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Managed streaming

Kafka as an operated platform

Assistance runs Apache Kafka for teams that need durable event streams without owning broker operations, capacity planning, upgrades, monitoring, and incident response alone.

Multi-broker clusters. Topic and retention governance. 24/7 critical response available for covered production platforms.

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Streaming platform operations

We operate brokers, storage, replication, monitoring, upgrades, and capacity planning inside an agreed service boundary.

Governed topics and retention

Topic creation, partitioning, retention, compaction, ACLs, and schemas are managed through clear operating rules.

Production integration support

We help teams onboard producers, consumers, connectors, and CDC pipelines without turning Kafka into an undocumented dependency.

Service playbook

From problem to operating evidence

Main content is structured like a case study: context first, scoped work next, then the operating changes and evidence a team can use after handoff.

Service briefBest-fit use casesWhat Assistance operatesOwnership boundaryDeployment options

Managed Kafka is for teams building event-driven services, integration pipelines, CDC flows, analytics ingestion, or replayable event logs. Assistance operates the Kafka platform while your teams own event contracts, producers, consumers, and business semantics.

Case-study lens

Scoped

Problem, responsibility, and handoff boundaries before implementation.

Evidence

Dashboards, runbooks, reviews, and operating records over borrowed logos.

Outcomes

Conservative summaries focused on observable operational improvement.

EvidenceSection 01

Best-fit use cases

Runbooks, dashboards, reviews, and handoff material make the work auditable.

Use caseWhy Kafka fits
Event-driven microservicesDurable topics decouple producers and consumers while preserving event history
Change data captureStream database changes into analytics, search, caches, or downstream systems
Integration busStandardize movement of events between internal services and external systems
Real-time analyticsFeed clickstream, activity, telemetry, or operational data into processing systems
Log and audit pipelinesRetain ordered, replayable event records for downstream analysis
Operating modelSection 02

What Assistance operates

Responsibilities, response paths, and technical changes are made explicit before work starts.

AreaIncluded managed service responsibility
ProvisioningCluster sizing, broker setup, storage configuration, network placement, secure defaults, and bootstrap details
ReliabilityReplication settings, broker health, controller health, backup/retention strategy where applicable, and runbooks
CapacityPartition, storage, throughput, consumer lag, and broker utilization monitoring
MaintenanceKafka version lifecycle guidance, patch planning, maintenance windows, rolling upgrades, and rollback planning
SecurityTLS, SASL, ACL model, service accounts, credential rotation support, and audit-friendly access practices
GovernanceTopic naming, retention defaults, partition guidance, schema registry practices, and onboarding workflow
SupportSeverity-based platform support and escalation for covered production clusters

Assistance operates Kafka. Your teams own event contracts, producer behavior, consumer correctness, idempotency, schema evolution decisions, and downstream business processing. We can facilitate governance, but event semantics remain application ownership unless scoped separately.

OutcomeSection 03

Ownership boundary

Expected changes are framed as practical operating improvements, not unsupported guarantees.

ResponsibilityAssistance ownsCustomer owns
Kafka runtimeBroker operations, upgrades, monitoring, capacity, and platform incident triageProducer and consumer application behavior
TopicsGuardrails, creation workflow, partition/retention recommendationsTopic purpose, event ownership, business retention requirements
SchemasRegistry operation and compatibility policy setup where includedSchema design, evolution approval, producer/consumer compatibility
ConnectorsPlatform operation when Kafka Connect is includedSource/sink credentials, data mapping, connector business behavior
IncidentsBroker/platform failures and service statusBad events, poison messages, consumer bugs, duplicate handling
EvidenceSection 04

Deployment options

Runbooks, dashboards, reviews, and handoff material make the work auditable.

OptionWhen to choose it
Assistance physical serversDevelopment, integration testing, lower-cost internal event platforms, and CI environments
Customer cloud accountProduction platforms that must live near cloud-native applications and data services
Cloud-managed Kafka operationsAssistance operates MSK, Confluent, Azure Event Hubs Kafka API, or similar services where preferred
HybridDevelopment Kafka on Assistance infrastructure with production Kafka in cloud
Operating modelSection 05

Reliability and support model

Responsibilities, response paths, and technical changes are made explicit before work starts.

TopicManaged Kafka approach
AvailabilityMulti-broker design and target availability scoped by topology, provider, and support tier
DurabilityReplication factor, min in-sync replicas, retention, and compaction policies designed around data criticality
RecoveryRecovery expectations documented for broker loss, topic misconfiguration, and retention-related scenarios
PerformanceThroughput, latency, partitions, storage, and consumer lag monitored continuously for covered services
ResponseP1 response targets scoped in support agreement; 24/7 critical response available for covered production clusters
OutcomeSection 06

Onboarding

Expected changes are framed as practical operating improvements, not unsupported guarantees.

Assessment step

1. Streaming assessment

We review event sources, consumers, throughput, retention, ordering needs, data sensitivity, replay requirements, expected growth, and integration targets.

Operating step

2. Platform design

Assistance proposes broker count, storage, replication, networking, ACLs, topic standards, schema registry approach, monitoring, and support model.

What changes

3. Producer and consumer onboarding

We document connection details, topic request workflow, ACLs, schema rules, consumer lag dashboards, and runbook expectations for new services.

What changes

4. Operate and govern

After go-live, we monitor broker health, lag, storage, throughput, and topic growth. Governance rules prevent unbounded retention, partition sprawl, and undocumented data ownership.

ScopeSection 07

Supported capabilities

The work is broken into visible capabilities, acceptance points, and handoff artifacts.

  • Apache Kafka broker clusters and KRaft/ZooKeeper lifecycle planning depending on version and environment
  • Topic and partition governance
  • Schema Registry with Avro, JSON Schema, or Protobuf where included
  • Kafka Connect operations for scoped source/sink connectors
  • Mirror or replication patterns for migration and disaster recovery where appropriate
  • Metrics and alerting for brokers, topics, partitions, and consumers
ScopeSection 08

Not included by default

The work is broken into visible capabilities, acceptance points, and handoff artifacts.

  • Designing every event contract or business data model
  • Rewriting producers or consumers for idempotency and compatibility
  • Guaranteeing delivery semantics for application code outside Kafka
  • Unlimited retention, topics, partitions, connectors, or throughput outside the plan
  • Owning downstream data correctness after consumers process events
Next stepSection 09

Related products

Decision points and common questions are made explicit so follow-up work is scoped cleanly.

Next stepSection 10

Getting started

Decision points and common questions are made explicit so follow-up work is scoped cleanly.

Request a Kafka assessment. We will map producers, consumers, topics, retention, ownership, and support requirements before proposing a managed streaming platform. Request Kafka assessment →

Next stepSection 11

Frequently asked questions

Decision points and common questions are made explicit so follow-up work is scoped cleanly.

Is Kafka the right choice for simple background jobs? Not always. Redis queues, a database-backed queue, or a managed cloud queue can be simpler. Kafka is best when you need durable replayable streams, multiple consumers, and event history.

Who creates topics? We define a topic request workflow. Assistance can create topics and enforce defaults, while your team identifies ownership, purpose, retention, schema, and consumers.

Can you operate MSK or Confluent instead of self-hosted Kafka? Yes. We can operate cloud-managed Kafka services in your account or tenancy when that is the better fit.

How do you handle schema changes? Schema governance is part of onboarding when Schema Registry is included. Your teams own schema design and compatibility decisions; Assistance operates the registry and policy mechanism.

What SLA applies to Kafka? Availability and response targets are scoped by cluster topology, provider dependencies, and support tier. We define these before production onboarding.

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Pricing

Flexible scopes available. if you need custom terms or bundled service pricing.

Small

€800€/month

Development or low-throughput streaming workloads.

  • 3-broker cluster
  • Schema Registry
  • Monitoring & alerting
  • SSL/TLS encryption
  • Multi-region
Most popular

Medium

€1.600€/month

Production streaming with enhanced throughput.

  • Enhanced broker cluster
  • Schema Registry + Connect
  • Full monitoring stack
  • SSL/TLS + SASL auth
  • Multi-region

Enterprise

Custom

High-throughput, multi-region Kafka deployments.

  • Custom cluster sizing
  • Full Kafka ecosystem
  • Advanced monitoring & alerting
  • Enterprise security
  • Multi-region replication

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Estimated monthly total

0 €/mo

Does not include server infrastructure costs (compute, storage, egress).

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