When building a cloud-based integration layer, selecting the right technologies — aligned with business needs and investment capacity — is crucial to sustaining operations.
In one of the projects I worked on, each integration call cost around $0.25, accounting for all the components involved: licenses, infrastructure, services, and the technical team.
After reassessing the architecture, replacing some components, and renegotiating prices and contracts, we reduced the cost to $0.002 per call — generating significant savings and enabling business scalability. Later, with the support of a new cloud provider that sponsored the migration, the cost dropped even further — turning the initiative into a success case.
What Makes an Integration Architecture More Cost-Effective?
There are many options: API Gateways, Docker containers, and other technologies with different features, purposes, and pricing. Understanding these differences is key to choosing an efficient and financially viable architecture.
Here’s a practical overview based on real-world experience:
1. API Gateways — Wide Range of Types and Prices
API Gateways are the central point for exposing, protecting, and monitoring APIs. They vary based on:
Type of offering:
- Cloud-managed (SaaS/PaaS): AWS API Gateway, Apigee, Azure API Management
- Open source/self-hosted: Kong, Tyk, KrakenD
Features:
- Access control and rate limiting
- Payload transformation
- Monitoring and logging
- Support for GraphQL, WebSockets
- Plugins, authentication (JWT, OAuth2)
2. Docker and Containers — Total Flexibility
Using Docker allows you to package and scale applications, gateways, and integration services. However, it requires technical management.
Examples of services running in containers:
- API Gateways (Kong, Tyk, KrakenD)
- Messaging systems (Kafka, RabbitMQ, NATS)
- Cache (Redis, Memcached)
- Serverless functions (OpenFaaS, Knative)
- Lightweight backends (Flask, FastAPI, Express.js)
Costs involved:
- Infrastructure (CPU, RAM, bandwidth)
- Monitoring and security
- Backup, scalability, and operations
2.1. OpenShift — Enterprise Platform for Integration Orchestration
While Docker provides the packaging of containerized apps, OpenShift (Red Hat’s Kubernetes-based platform) delivers a full environment to orchestrate, scale, and govern those apps securely and automatically — ideal for enterprise contexts.
Where OpenShift fits in integration architecture:
You can host the following on OpenShift:
- Open-source API Gateways (Kong, Tyk, KrakenD)
- Messaging services (Kafka, RabbitMQ)
- Serverless functions (via Knative)
- Data pipelines (Airbyte, dbt, Apache NiFi)
- CDC and data streams (Debezium, Kafka Connect)
OpenShift can replace or complement iPaaS platforms (like Boomi or Mulesoft) for companies seeking flexibility, security, and cost control — with the advantage of being highly customizable.
Resources that support integration:
- CI/CD pipelines with Tekton
- Serverless with OpenShift + Knative
- Native observability (Prometheus, Grafana, Jaeger)
- Operators for automating Kafka, Camel, MongoDB, etc.
- Built-in enterprise security (AD, LDAP, RBAC)
3. How to Choose? Key Questions
✅ How many calls/month does your application perform? Helps forecast variable costs.
✅ Do you need advanced authentication? Some open-source gateways require extra setup.
✅ Does your team have infrastructure maturity? May justify using managed solutions.
✅ Do you need auto-scaling? Cloud-native may be the best fit.
✅ Are you on public cloud? Use native integrations (e.g., AWS + Lambda + API Gateway).
Other Common Cloud Integration Technologies
4. Messaging and Event-Driven Architecture
- AWS: SQS, SNS, EventBridge
- Azure: Service Bus, Event Grid
- Google Cloud: Pub/Sub
- Open source: Kafka, RabbitMQ, NATS
✅ Resilience, fault tolerance
⚠️ Management complexity
5. iPaaS (Integration Platform as a Service)
Low-code platforms with ready-made connectors:
MuleSoft, Boomi, Zapier, Workato, Make, Jitterbit
✅ Agility, governance
⚠️ Volume-based cost, vendor lock-in risk
6. Serverless Functions (FaaS)
AWS Lambda, Azure Functions, Google Cloud Functions
Alternatives: OpenFaaS, Knative, Kubeless
✅ Pay-per-use, scalability
⚠️ Cold start, execution limits
7. ETL/ELT and Data Pipelines
Managed: AWS Glue, Azure Data Factory, Google Dataflow
Open Source: Apache NiFi, Airbyte, dbt
✅ Structured data processing
⚠️ Compliance concerns (LGPD/GDPR)
8. B2B Gateways (EDI, B2B APIs)
Protocols: EDI, AS2, SFTP
Platforms: Axway, IBM Sterling
✅ Standardization
⚠️ Rigid and expensive
9. Orchestration and Workflows
Temporal.io, Camunda, Airflow, AWS Step Functions
✅ Visibility and control
⚠️ Modeling complexity, latency
10. Service Mesh and CDC
Service Mesh: Istio, Linkerd, App Mesh
CDC (Change Data Capture): Debezium, Kafka Connect, AWS DMS
✅ Observability, real-time updates
⚠️ Steep learning curve, dependency management
Bonus: Integration Efficiency Isn’t Just About Technology — Negotiation Is Also Strategy
Choosing the right technology is essential — but smart negotiation of contracts, licenses, and pricing models is often what separates a sustainable project from one that becomes financially unfeasible over time.
Key negotiation points in integration solutions:
- Volume-based vs. usage-based licensing → Understand if your business model scales by transaction, user, or payload — and negotiate accordingly.
- Minimum packages and price adjustment clauses → Watch out for contracts with enforced minimum usage or automatic price hikes.
- Support and professional services → Some platforms charge heavily for support. Sometimes it’s better to train an internal team.
- Sponsored migration or promotional credits → As in the case shared at the start, some cloud providers offer financial or technical support for migration — significantly reducing initial costs.
- Vendor lock-in and exit costs → Carefully evaluate your dependency on certain platforms, especially proprietary iPaaS tools.
Conclusion
Choosing the right integration technology is not just a technical decision — it’s a strategic and financial one. Poorly designed architectures can be costly, as the opening case shows. But with a critical eye, volume analysis, technical maturity, and cost control, it’s possible to turn a bottleneck into a lever for scale and efficiency.