IBM Acquires Confluent for $11B: What It Means for Kafka Teams

IBM just paid $11 billion for Confluent, a 30% premium over trading price and one of IBM's largest acquisitions ever, behind only the $34 billion Red Hat deal in 2019.

Kafka powers everyday business operations. It connects systems that cannot afford to fail. It drives AI initiatives that CIOs and CTOs are measured against. When that dependency sits at the center of your operational, analytical, and integration pipelines, any major vendor shift prompts a re-evaluation.

Confluent Is Not "The Kafka Company"

Apache Kafka is open source, maintained by the Apache Software Foundation. Confluent built the enterprise wrapper: managed cloud services, commercial support contracts, Schema Registry, Kafka Connect with hundreds of connectors, security features, and compliance certifications.

Many customers pay Confluent for risk mitigation, not technical superiority. "Kafka runs with fewer operational surprises when you have Confluent's support team on speed dial."

IBM's press release mentions building a "smart data platform for enterprise generative AI." Event-driven architectures do matter for AI/ML workloads (real-time feature stores, streaming inference, agent systems). But look at IBM's acquisition pattern:

• Red Hat ($34B, 2019): Enterprise Linux and Kubernetes
• HashiCorp ($6.4B, completed February 2025): Infrastructure-as-code tooling
• Confluent ($11B, announced December 2025): Event streaming platform

Each acquisition follows the same logic: buy market share and enterprise contracts. Fortune 100 companies already use these tools. IBM wants the recurring revenue, the account relationships, and the multi-year support contracts.

Kafka TCO Is Operational, Not Licensing

TCO in streaming is the compounding operational cost of onboarding applications, maintaining permissions, enforcing security, standardizing environments, and keeping clusters stable under dynamic workloads.

As adoption grows, pressure increases. More teams want to produce to Kafka, more services want to consume, and more AI-driven applications rely on consistent low-latency streams. More teams also means more schema evolution pressure, more ACL management, more consumer lag alerts to triage.

The IBM-Confluent deal will prompt many organizations to ask:

• How do we support more teams without adding headcount?
• How do we reduce friction in onboarding and governance?
• How do we avoid over-provisioning and runaway cluster complexity?
• How do we apply the same rules across MSK, Confluent Cloud, and on-prem?

Proxy Layers Decouple Applications from Clusters

A platform team spends months migrating to a new Kafka provider, only to discover their applications are tightly coupled to cluster-specific configurations. Client connection strings are hardcoded. Security policies vary by environment. Every migration requires touching application code.

A proxy layer solves this by sitting between client applications and Kafka clusters. Applications connect to one stable endpoint. The proxy handles routing, policy enforcement, and cluster abstraction behind the scenes. When infrastructure changes - whether that's a Confluent-to-MSK migration or a cluster upgrade - applications don't need to redeploy.

A large European airline migrated 25 on-prem Kafka clusters and 170 applications to Confluent Cloud over nine months using this pattern. Conduktor Gateway maintained centralized security and self-service access for 2,000+ developers while infrastructure changed underneath.

Post-acquisition, this matters more. Your architecture should evolve independently of any single vendor's roadmap.

Most Enterprises Run Multiple Kafka Providers

Multi-provider is the norm. It happens through organizational structure, acquisitions, regional requirements, or inherited systems.

A typical pattern: one business unit runs MSK because they're AWS-native, another inherited Confluent Cloud through an acquisition, and a third still runs on-prem Kafka that predates the cloud migration. Three Kafka environments, three sets of tooling, three security models.

The question isn't whether to standardize on one provider - that ship has sailed for most organizations. The question is how to operate consistently across them: unified access controls, consistent observability, predictable onboarding regardless of which cluster a team lands on.

Tools that abstract across providers - without creating new dependencies - become essential here.

Streaming Vendors Are Acquisition Targets

Snowflake was reportedly in talks to acquire Redpanda before that deal fell through. Redpanda is the most credible Kafka-compatible alternative: wire-compatible at the protocol level, written in C++ with different performance characteristics (particularly lower tail latencies at smaller scale).

The streaming layer is becoming essential infrastructure for modern data platforms. Every major player wants to own it: IBM succeeded with Confluent, Snowflake tried with Redpanda, and the hyperscalers are building their own (AWS MSK, Azure Event Hubs with Kafka protocol support).

When infrastructure becomes strategic, independent vendors become acquisition targets.

What this means for your planning:

• Redpanda is Kafka-compatible and viable today, but acquisition risk looms
• Apache Pulsar offers a different architecture (tiered storage, native multi-tenancy) with StreamNative as commercial backing
• Self-hosted Kafka remains an option, but you're trading vendor risk for operational burden - only viable if you have dedicated Kafka expertise
• AWS MSK is vanilla Kafka with no protocol lock-in, though you take on cloud dependency
• Azure Event Hubs offers a Kafka-compatible endpoint, but with some protocol gaps

There's no perfectly safe choice. Every alternative comes with its own consolidation risk or operational cost. This is the new reality of infrastructure decisions - and why decoupling your applications from any single vendor matters more than ever.

Expect Autonomy, Then Integration, Then Talent Exodus

If you've watched IBM acquisitions, the trajectory is predictable.

Year 1: The Honeymoon. Confluent will operate with relative autonomy. IBM will issue retention bonuses to key engineers and leadership. The product roadmap continues mostly unchanged. Sales teams get access to IBM's enterprise accounts. Everyone is optimistic.

Years 2–3: The Integration. HR, finance, and legal functions get absorbed into IBM. Reporting structures change. IBM's asset management systems, procurement processes, and security audits become mandatory.

The Talent Question. Once retention bonuses vest, departures typically follow. This happened at Red Hat. It happened at HashiCorp (compounded by the BSL licensing controversy). The engineers who built Confluent's technical advantage may migrate to hyperscaler streaming teams, to startups, or to companies building the next generation of streaming tools.

The Red Hat Counterpoint. Red Hat is still shipping RHEL six years later. The Ansible and OpenShift businesses are functional. IBM can let subsidiaries operate semi-independently. But Red Hat also hasn't driven the kind of developer-experience innovation it had before the acquisition. Maintenance mode isn't the same as death, but it's not growth either.

What to Do Depending on Your Current Setup

If you're currently on Confluent Cloud: Budget defensively for potential price increases over the next three years. More importantly, review your current contract terms now. Pre-close is often the best window to renegotiate pricing protections, SLA guarantees, and exit provisions - Confluent sales teams are motivated to lock in commitments before ownership transfers.

If you're evaluating Kafka for a new project: Run the right-sizing exercise honestly. Not every workload needs Kafka's complexity. But if you do need replay, high fan-out, and GB/s throughput, Kafka's architecture is designed for it.

If you're already running self-managed Kafka: You're in a strong position if you have dedicated Kafka expertise. You're not exposed to Confluent's pricing changes, and the open-source project will continue. But if your "Kafka team" is one SRE who also manages everything else, your operational risk may exceed vendor risk. Be honest about your operational capacity.

If you're considering migration to an alternative: Be realistic about the effort. Protocol compatibility is largely solved. But migrating Kafka Connect pipelines, Avro schemas (especially if you use Confluent's Schema Registry with schema linking), ACLs, and monitoring configurations is significant work. Unless you have strong signals that pricing will become untenable, the switching cost probably exceeds the risk.

The right move for most teams: acknowledge the risk and plan defensively, not migrate immediately.

IBM Is Betting on Your Inertia

IBM won't kill Kafka. The open-source project will continue. Confluent won't disappear overnight.

But the center of gravity shifts. Developer experience becomes secondary to enterprise sales. OSS community input matters less than IBM's product roadmap. Innovation slows as the focus turns to margin optimization.

IBM is betting that even if you're unhappy, the switching cost is too high. That even if pricing rises, you'll grumble and pay. For most teams, they're probably right.

But now is a good time to ask:

• Do we actually need Kafka, or did we adopt it without validating the requirements?
• If we do need it, what's our exposure to pricing changes?
• If we were starting fresh today, would we make the same choice?

The answers might not change anything. Or they might reveal that you've been over-provisioning complexity you don't need.

Either way, you'll know. And that's better than finding out when your CFO asks you why the Confluent bill tripled.

Whether you're staying on Confluent, evaluating alternatives, or running a multi-provider setup, the common thread is operational independence. That's what we built Conduktor for: giving teams control over Kafka without tying architecture to any single vendor's roadmap.

See how teams run Kafka across multiple providers →