Kafka Platform: Build vs Buy Decision Framework
Framework for evaluating self-managed Kafka vs managed services like Confluent Cloud and MSK. TCO analysis, team skills assessment, and decision criteria.
The build vs buy decision for Kafka isn't about technology. It's about where you want to invest engineering attention.
I've helped organizations make this decision dozens of times. The ones who get it right focus on organizational capacity, not feature comparisons. The ones who get it wrong underestimate operational cost by 3-5x.
We thought self-managed Kafka would cost us $15K/month in infrastructure. Two years later, we realized the real cost was $80K/month when you count the three engineers who spent half their time on Kafka operations.
VP Engineering at a SaaS company
What You're Actually Buying
When you "buy" managed Kafka, you're purchasing operational capacity. When you "build," you're committing engineering time to infrastructure instead of product.
Self-managed means owning: cluster provisioning, broker tuning, security (SSL, SASL, ACLs), monitoring, alerting, incident response, upgrades, patching, disaster recovery. You'll also need chargeback and cost visibility to track consumption across teams.
Managed services abstract away: infrastructure provisioning, broker lifecycle, basic monitoring, upgrades.
They don't solve: application-level governance, cross-team access control, schema evolution strategy, cost optimization for your workloads.
TCO: What Most Organizations Miss
The license cost of Apache Kafka is zero. The operational cost is not.
| Self-Managed Cost | Monthly Range |
|---|---|
| Infrastructure (3-broker prod) | $3,000–$15,000 |
| Engineering time (1 FTE) | $15,000–$25,000 |
| Monitoring stack | $500–$2,000 |
| DR infrastructure | 80–100% of primary |
| Managed Service Cost | Monthly Range |
|---|---|
| Service fee | $3,000–$30,000 |
| Data transfer | $500–$5,000 |
| Add-on features | $1,000–$10,000 |
Managed services appear cheaper at small scale. Self-managed becomes economical at high, stable throughput—but only if you already have the team.
Team Requirements: The Honest Assessment
To run Kafka in production responsibly, you need 2 FTEs minimum for a single cluster: one Kafka admin, 0.5 platform engineer, 0.5 on-call coverage.
For enterprise-grade multi-cluster deployments: 4 FTEs.
Honest check: If your team can't explain the difference between session.timeout.ms and max.poll.interval.ms without looking it up, you're not ready to self-manage.
Decision Criteria
| Criterion | Favor Managed | Favor Self-Managed |
|---|---|---|
| Kafka expertise | No dedicated expertise | Multiple Kafka engineers |
| Time to production | This quarter | 12+ month runway |
| On-call capacity | Can't staff 24x7 | Strong on-call culture |
| Throughput | Variable | Stable, predictable, high |
| Compliance | Standard controls | Requires specific controls |
The Hybrid Path
Pure build or pure buy is rare. Most organizations combine:
Managed Kafka + Self-Managed Tooling: Confluent Cloud or MSK for brokers, your own governance layer on top.
Multi-Tier: Managed for dev and non-critical. Self-managed for production where you need maximum control.
Red Flags
Self-managed is failing when: Upgrades are perpetually deferred. Incidents escalate to the same 1-2 people. Developers avoid Kafka because "it's too hard."
Managed isn't delivering when: Monthly bill surprises. Support tickets take days. Can't implement required security controls.
The Real Question
The goal isn't to pick the cheapest option. It's to pick the option that lets your organization move fastest on what matters most.
Where should your engineering attention go? Infrastructure or product?
Book a demo to see how Conduktor provides governance and developer experience that managed services don't include—and self-managed teams don't have time to build.