Kafka Multi-Cloud: AWS, Azure, and GCP

Multi-cloud isn't a choice. It's a consequence of acquisitions, compliance, and vendor strategy.

Organizations rarely start multi-cloud intentionally. They start on AWS, acquire a company running Azure, face GDPR requirements demanding EU data stays in EU (Azure regions), or negotiate enterprise agreements including GCP credits. Suddenly, "we're an AWS shop" becomes "we run Kafka across three cloud providers."

The operational challenge isn't Kafka itself—Kafka runs the same everywhere. A unified control plane abstracts provider differences. The challenge is: different network topologies (VPCs, VNets, VPCs), different managed services (MSK, Event Hubs, Confluent Cloud), different monitoring tools (CloudWatch, Azure Monitor, Stackdriver), and different operational patterns.

Managing multi-cloud Kafka without unified tooling means: context-switching between cloud consoles, maintaining separate monitoring dashboards per provider, learning provider-specific quirks, and operationally treating clusters as unique snowflakes instead of commodity infrastructure.

Why Multi-Cloud Happens

Compliance and data residency drive multi-cloud adoption. GDPR requires EU customer data stays in EU, Chinese regulations require China data stays in China, Russian data localization laws require Russia data stays in Russia.

If your primary cloud provider lacks presence in required regions, you need secondary providers. AWS is strong in US/EU, less so in China. Azure has better China presence through 21Vianet partnership. Result: AWS for global infrastructure, Azure for China operations.

Disaster recovery across cloud providers provides resilience from provider-wide outages. If AWS us-east-1 experiences regional failure (has happened), failover to Azure or GCP keeps services running.

Single-cloud DR (AWS us-east-1 to AWS us-west-2) protects against regional failure but not provider-wide issues. Multi-cloud DR protects against both.

Acquisition integration brings existing cloud infrastructure. Company A runs Kafka on AWS, acquires Company B running on Azure. Immediate migration isn't feasible (takes months, high risk, no immediate business value). Result: operate both until consolidation happens (if ever).

Vendor negotiation leverage improves pricing. "We're considering AWS, Azure, and GCP" gets better pricing than "we're an AWS shop and can't move." Multi-cloud gives optionality and leverage.

Best-of-breed services combines provider strengths. AWS for compute/networking, GCP for machine learning, Azure for enterprise integration. Kafka spans providers, moving data between specialized services.

The Multi-Cloud Challenge

Operating Kafka across cloud providers amplifies operational complexity.

Network complexity differs by provider. AWS VPCs, Azure VNets, GCP VPCs have different capabilities, limits, and operational patterns. Cross-cloud networking (AWS to Azure) requires VPN, Direct Connect/ExpressRoute, or public internet (higher latency, security concerns).

Managed Kafka variations differ significantly:

• AWS MSK: Kafka-compatible, tight AWS integration, limited configuration control
• Azure Event Hubs: Kafka protocol compatible but not actual Kafka (different ops model)
• GCP managed Kafka: Limited availability, organizations often self-host on GCP
• Confluent Cloud: Multi-cloud abstraction layer, consistent operations, higher cost

Each has different: configuration options, monitoring integrations, upgrade processes, and pricing models. Operational knowledge doesn't transfer completely across providers.

Monitoring fragmentation means: CloudWatch for AWS clusters, Azure Monitor for Azure clusters, Stackdriver for GCP clusters. Three dashboards, three query languages, three alerting systems.

Answering "are all Kafka clusters healthy?" requires checking three separate systems. Cross-cloud correlation (are all providers experiencing issues simultaneously?) requires manual aggregation.

Identity and access management differs per provider. AWS IAM, Azure AD, GCP IAM have different models. Service-to-service authentication (AWS Lambda consuming from Azure Event Hubs) requires federation or shared credentials (security risk).

Unified Management Across Clouds

Centralized management abstracts provider differences.

Single control plane for all clusters regardless of provider. Create topics through same API whether cluster runs on AWS MSK, Azure self-hosted, or GCP Compute Engine.

Benefits: consistent operations (same commands everywhere), single audit trail (all changes logged centrally), unified RBAC (permissions apply across all providers).

Provider-agnostic APIs hide differences. Operators shouldn't need to know "this cluster is MSK, use AWS-specific commands" vs. "this cluster is self-hosted, use standard kafka-topics.sh."

Abstraction layer translates generic operations to provider-specific implementations automatically.

Unified monitoring aggregates metrics across providers. Single dashboard shows: all clusters health, total throughput (AWS + Azure + GCP), worst-performing cluster regardless of provider.

Drill-down shows provider-specific details (AWS MSK metrics, Azure VM metrics, GCP Compute metrics), but high-level view is consistent.

Cross-cloud correlation detects widespread issues. If all clusters experience elevated latency simultaneously, root cause is likely shared (application changes, schema updates) not provider-specific (network issue, cloud outage).

Without correlation, engineers investigate each provider separately, missing patterns visible across infrastructure.

Network and Latency Considerations

Multi-cloud Kafka means cross-cloud data transfer: producers in AWS, consumers in Azure.

Latency impact: Cross-cloud networking adds 20-100ms latency compared to intra-cloud (1-5ms). For latency-sensitive workloads (fraud detection, real-time recommendations), this might be unacceptable.

Solutions:

• Keep producers and consumers in same cloud (minimize cross-cloud traffic)
• Use regional hubs (AWS producers → AWS Kafka → Azure Kafka → Azure consumers)
• Accept latency tradeoff (multi-cloud resilience worth 50ms extra latency)

Network costs: Cloud providers charge egress fees for data leaving their network. Cross-cloud replication (AWS Kafka → Azure Kafka) incurs egress on AWS side, ingress usually free.

For high-throughput replication (TB/day), egress costs exceed compute costs. Budget accordingly or minimize cross-cloud data movement.

Network reliability: Cross-cloud networking is generally reliable but adds failure modes. VPN can fail, ExpressRoute can have issues, internet paths can degrade.

Monitoring should track: cross-cloud network latency, packet loss, replication lag across clouds. Increased lag might indicate network issues, not Kafka problems.

Cost Optimization Across Providers

Multi-cloud enables cost optimization through provider arbitrage.

Compute pricing varies by provider for similar instances. Compare equivalent VMs across AWS, Azure, GCP for Kafka brokers. Price differences of 20-30% are common for similar performance.

Organizations use: primary provider for most workloads, secondary provider for cost-sensitive batch processing, tertiary provider for development/testing (wherever is cheapest).

Committed use discounts (reserved instances, committed use contracts) vary by provider. AWS, Azure, GCP all offer discounts for long-term commitments (1-3 years) of 30-40% off on-demand pricing.

Negotiate discounts with multiple providers, use cheapest for new workloads.

Spot/preemptible instances reduce costs for fault-tolerant workloads. GCP preemptible VMs are often cheaper than AWS spot instances. For dev/test Kafka clusters (ephemeral, fault tolerance less critical), use cheapest spot instances across providers.

Storage tiering differs by provider. S3 glacier vs Azure Archive vs GCP Nearline have different pricing, retrieval times, and durability. For Kafka tiered storage (offloading old data from brokers), compare provider storage costs.

Disaster Recovery Across Clouds

Multi-cloud provides ultimate disaster recovery: complete provider failure doesn't cause downtime.

Cross-cloud replication mirrors topics from primary cloud to secondary. AWS Kafka replicates to Azure Kafka. If AWS region fails, failover to Azure.

Challenges:

• Replication lag: Cross-cloud replication is slower than intra-cloud (network latency). RPO (recovery point objective) might be minutes, not seconds.
• Consumer coordination: Consumers must reconfigure to secondary cloud after failover. Manual (update DNS, redeploy services) or automated (service mesh, load balancer).
• Cost: Replicating all data across clouds doubles storage and network costs. Replicate only critical topics to control costs.

Active-active across clouds runs producers and consumers in both clouds simultaneously. AWS Kafka and Azure Kafka both serve traffic. If one fails, the other handles full load.

Benefits: Zero RPO (no replication lag), automatic failover (services already connected to both).

Challenges: Consistency (ensuring same data in both clouds), complexity (bidirectional replication, conflict resolution), cost (running double infrastructure).

DR testing validates failover procedures. Quarterly tests ensure: replication lag is acceptable, consumer failover works, services recover within RTO (recovery time objective).

Untested DR plans fail during real disasters. Regular testing finds gaps before incidents.

Measuring Multi-Cloud Success

Track operational efficiency, cost, and availability.

Operational overhead per cloud measures: engineering hours spent on provider-specific operations. Target: under 10% of time on provider-specific tasks.

If 40% of time is learning provider quirks or maintaining provider-specific scripts, multi-cloud overhead is unsustainable. Automation and abstraction should eliminate most provider-specific work.

Cross-cloud latency measures: replication lag, network latency between clouds. Target: under 100ms p99 latency, under 5-second replication lag for critical topics.

High latency indicates network issues or insufficient bandwidth for cross-cloud traffic.

Cost efficiency compares: cost per message processed, cost per GB stored across providers. If one provider is 30% more expensive for equivalent workloads, consolidate or migrate to cheaper provider.

Availability across all clouds measures aggregate uptime. Target: 99.95% availability across all clusters.

Multi-cloud should increase availability (provider failure doesn't cause downtime), not decrease it (more complexity causes more incidents).

The Path Forward

Kafka multi-cloud management requires unified control plane (consistent operations across providers), centralized monitoring (single view of health), cross-cloud networking (low-latency, high-bandwidth connections), and cost optimization (provider arbitrage for efficiency).

Conduktor provides multi-cloud unified management, provider-agnostic APIs (via Terraform and CLI), centralized monitoring across AWS/Azure/GCP, and cross-cloud replication monitoring. Teams operate Kafka across multiple clouds without operational overhead scaling linearly with provider count.

If managing multi-cloud Kafka means maintaining separate tools per provider, the problem isn't cloud providers—it's lack of abstraction layer providing consistent operations.

Related: Vendor-Neutral Architecture → · Kafka Control Plane → · Kafka Cluster Management →