick Overview

What It Is: A step-by-step, production-safe POC pattern for evaluating Redpanda as a Kafka-compatible streaming platform and Agentic Data Plane, with controlled blast radius and concrete latency/TCO measurements.
Who It Is For: Platform teams, data engineers, and architects currently running Kafka or Confluent who need hard numbers—not slideware—to justify change.
Core Problem Solved: You need to know if Redpanda is actually faster, simpler, and cheaper in your environment, under your workloads, without risking production stability.

How It Works

At a high level, the fastest way to run a production POC on Redpanda and measure latency/TCO is:

Select and scope a production workload that is both important and self-contained.
Stand up a Redpanda cluster (managed or self-hosted) that mirrors your current Kafka/Confluent setup for that workload.
Mirror traffic and run side-by-side while measuring latency and cost with your existing observability stack.

Because Redpanda is fully Kafka API compatible and ships as a single binary with zero external dependencies, you avoid most of the usual lift-and-shift friction. You don’t rebuild apps—you re-point them. You don’t re-learn the ecosystem—you keep using Kafka clients and tools.

Here’s the end-to-end flow:

Connect (Mirror / Route Traffic):
Stand up Redpanda, configure topics and ACLs, and either mirror traffic from Kafka or route a subset of production traffic to both clusters.
Control (Scope & Guardrails):
Keep the blast radius tight: one domain, a handful of services, clear rollback. If you’re testing agent workloads, enforce the same identity and authorization rules in Redpanda that you have in your current stack.
Operate (Measure Latency & TCO):
Run under production conditions, capture p50/p95/p99 latencies, throughput, hardware usage, and operational work. Compare those metrics directly to your Kafka/Confluent baseline.

Step 1: Choose the Right Production POC Scope

You’ll get the most signal by picking one of these patterns:

High-volume, steady traffic
Example: clickstream, ad events, IoT, logs.
Goal: expose throughput vs hardware and sustained latency under load.
Latency-sensitive pipeline
Example: fraud detection, order routing, customer-facing recommendations, agentic copilots responding to live events.
Goal: measure end-to-end reaction time (producer → broker → consumer/agent).

Ideal POC characteristics:

3–10 topics, not 300.
A few producer/consumer services (microservice + stream processor + sink).
Clear SLOs (e.g., p99 < 50 ms, 10K messages/sec, 24x7 availability).
Exists in both test and production environments so you can dry-run the setup first.

Key decision: Decide if this POC is Kafka-only, or if you’re also evaluating Redpanda as an Agentic Data Plane for AI agents. If agents are in scope, include one pipeline where agents both read and write events (e.g., support copilots updating case state), and plan to measure:

Agent response latency with streaming context
Policy enforcement (who can do what, before it happens)
Auditability and replay of agent sessions

Step 2: Provision Redpanda the Fast Way

You have two fast paths:

Redpanda Serverless (fastest to start):
From zero to streaming in ~5 seconds. Great for initial benchmarking and functional testing.
Redpanda Enterprise / BYOC / Self-Hosted:
When you’re ready to test realistic production networking, security, and cost.

For a production-grade TCO comparison, you ultimately want Redpanda running in a shape comparable to your current Kafka/Confluent deployment—same cloud, similar instance class, same storage tier.

Minimal Redpanda production-like setup

Because Redpanda is a single C++ binary with zero external dependencies, your cluster is just:

A small number of brokers (often fewer than Kafka because of higher efficiency).
Optional tiered storage for long retention with reduced local SSD footprint.
Kafka-compatible ports, SASL/OIDC/RBAC for auth/authz.

Typical steps:

Size the cluster:
Start with equivalent or slightly smaller hardware compared to your Kafka brokers. Redpanda routinely delivers:
- Up to 10x lower average latency vs Kafka
- 3–6x better cost efficiency from reduced hardware and operational overhead
- Tested workloads like NYSE at 1.1T records/day, Teads with 100B events and 87% reduction in brokers, and gaming tests at 100GB/min and 100K tx/sec
Deploy:
- Self-managed: use the Helm chart or Terraform in your VPC/k8s.
- Managed/BYOC: let Redpanda run the control plane while your data stays in your cloud account.
Configure topics & security:
- Create matching topics (names, partitions, replication factor) to your Kafka/Confluent POC workload.
- Configure authentication (SASL/OIDC) and ACLs so your existing Kafka clients can connect without code changes.

Step 3: Wire Producers & Consumers with Kafka Compatibility

Redpanda is fully Kafka API compatible, so the fastest path is:

Keep your existing Kafka clients.
Change only the bootstrap servers to point to Redpanda.
Keep serializers, schemas, and client libraries as-is (including Confluent clients).

Example adjustments:

bootstrap.servers: change from kafka-prod.internal:9092 to redpanda-poc.internal:9092.
SASL/SSL: match your existing auth mode (or switch to OIDC if you’re testing Redpanda’s identity story).

Smoke test in lower environments:

Validate end-to-end message flow.
Validate schema evolution if you’re using Schema Registry (or test alternative patterns).
Confirm consumer group behavior, offsets, and rebalancing are as expected.

Once that passes, you’re ready for side-by-side production traffic.

Step 4: Mirror or Split Production Traffic Safely

To keep the POC production-realistic without risking downtime, you have two common patterns:

Pattern A: Dual-write (producers write to Kafka & Redpanda)

Producers publish the same messages to both clusters.
Consumers continue reading from Kafka until you’re ready to flip.
You compare Kafka vs Redpanda behavior in parallel.

Pros:

Very easy rollback (stop sending to Redpanda).
Perfect for read-only POC where you don’t want dual consumers affecting external systems.

Cons:

Slight extra overhead on producers.
You need idempotent logic if you ever let downstream systems read from both.

Pattern B: Kafka → Redpanda mirroring

Keep producers writing only to Kafka.
Use a mirroring tool or connector to replicate topics to Redpanda.
Redpanda consumers subscribe to mirrored topics.

Pros:

Zero change to producers during initial POC.
Good for testing read-heavy workloads and AI agents reading historical + streaming context from Redpanda.

Cons:

Extra hop adds latency vs “true” Redpanda producer.
Harder to compare identical producer-side behavior.

Fastest path in practice: Start with mirroring for validation, then move one or two producers to dual-write or Redpanda-only to measure true end-to-end latency.

Step 5: Instrument for Latency Measurements (Apples-to-Apples)

To compare Redpanda vs Kafka/Confluent, measure the same metrics, with the same tools, using the same definitions.

Metrics to prioritize

End-to-end latency
- Producer timestamp → consumer processing timestamp.
- Look at p50, p95, p99, and max under steady load and burst conditions.
Broker-level latency
- Produce and fetch latencies from client metrics and broker dashboards.
- Request queue times, disk flush times if available.
Throughput & utilization
- Messages/sec and bytes/sec per topic.
- CPU, memory, disk, and network for brokers.
Error & retry behavior
- Time spent in retries.
- Any differences in timeout behaviors between Kafka and Redpanda clients.

Because Redpanda often delivers 10x lower average latencies vs Kafka and supports GB/s throughput with fewer brokers, you should see deltas even before tuning.

HOW to capture the numbers

Reuse your current observability stack:
- Prometheus + Grafana, Datadog, New Relic, etc.
- Export Redpanda metrics to the same system for one unified view.
Annotate the timeline when you flip services from Kafka to Redpanda, so you can compare before/after.
Capture at least one full traffic cycle: daily peaks, batch jobs, weekends, etc.

If AI agents are involved, add:

Agent response latency: event ingestion → agent output, using Redpanda as the Agentic Data Plane.
Session replay: log and replay agent sessions against Redpanda to measure consistency and debuggability.

Step 6: Measure TCO: Hardware, Ops, and Licensing

TCO isn’t just cloud bills. It’s the combo of:

Hardware/infra footprint
Operational complexity and toil
Licensing and support costs

1. Hardware and infra

Compare:

Number and size of brokers in Kafka/Confluent vs Redpanda.
Storage utilization with and without tiered storage.
Cross-AZ or cross-region traffic patterns.

Because Redpanda is a performance-engineered C++ system, customers typically see:

3–6x better cost efficiency vs traditional Kafka stacks.
Significant reductions in broker counts (Teads saw 87% fewer brokers for 100B events).

Record:

Cost per month per environment (prod, staging) for Kafka vs Redpanda.
Cost per MB/GB of data processed or per million messages.

2. Operational overhead

Track for the duration of the POC:

Number of incidents or on-call interventions.
Time spent on:
- Partition and replica management
- Broker upgrades
- Balancing and rebalancing
- Zookeeper or extra components (which Redpanda doesn’t require)

With Redpanda’s single binary, zero external dependencies model, you should see a meaningful drop in day-two operations. Less juggling between brokers, ZooKeeper, controllers, and ancillary services.

3. Licensing and support

Compare vendor support costs for equivalent SLAs.
Factor in any per-message or per-GB pricing if you’re using a managed Kafka/Confluent service vs Redpanda Cloud or BYOC.

Bring all three dimensions together into a simple table:

Before (Kafka/Confluent): infra, ops time, license/support.
After (Redpanda POC): projected costs assuming full migration.

Step 7: Document POC Results and Next Moves

For a production POC to carry weight with stakeholders, you need a concise readout:

Scope: which topics/services, traffic level, duration.
Latency: Kafka vs Redpanda, p50/p95/p99 under normal and peak load.
Stability: errors, retries, incidents, any regressions.
TCO: infra footprint, operator time, licensing.
Agent behavior (if applicable):
- Policy enforcement: what you could govern “before it happens.”
- Observability: what you could replay and audit.

From there, you can make a measured call:

Expand the POC to more services.
Graduate Redpanda to primary for the scoped workload.
Integrate Redpanda as your Agentic Data Plane for AI workloads, while maintaining Kafka compatibility for the rest of the estate.

Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
Kafka API compatibility	Lets your existing Kafka/Confluent producers and consumers connect to Redpanda by changing only bootstrap servers and security configs.	Fastest POC path with minimal code change; like-for-like latency and TCO comparison.
Single binary, zero dependencies	Runs the full streaming stack without ZooKeeper or multiple coordination components.	Simplifies deployment and operations, reducing infra sprawl and operator toil.
Performance-engineered C++ core	Maximizes hardware utilization, delivering 10x lower average latencies and GB/s throughput on fewer brokers.	Smaller cluster sizes and lower cloud bills for the same or better performance.

Ideal Use Cases

Best for production POCs where you must prove latency gains:
Because Redpanda’s Kafka compatibility and performance profile let you re-point a real workload and directly compare end-to-end latencies, without rewrites.
Best for teams evaluating TCO before a Kafka/Confluent migration:
Because you can run Redpanda side-by-side, reproduce your current topology, and measure hardware footprint and operational complexity under the same traffic.

Limitations & Considerations

POC duration that’s too short:
A 1–2 day test may miss weekly peaks, batch jobs, and failure scenarios. Aim for at least one full traffic cycle (ideally 2–4 weeks) to measure realistic latency and TCO.
Over-scoping the first POC:
Trying to mirror your entire Kafka estate on day one will slow you down and increase risk. Start with one well-defined pipeline, then expand once you’ve validated the gains.

Pricing & Plans

Redpanda offers multiple consumption models so you can run a production POC in the environment that best matches your Kafka/Confluent setup:

Redpanda Community Edition (self-managed):
Free, open core for teams comfortable running their own clusters. Best for hands-on evaluations where you want full control over infra sizing and benchmark conditions.
Redpanda Enterprise / Cloud / BYOC:
Commercial support, SLAs, and managed options, including Bring Your Own Cloud (BYOC) where the control plane is managed by Redpanda and data lives in your VPC. Best for teams needing enterprise features, 24x7 support, and a clear TCO story vs managed Kafka/Confluent.

For detailed pricing—including volume, support levels, and Cloud vs BYOC—reach out directly so we can model it against your current Kafka/Confluent spend.

Self-Managed / Community: Best for engineering teams wanting maximum control over infra and a deep technical POC, including performance benchmarking.
Enterprise / Cloud / BYOC: Best for organizations needing production SLAs, security/compliance guarantees, and a direct TCO comparison against existing managed Kafka/Confluent offerings.

Frequently Asked Questions

How long should a production POC run to fairly compare Redpanda vs Kafka/Confluent?

Short Answer: Aim for at least 2–4 weeks so you capture normal, peak, and failure scenarios.

Details:
You want to see how Redpanda behaves when your system is under stress, not just during happy-path traffic. That means:

At least one full business cycle (including peak hours).
Any scheduled batch jobs that hit your cluster hard.
Enough time to observe operational tasks (topic changes, rolling restarts, upgrades).

Run Kafka and Redpanda in parallel during this window, annotate when you make cutovers, and then compare latency, throughput, incidents, and operator time for a real-world TCO story.

Do we need to rewrite our Kafka/Confluent applications to test Redpanda?

Short Answer: No. Redpanda is fully Kafka API compatible, so you generally change only bootstrap servers and security configs.

Details:
For the POC:

Keep your Kafka producers and consumers.
Keep your serializers and schema strategy.
Keep your client libraries (including Confluent clients).

You:

Stand up Redpanda.
Create matching topics.
Change client configs to point at Redpanda and authenticate.

This lets you run a true apples-to-apples comparison on latency and TCO without introducing application rewrites as a confounding variable. If you adopt Redpanda long-term, you can then decide where to lean into additional features like tiered storage, SQL query layers across streams and history, and Agentic Data Plane capabilities for governing AI agents.

Summary

The fastest way to run a production POC on Redpanda and measure latency and TCO vs your current Kafka/Confluent setup is to:

Pick one meaningful, bounded production workload.
Stand up a production-like Redpanda cluster with Kafka-compatible topics and security.
Mirror or dual-write traffic so you can compare behavior side-by-side.
Measure latency and cost with your existing tools, over at least one full traffic cycle.
Document results for latency, cluster size, operator effort, and spend.

Because Redpanda is Kafka-compatible, runs as a single binary, and is engineered for 10x lower latency and 3–6x better cost efficiency vs traditional Kafka stacks, you can move from “what if?” to a production-grade proof in weeks—not quarters—without rewriting your applications.

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