Microservices Architecture
From monolith migration to greenfield microservices, we design and build distributed architectures that scale independently, deploy safely, and recover gracefully. Kubernetes-native, event-driven, and production-hardened.
Microservices
Distributed Architecture
Architecture Benefits
The Scaling Challenge
Monolithic architectures work great—until they don't. As your product grows and teams scale, the tight coupling that once made development fast becomes the bottleneck that slows everything down.
Your monolithic application can't scale specific components independently. High-traffic features force you to scale everything, wasting resources and increasing costs.
Stuck with aging frameworks because your entire codebase depends on them. Modernizing means rewriting everything—a massive, risky undertaking.
Every change requires full application deployment. Small updates become risky operations. Teams step on each other's toes, slowing everyone down.
One component failure brings down the entire system. A bug in the billing module shouldn't crash your user dashboard, but it does.
We decompose your system into well-defined services using Domain-Driven Design. Each service owns its data and can scale, deploy, and fail independently.
Synchronous APIs for real-time operations, asynchronous messaging for everything else. Circuit breakers and retries ensure resilience across service boundaries.
Kubernetes-native deployment with auto-scaling, self-healing, and rolling updates. Deploy dozens of times a day without coordinated releases.
Distributed tracing, centralized logging, and service mesh telemetry. Find issues in minutes instead of days, even across complex service graphs.
Our Services
From greenfield microservices architectures to complex monolith migrations, we provide comprehensive services to help you build, deploy, and operate distributed systems at scale.
Design distributed systems that actually work. We use Domain-Driven Design to identify service boundaries, define clean APIs, and create architectures that enable independent team autonomy while maintaining system coherence.
Safely decompose monolithic applications into microservices without disrupting your business. We use the strangler fig pattern to migrate incrementally, reducing risk and delivering value continuously.
Implement service-to-service communication that's reliable, observable, and secure. We deploy service meshes that handle load balancing, encryption, and traffic management transparently.
Build production-ready Kubernetes infrastructure that scales. From cluster setup to advanced operators, we create container platforms that enable rapid, reliable deployments.
Design systems that gracefully handle failure. We implement circuit breakers, bulkheads, retries, and timeouts that keep your system running even when components fail.
Gain visibility into distributed systems. We implement comprehensive observability with distributed tracing, structured logging, and real-time metrics that help you understand system behavior.
Not sure if microservices are right for you? Let's assess your architecture.
Schedule an Architecture ReviewOur Process
Microservices migrations are complex. Our proven process reduces risk, delivers value incrementally, and ensures your team can operate the system independently.
We analyze your current system to understand pain points, identify service boundaries, and determine if microservices are the right solution. Not every system needs microservices—we'll tell you honestly.
Key Activities
Deliverables
Architecture assessment report, migration vs. optimization recommendations
Using Domain-Driven Design, we map your business domains to identify bounded contexts and service boundaries. This ensures services align with business capabilities, not arbitrary technical divisions.
Key Activities
Deliverables
Domain model, context maps, service decomposition strategy
We design the target microservices architecture: service interactions, data ownership, communication patterns, and infrastructure. Every decision is documented with clear rationale.
Key Activities
Deliverables
Technical design docs, API contracts, ADRs (Architecture Decision Records)
We build the foundational platform: Kubernetes infrastructure, CI/CD pipelines, service mesh, and observability stack. This enables rapid service development and deployment.
Key Activities
Deliverables
Production-ready platform, deployment templates, runbooks
We migrate or build services incrementally using the strangler fig pattern. Each service is extracted, tested, and deployed independently, reducing risk and delivering value continuously.
Key Activities
Deliverables
Working services, migration metrics, rollback procedures
Distributed systems require rigorous testing. We implement contract testing, integration testing, chaos engineering, and performance testing to ensure reliability at scale.
Key Activities
Deliverables
Test suites, chaos experiment results, performance baselines
We ensure operational excellence with comprehensive monitoring, incident response procedures, and continuous optimization. Your team is trained to operate and evolve the system independently.
Key Activities
Deliverables
Production systems, operations documentation, trained team
Our microservices delivery process is powered by SPARK™—our delivery framework that brings predictability, quality gates, and clear communication to complex architectural transformations.
Technology Stack
We leverage industry-leading tools and frameworks for building resilient, scalable microservices architectures. Our stack is battle-tested across high-traffic production environments.
Production-grade container platforms and orchestration
Service-to-service communication and traffic management
Service APIs and inter-service messaging
Microservice-friendly backend frameworks
Monitoring, logging, and distributed tracing
Distributed databases and data patterns
Platform-agnostic approach: We work with your existing infrastructure and cloud provider. Whether you're on AWS, Azure, GCP, or on-premises, we design solutions that fit your environment.
Why Microservices
When implemented correctly, microservices unlock organizational and technical benefits that monoliths can't match. Here's what you gain.
Scale only the services that need it. Handle 10x traffic spikes in your API gateway without scaling your entire system.
10x
Scale capacity
Deploy services independently, dozens of times per day. No more coordinating releases or waiting for the entire monolith to pass tests.
50x
More deployments
A bug in one service doesn't crash your entire system. Circuit breakers and bulkheads keep failures contained.
99.9%
Uptime target
Teams own services end-to-end. No more stepping on each other's code. Ship features without cross-team coordination.
100%
Team ownership
Choose the right technology for each service. Use Go for performance-critical services, Python for ML workloads, Node.js for APIs.
Any
Tech stack per service
Pay only for the resources each service needs. Right-size containers and auto-scale based on actual demand.
40%
Infrastructure savings
Distributed tracing shows exactly how requests flow through your system. Find bottlenecks and debug issues in minutes.
< 5min
Issue detection
Services map to business capabilities. Technical architecture reflects organizational structure, enabling Conway's Law to work for you.
1:1
Team-service mapping
Use Cases
Microservices aren't right for every situation. Here are the scenarios where microservices architecture delivers the most value.
Scaling Beyond the Monolith
Your startup is growing fast and your monolith is showing cracks. Features take longer to ship, deployments are risky, and scaling is expensive. It's time to evolve your architecture.
Common Scenarios
Breaking Down Monolithic Systems
Your enterprise system has grown complex over years. Multiple teams working in the same codebase, long release cycles, and high change failure rates. Microservices can restore agility.
Common Scenarios
From Monolith to Modern Architecture
Your legacy system works but is holding you back—difficult to maintain, impossible to hire for, and too risky to change. We help you migrate to microservices without business disruption.
Common Scenarios
Multi-Region & Multi-Tenant Systems
Building platforms that serve global users with local data residency, multi-tenant isolation, and geographic distribution. Microservices enable the flexibility these systems demand.
Common Scenarios
Engagement Models
Whether you need a quick assessment, a pilot project, or a long-term partnership — we have an engagement model that fits your needs.
We analyze your codebase, processes, and team dynamics to identify bottlenecks and opportunities. You get a clear roadmap — no commitment required.
Ideal for: Teams wanting an objective assessment before committing
Learn moreStart with a focused pilot project. A small Pod works alongside your team on a real deliverable, so you can evaluate fit and capabilities with minimal risk.
Ideal for: Teams wanting to test the waters before scaling
Learn moreDedicated cross-functional teams that integrate with your organization. Full accountability for delivery with built-in QA, architecture reviews, and the SPARK™ framework.
Ideal for: Teams ready to scale with a trusted partner
Learn moreNeed specific skills? Augment your team with vetted engineers who work under your direction. React, Node, Python, AI engineers, and more.
Ideal for: Teams with clear requirements and strong internal leadership
Learn moreLet's talk about your goals, team structure, and timeline. We'll recommend the best way to start — with no pressure to commit.
Schedule a Free ConsultationMicroservices architecture is an approach to building software applications as a collection of small, independent services that communicate over well-defined APIs. Each service is self-contained, owns its data, and can be developed, deployed, and scaled independently of other services.
The term "microservices" emerged around 2011-2012 from practitioners at companies like Netflix, Amazon, and Spotify who were dealing with the scaling challenges of large monolithic applications. These companies found that breaking their systems into smaller, loosely coupled services enabled faster development, better scalability, and improved fault isolation.
A microservices architecture typically exhibits these characteristics: services are organized around business capabilities (not technical layers); each service is independently deployable; services own their data and don't share databases; communication happens through lightweight protocols; and teams have autonomous ownership of services end-to-end.
At Salt, we help organizations design and implement microservices architectures that actually work in production. We combine deep expertise in Domain-Driven Design with practical experience operating distributed systems at scale.
Understanding the trade-offs between microservices and monolithic architecture is essential for making the right architectural decisions.
In a monolithic architecture, all application functionality lives in a single codebase and deploys as a single unit. This approach has significant advantages: simplicity (one codebase to understand), easy local development (everything runs together), simpler testing (no distributed systems to mock), and straightforward deployment (one artifact to deploy).
Monoliths work well for smaller teams and early-stage products. They let you move fast when you're still figuring out your domain. Many successful products run on well-structured monoliths for years.
Monoliths hit limits as teams and systems grow. Deployment becomes risky because every change affects the entire system. Different parts of the application can't scale independently. Teams step on each other's code, creating coordination overhead. Technology choices are constrained because everything must use the same stack.
Microservices solve these scaling challenges but introduce distributed systems complexity. You gain independent deployment, selective scaling, technology flexibility, and team autonomy. But you pay with operational complexity, network latency, distributed debugging challenges, and eventual consistency.
The decision isn't binary. Many organizations successfully run hybrid architectures—a core monolith for stable functionality with microservices for components that need independent scaling or different technology stacks.
Microservices aren't always the right choice. Here are the signals that suggest microservices might be valuable:
Multiple teams need to work on the same system without blocking each other. Different teams have different deployment cadences. You need to scale engineering capacity significantly. Teams want autonomy over their technology choices.
Parts of your system have significantly different scaling requirements. You need to adopt different technologies for different components. Fault isolation is critical—one component's failure shouldn't cascade. Your deployment pipeline has become a coordination bottleneck.
Small team (under 10-15 engineers). Early-stage product still discovering its domain. Simple scaling requirements that a monolith can handle. Limited operational expertise for distributed systems. Strong need for development speed over operational flexibility.
Many microservices experts, including Martin Fowler, recommend starting with a well-structured monolith and extracting services when needed. This approach lets you discover your domain before committing to service boundaries. Prematurely defined service boundaries are expensive to change.
Successful microservices architectures rely on proven patterns for service design, communication, and data management:
Domain-Driven Design provides the conceptual framework for defining service boundaries. Bounded contexts identify areas of the domain with their own models and language. Aggregates define consistency boundaries within services. Context mapping shows how services relate and integrate.
An API gateway provides a single entry point for external clients, handling cross-cutting concerns like authentication, rate limiting, and request routing. It can also aggregate calls to multiple services, reducing client-side complexity.
A service mesh (like Istio or Linkerd) handles service-to-service communication transparently, providing load balancing, encryption, observability, and traffic management without changing application code. It's especially valuable at scale.
Services communicate through events for operations that don't need immediate responses. This enables loose coupling, temporal decoupling, and better resilience. Event sourcing and CQRS patterns extend this for complex domains.
Distributed transactions across services use the saga pattern—a sequence of local transactions where each step publishes events triggering the next. If a step fails, compensating transactions undo previous changes. This maintains consistency without distributed locks.
Circuit breakers prevent cascade failures by stopping requests to failing services. When failures exceed a threshold, the circuit "opens" and requests fail fast instead of waiting for timeouts. This protects both the failing service and its callers.
Migrating from a monolith to microservices requires a careful, incremental approach:
Named after fig trees that gradually envelope their host, this pattern builds new microservices alongside the existing monolith. Traffic is gradually routed to new services until the old code can be removed. This approach minimizes risk and delivers value continuously.
Begin with services that are: well-bounded (clear domain boundaries); less critical (lower risk while learning); high-change (frequent deployments benefit most from independence); or performance-critical (needing independent scaling). Building capability with easier extractions before tackling core functionality.
Data is often the hardest part. Strategies include: database-per-service (complete separation); shared database with schema partitioning (intermediate step); and event-driven data synchronization (eventual consistency). The right approach depends on consistency requirements and coupling tolerance.
An anti-corruption layer translates between the old monolith and new microservices, preventing legacy concepts from polluting new designs. This enables clean service designs while maintaining integration with existing systems.
Microservices architecture involves technology decisions across multiple layers:
Synchronous communication (REST, GraphQL, gRPC) for operations needing immediate responses. Asynchronous messaging (Kafka, RabbitMQ, NATS) for event-driven communication and background processing. Most systems use both, choosing based on latency requirements and coupling tolerance.
Kubernetes has become the standard for running microservices at scale, providing deployment automation, scaling, self-healing, and service discovery. Managed Kubernetes services (EKS, GKE, AKS) reduce operational burden for teams without deep Kubernetes expertise.
Distributed systems require comprehensive observability: metrics (Prometheus, Datadog) for system health; logs (ELK, Loki) for debugging; traces (Jaeger, Zipkin) for following requests across services. OpenTelemetry provides vendor-neutral instrumentation.
One benefit of microservices is polyglot development. Go and Rust for performance-critical services; Python for ML workloads; Node.js and Java for business logic APIs. The constraint is operational—more languages mean more expertise needed. Most teams standardize on 2-3 languages.
Operating microservices requires capabilities beyond running a monolith:
With many services deploying independently, manual deployment becomes impossible. CI/CD pipelines must handle automated testing, container building, and deployment to multiple environments. GitOps approaches (ArgoCD, Flux) enable declarative, auditable deployments.
Testing distributed systems requires layered approaches: unit tests for service logic; integration tests for service boundaries; contract tests (Pact) for API compatibility; end-to-end tests for critical paths; and chaos engineering for resilience verification.
Security operates at multiple layers: API gateway for authentication and rate limiting; service mesh for mTLS encryption; service-level authorization based on identity; network policies to restrict communication; and secrets management (Vault, cloud-native solutions).
Microservices work best with team structures that match service boundaries (Conway's Law). Cross-functional teams own services end-to-end: development, testing, deployment, and operations. Platform teams provide shared infrastructure and tooling.
Salt brings a differentiated approach to microservices architecture and migration. Here's what sets us apart:
Production Experience: Our teams have built and operated microservices architectures handling millions of requests. We know what works in production, not just in theory.
Domain-Driven Design Expertise: We use DDD to identify service boundaries that align with business capabilities. This ensures services make sense to stakeholders, not just engineers.
Incremental Migration: We use the strangler fig pattern to migrate systems without disruption. You see value early and can stop or pivot at any point. No big-bang rewrites.
Full-Stack Capability: From Kubernetes infrastructure to service mesh configuration to application development, our managed pods handle the complete stack. No gaps between teams.
SPARK™ Framework: Our SPARK™ delivery framework brings structure to complex migrations. Clear phases, quality gates, and success metrics ensure predictable delivery.
Knowledge Transfer: We don't just build—we teach. Every engagement includes comprehensive documentation and training so your team can operate and evolve the system independently.
Honest Assessment: Microservices aren't always the answer. We'll tell you if a well-structured monolith or modular monolith is a better fit for your situation.
Ready to modernize your architecture? Schedule an architecture review with our team to discuss whether microservices are right for you and how we can help.
Industries
We've built software for companies across industries. Our teams understand your domain's unique challenges, compliance requirements, and success metrics.
HIPAA-compliant digital health solutions. Patient portals, telehealth platforms, and healthcare data systems built right.
Scale your product fast without compromising on code quality. We help SaaS companies ship features quickly and build for growth.
Build secure, compliant financial software. From payment systems to trading platforms, we understand fintech complexity.
Platforms that convert and scale. Custom storefronts, inventory systems, and omnichannel experiences that drive revenue.
Optimize operations end-to-end. Route optimization, warehouse management, and real-time tracking systems.
Hire dedicated developers to extend your team
Whether you need to build a new product, modernize a legacy system, or add AI capabilities, our managed pods are ready to ship value from day one.
100+
Engineering Experts
800+
Projects Delivered
14+
Years in Business
4.9★
Clutch Rating
FAQs
Common questions about microservices architecture, migration strategies, and how we help organizations build and operate distributed systems.
Microservices are an architectural style where an application is composed of small, independent services that communicate over well-defined APIs. Unlike monolithic architecture where all functionality lives in a single codebase and deploys as one unit, microservices can be developed, deployed, and scaled independently. Each service owns its data and can use different technologies. This enables faster development, better fault isolation, and more flexible scaling—but adds complexity in distributed systems management.
Consider microservices when: your teams are stepping on each other's code and deployments are becoming coordination nightmares; you need to scale specific components independently; different parts of your system have different technology requirements; or your organization has grown to multiple teams that need autonomous ownership. However, microservices aren't always the answer. A well-structured monolith is often better for smaller teams and early-stage products. We help assess whether microservices are right for your situation.
A complete migration depends on system complexity, but it's measured in months to years, not weeks. The key is not to approach it as a big-bang rewrite. We use the strangler fig pattern to migrate incrementally—extracting one service at a time while the monolith continues running. This reduces risk and delivers value continuously. A typical service extraction takes 4-8 weeks. We usually start with less critical services to build team capability before tackling core functionality.
The strangler fig pattern is an incremental migration approach named after fig trees that gradually envelope and replace their host trees. In software, it means building new microservices alongside your existing monolith, gradually routing traffic to new services, and eventually decommissioning the old code. This approach lets you migrate without downtime, learn from each extraction, and stop or pivot at any point. It's much safer than a complete rewrite.
Microservices communicate through two main patterns: synchronous (REST APIs, GraphQL, gRPC) for request-response operations, and asynchronous (message queues, event streaming) for operations that don't need immediate responses. We typically use REST or gRPC for queries and commands that need immediate results, and Kafka or RabbitMQ for events and background processing. The right choice depends on your latency requirements, coupling tolerance, and operational complexity.
A service mesh (like Istio or Linkerd) is infrastructure that handles service-to-service communication, providing features like load balancing, encryption, observability, and traffic management without changing application code. You need a service mesh when: you have many services with complex communication patterns; you need consistent security policies across services; or you want advanced deployment patterns like canary releases. For smaller deployments, a service mesh may add unnecessary complexity.
Each microservice should own its data—no sharing databases between services. This enables independent deployment and scaling but creates challenges for data consistency. We use patterns like event sourcing for maintaining consistency across services, CQRS for separating read and write models, and saga patterns for distributed transactions. Data partitioning strategies are defined during architecture design based on your consistency, availability, and performance requirements.
Testing microservices requires a multi-layered approach: unit tests for individual service logic; integration tests for service boundaries; contract tests (using tools like Pact) to verify API compatibility between services; and end-to-end tests for critical user journeys. We also use chaos engineering to verify resilience—intentionally breaking things to ensure the system handles failures gracefully. The test strategy balances confidence with execution speed.
Distributed systems require comprehensive observability. We implement the three pillars: metrics (Prometheus/Grafana) for system health and performance; logs (ELK/Loki) for detailed debugging; and traces (Jaeger/Zipkin) for following requests across services. Correlation IDs link all three, enabling you to trace a user request from entry to completion. We also set up alerts for anomalies and SLO violations. Without this observability stack, debugging distributed systems is extremely difficult.
Security in microservices operates at multiple layers: API gateway handles authentication and rate limiting; service mesh provides mTLS encryption for all internal traffic; each service authorizes requests based on JWT claims or service identity; secrets are managed through tools like Vault or cloud-native secret managers. We also implement network policies to restrict service-to-service communication and scan containers for vulnerabilities. Defense in depth is essential.
Operating microservices requires different skills than operating monoliths. Teams need experience with containers, Kubernetes, distributed systems debugging, and observability tools. We provide comprehensive training and documentation as part of every engagement. We also start with smaller, less critical services to build team capability before extracting core functionality. Our goal is always to leave your team fully capable of operating and evolving the system independently.
Microservices typically have higher infrastructure and operational costs than monoliths—more services mean more containers, more networking, more monitoring. However, they can reduce total cost of ownership through: efficient scaling (only scale what needs it); faster development (independent deployments); and reduced outage impact (fault isolation). The ROI depends on your scale, team structure, and growth trajectory. We help model the costs and benefits for your specific situation.