Your Kafka Investment Is Hiding Costs and Opportunities
Your Kafka investment is hiding costs, unused capacity, and optimization opportunities that no dashboard can surface. AI changes what's possible to ask.
Your Kafka bill went up 40% last year. Your VP of Engineering says it's because adoption is growing. Your CFO asks if you're sure you need all of it.
Nobody can answer that question confidently. Not because the data doesn't exist, but because no one has the time or tooling to look across every cluster, every topic, and every team to figure out what's actually being used, what's over-provisioned, and what was created for a project that ended six months ago.
This is the state of Kafka at most organizations. The infrastructure works. The investment grows. And the visibility into whether that investment is right-sized sits in the heads of two or three people who are too busy keeping things running to do the analysis.
The core problem: Kafka's operational complexity means that the people qualified to assess your infrastructure spend are the same people too busy operating it. The audit never happens. AI-assisted operations through MCP (Model Context Protocol) changes what's possible to ask.
By the numbers: Organizations waste an average of 27% of cloud spend on unused or over-provisioned resources (Flexera, 2025). For streaming infrastructure, which is harder to audit than general compute, the waste tends to be higher. On a $1.5M annual Kafka bill, 27% is $400K+ in addressable waste.
Where the money hides
Kafka cost isn't a line item you can audit in a spreadsheet. It's distributed across clusters, topics, retention policies, partition counts, replication factors, and consumer patterns. The waste hides in the gaps between these layers.
| Pattern | What happens | Why it persists |
|---|---|---|
| Unused topics | Production topics receiving data with zero active consumers | Nobody told the producers to stop. Retention keeps storing. Conduktor customers regularly find that up to 70% of their topics are stale or abandoned once they actually look. |
| Over-provisioned environments | Staging clusters provisioned at production capacity, handling 5% of the traffic | Provisioned "just in case" and never revisited. Not a safety margin, just a line item. |
| Excess retention | 30-day retention where every consumer reads within minutes | You're paying to store 29 days of data that will never be read again. |
Dashboards answer questions. AI asks them.
The limitation of monitoring tools isn't accuracy. It's scope.
A dashboard answers the questions you thought to ask when you built it. It does that well. What it doesn't do is explore. It doesn't walk through your entire Kafka footprint and flag things you didn't know to look for. It doesn't compare clusters side by side. It doesn't follow a thread from an unused topic to the team that owns it to the retention policy that's costing you money.
Tools like Conduktor Insights close part of this gap by automating the scan: risk detection, cost analysis, governance coverage, topic health scores across every cluster. But scanning is one part of the toolkit. The next question is "why is this happening, who owns it, and what should we do about it?"
This is what changes when you connect AI to live infrastructure data through protocols like MCP (Model Context Protocol). MCP takes what automated scanning surfaces and lets you reason about it. Instead of building a dashboard for every question, you ask the question directly:
- Walk through my entire Kafka footprint and flag anything misconfigured, underutilized, or disproportionately expensive. What am I missing?
- Compare my production and staging clusters. Are they proportionally sized, or am I overpaying for environments that don't need the same capacity?
- Which topics contain PII, what are their retention and encryption policies, and who has consumer access? Format it for our quarterly audit.
- Generate an optimization report by team: what should each team fix first, and what's the estimated impact on cost, risk, and performance?
AI answers these in a single conversation, and goes further: it synthesizes findings into prioritized recommendations, attributes ownership, and projects impact. Not just "here's what's wrong" but "here's what to fix, in what order, and what it's worth."
What this delivers
1. Cost savings you can act on
AI turns known-but-unaddressed waste into a prioritized execution plan. It quantifies what each fix is worth, attributes ownership by team, and ranks optimizations by impact. You go from "we think we're overspending" to an action plan with estimated savings per team, in minutes instead of weeks.
One mid-market fintech ran their first MCP audit on a Tuesday afternoon. By Thursday: 40% of topics with zero active consumers, three staging environments at production scale handling single-digit traffic, excess retention across the board. Projected savings from the first round of fixes: $280K annually on a $1.1M Kafka bill.
2. Opportunity discovery
Cost savings is the defensive play: spend less. Opportunity discovery is the offensive one: do more with what you already have.
The same analysis that finds waste also finds untapped capacity. Clusters with headroom that could absorb new workloads without provisioning new infrastructure. Teams under-utilizing resources that another team is about to request. Consolidation opportunities across environments that would simplify operations and reduce overhead.
In the same fintech audit, two clusters earmarked for a new payments workload turned out to have 60% spare capacity on existing infrastructure. The team canceled a provisioning request and deployed to what they already had, avoiding $150K in new infrastructure spend and saving weeks of setup time.
3. Audit and compliance readiness
Every quarter, someone spends days compiling compliance evidence: which topics contain PII, what are the retention and encryption policies, who has consumer access. That's engineering time spent on evidence gathering instead of building.
When AI can query your live infrastructure directly, that evidence is available on demand. Regulatory exposure shrinks when compliance data is generated from live systems instead of assembled under deadline pressure from stale spreadsheets.
4. Risk visibility before major changes
Every infrastructure change carries risk. Migrations, upgrades, deprecations. The question leadership needs answered isn't "which systems are affected" but "what's the probability this change breaks something in production, and how big is the impact if it does?"
Today, answering that requires manually tracing dependencies across systems. Most of the time, that analysis is incomplete because it's too time-consuming to be thorough. Decisions get made on partial information, and teams discover the gaps in production.
AI traces the full dependency graph in one query, giving your teams the confidence to move faster on changes that used to stall in review for weeks.
Security: why this works without creating new risk
The reasonable concern with connecting AI to infrastructure is security. Your security team will ask: where does the data go? What new access does this create? Can we shut it off?
For how MCP works with Conduktor Console, the answer is straightforward:
| Concern | How it's addressed |
|---|---|
| Data residency | MCP runs inside your Console instance. Kafka metadata never leaves your infrastructure. |
| Access scope | AI sees exactly what the user already sees in Console. Personal Access Tokens inherit Console RBAC. No new permissions model. |
| Revocation | Revoke a single user's token or disable MCP entirely with one config change. Token validity checked on every request. |
| Scoped actions | AI capabilities are governed by the same RBAC policies that control Console access. No separate permissions layer. |
The key design decision: MCP is built into the existing Console security model, not alongside it. Your security team isn't evaluating a new access framework. They're evaluating a new interface to the framework they already approved.
Who benefits and how
For platform teams, AI handles the cross-cutting analysis that's important but never urgent: infrastructure audits, configuration drift detection, ownership mapping. The work that improves platform health over time but always loses priority to the next incident.
For developers, AI compresses the time to understand how systems connect. A new engineer can ask "how does the order fulfillment pipeline work, trace the data flow from the source through all downstream consumers" and get an accurate answer from live data, not stale documentation.
For engineering leaders, AI turns your Kafka investment from a black box into something you can actually interrogate. What are we spending? Where is the waste? Which teams are growing fastest? Where should we invest next?
The organizations that have clear answers to these questions make better infrastructure decisions. The ones that don't are guessing, and paying more for the privilege.
Where to start
MCP ships with Conduktor Console. Setup is a Personal Access Token and a config file. Your platform team can run a first infrastructure audit in an afternoon and have a prioritized optimization report to review by end of week.
See how MCP works → or book a demo to see it against a live environment.