Sankalpsutra Tech
MICROSERVICES ARCHITECTURE

Plan service boundaries before distributed complexity spreads

We help teams define microservice boundaries, API contracts, deployment independence, messaging paths, and resilience patterns for SaaS and cloud-native platforms.

Service topology map

From client entry to operational visibility

1Client Apps
2API Gateway
3Domain Services
4Message Bus
5Data Stores
6Observability
BoundariesAPI contractsIndependent deployMessagingFault isolationObservability

WHY MICROSERVICES

Monolith coupling slows teams when scale and ownership grow

Microservices are not a default. They help when independent delivery, scaling, and fault isolation justify the operational cost.

Problem panel

  • Shared deployments block parallel team delivery
  • One module failure can stall the whole release
  • Scaling forces scaling the entire application
  • Database coupling hides service boundaries
  • Integration logic spreads across controllers and jobs

These issues often appear before teams are ready to operate distributed systems.

Boundary-first response path

Define contexts and contracts before splitting deployables.

  1. 1

    Map business capabilities

    Find natural ownership boundaries

  2. 2

    Define API contracts

    Stable interfaces between teams

  3. 3

    Extract services incrementally

    Phased migration, not big bang

  4. 4

    Add async integration

    Decouple long-running workflows

  5. 5

    Independent CI/CD paths

    Release without full-system risk

  6. 6

    Trace cross-service flows

    Ops visibility from day one

PATTERN MAP

Core patterns behind maintainable microservices

Each pattern reduces a specific distributed-system failure mode.

  • API gateway

    What it solves

    Single client entry with routing and cross-cutting controls

    Where it fits

    Mobile, web, and partner API surfaces

    Risk avoided

    Clients coupled to internal service topology

  • Bounded contexts

    What it solves

    Clear domain ownership before service splits

    Where it fits

    Modular monolith planning and team alignment

    Risk avoided

    Chatty services with blurred responsibilities

  • Database per service

    What it solves

    Data ownership aligned to service boundaries

    Where it fits

    Independent schema evolution per capability

    Risk avoided

    Hidden coupling through shared tables

  • Async integration

    What it solves

    Non-blocking workflows between services

    Where it fits

    Notifications, billing hooks, and side effects

    Risk avoided

    Synchronous chains that amplify latency

  • Saga orchestration

    What it solves

    Multi-step workflows with compensating actions

    Where it fits

    Orders, payments, and provisioning flows

    Risk avoided

    Partial failures leaving inconsistent state

  • Circuit breaker

    What it solves

    Contain downstream failures

    Where it fits

    External provider and internal dependency calls

    Risk avoided

    Cascading outages across services

  • Backend for frontend

    What it solves

    Tailored API aggregation per client type

    Where it fits

    Mobile vs admin vs partner experiences

    Risk avoided

    One generic API forced on all clients

  • Distributed tracing

    What it solves

    End-to-end request visibility

    Where it fits

    Debug, SLO tracking, and incident response

    Risk avoided

    Blind spots across service hops

TECHNOLOGY DECISIONS

Choosing the right runtime and integration backbone

Stack choices depend on team skills, cloud alignment, throughput, and operations maturity.

Kubernetes

Best fit
Multi-service platforms with ops maturity
Delivery model
Container orchestration
Operational complexity
Higher (cluster operations)
Retry / DLQ support
Via service mesh and worker patterns
Scale pattern
Horizontal pod autoscaling
When we recommend it
Production SaaS with multiple services and CI/CD pipelines

Azure Container Apps

Best fit
Azure-native services with managed scaling
Delivery model
Managed containers and revisions
Operational complexity
Lower (managed platform)
Retry / DLQ support
Durable Functions / Service Bus integration
Scale pattern
Revision-based and KEDA scaling
When we recommend it
Azure estates and .NET microservice platforms

AWS ECS / Fargate

Best fit
AWS-native container workloads
Delivery model
Task and service scheduling
Operational complexity
Moderate (managed tasks)
Retry / DLQ support
SQS workers and Step Functions
Scale pattern
Service autoscaling on CPU/RPS
When we recommend it
AWS-first products with containerized services

RabbitMQ

Best fit
Task queues and flexible routing
Delivery model
Broker-based messaging
Operational complexity
Moderate
Retry / DLQ support
DLX and TTL retry patterns
Scale pattern
Consumer scaling
When we recommend it
Workflow queues and integration-heavy services

Modular monolith first

Best fit
Early SaaS with growing team complexity
Delivery model
Single deploy with module boundaries
Operational complexity
Lower until split is justified
Retry / DLQ support
In-process or simple queue add-on
Scale pattern
Vertical then selective extraction
When we recommend it
MVP and Phase 2 before full service split

IMPLEMENTATION OWNERSHIP

Implementation layers we own for microservices platforms

Discovery-led delivery with milestone outputs confirmed after the architecture workshop.

Microservices delivery map

  1. Assess
  2. Boundaries
  3. Contracts
  4. Build
  5. Deploy
  6. Operate

  • Domain decomposition

    Capability map, bounded contexts, and phased extraction plan.

    • Context map
    • Ownership
    • Migration phases

  • API contract design

    OpenAPI specs, versioning rules, and consumer compatibility.

    • OpenAPI
    • Versioning
    • Breaking change policy

  • Service scaffolding

    Service templates, shared libraries, and health endpoints.

    • Templates
    • Health checks
    • Config patterns

  • Container orchestration

    Docker images, environments, and rollout sequencing.

    • Docker
    • CI/CD
    • Environment isolation

  • Integration patterns

    Sync APIs, async events, and adapter boundaries.

    • REST/gRPC
    • Events
    • Adapters

  • Production observability

    Tracing, metrics, alerts, and runbooks for distributed flows.

    • OpenTelemetry
    • SLOs
    • Runbooks

OUTCOMES

What microservices architecture delivers

When boundaries are justified, teams gain delivery speed and operational control.

  • Services scale independently

    Hot paths scale without dragging the entire platform.

    Outcome signal

    Targeted capacity growth

  • Teams ship in parallel

    Clear ownership reduces merge conflicts and release coupling.

    Outcome signal

    Faster milestone delivery

  • Failures stay contained

    Circuit breakers and boundaries limit blast radius.

    Outcome signal

    Lower cascade risk

  • Technology fits each capability

    Services can adopt the right stack where it matters.

    Outcome signal

    Pragmatic stack choices

  • Ownership maps to architecture

    On-call, roadmaps, and SLAs align to service boundaries.

    Outcome signal

    Clear team accountability

Related architecture

Splitting services without a boundary plan?

We can review your domain model, deployment model, integration paths, and ops readiness before you commit to a distributed architecture.

View related case study