What microservice architecture is and how it helps businesses scale faster, reduce outages, and speed up releases

24.10.2025 Microservices reduce downtime from about 4 hours to 30-50 minutes by isolating services and containing failures.

Independent teams plus CI/CD speed up time-to-market and increase release frequency.

Scaling in parts allocates resources only to hot domains and saves infrastructure during peaks. API-first, queues/events, and a flexible domain-specific stack improve resilience and technical flexibility without rewriting the core.

Unified observability (tracing, metrics, alerts, SLOs) makes incidents more predictable and reduces support costs.

During this time, support load grows, customers are lost, and revenue declines.

Building a microservices architecture reduces downtime to 30-50 minutes per incident - the business can react to the market quickly and scale.

The move to microservices is a process that requires a step-by-step approach:

Audit the current architecture and business requirements.

Identify the system bottlenecks, load, component interaction rules, and business features that need growth or change.

Define the target architecture and container services.

Split the system into services:

• define boundaries
• define ownership
• choose a technology
• define interaction - API
• message queue

If boundaries are not defined, services end up implementing overlapping functions, coupling between components increases, and autonomy is lost.

Plan deployment and set up automation, monitoring, and logging. Pilot project.

Choose a small non-critical service and implement it as a microservice: develop, deploy, and connect it to the overall system.

A pilot makes it possible to validate the solution principles and identify issues before scaling up. Gradual scaling.

Move individual parts of the system to microservices one by one, gradually moving away from the monolith.

Gradual rollout reduces implementation risks and gives the team time to adapt. Optimization, monitoring, technical management.

Track performance, errors, and interactions; optimize; and introduce failure-handling practices.

To ensure a smooth transition, take a systematic approach.

Start with an audit, choose one service for the pilot, and refine CI/CD on it.

Develop a step-by-step plan with metrics and business context in mind.

Containers are the standard way to package a service with all its dependencies so it runs the same on a developer's laptop, in test, and in production.

A well-built container enables predictable deployment and reduces security and release-speed issues. Service orchestration.

When there are dozens or hundreds of services, they must be deployed, restarted, and scaled automatically. Kubernetes is the standard container orchestration system that makes microservices manageable and resilient. API gateways and service mesh. Microservices have two traffic planes: external, client-facing, and internal, between services.

They are managed by different components: API gateways for the external perimeter and a service mesh for internal traffic.

They standardize network behavior and eliminate instability and manual operations in code. Messages and queues.

Asynchronous processing smooths out peaks and decouples services over time.

Queues protect against collapse under peak loads and help prevent data loss during temporary receiver outages.

Without metrics and tracing, it is hard to find the source of a problem.

A single tracing context across the gateway, service mesh, queues, and database helps investigate incidents faster.

Security and access management.

The more services you have, the larger the attack surface, so build security into the platform. CI/CD and release strategies.

The entire business rhythm depends on pipeline speed and reliability.

Automation and standardized pipelines save months of work and make releases simple and predictable.

Configuration and feature management.

Configuration changes more often than code, so move it out and control it.

Managed configuration reduces manual work and speeds up experiments. Performance and caching.

Peak loads are the main test for microservices.

Use caches, connection pools, and profiling, and run load testing. Add redundancy, backups, and a recovery plan.

Regularly back up databases and verify that recovery works.

Create a disaster recovery plan: who is responsible for what, and what to do in case of failure. Economics: FinOps and quotas.

Microservices can quickly consume resources if the budget is not managed.

Set limits in Kubernetes and report consumption by product/team.

Set up automatic shutdown of environments outside working hours, TTL for temporary environments, monthly project limits, and alerts.

Transparent costs bring discipline to architecture and teams.

Environments and providers. Choose where to run the microservices architecture.

Take into account CIS regulatory requirements for localization, storage, and processing of personal data.

Often the optimal solution is hybrid:

• critical processes locally
• on owned racks in the data center
• the rest - in the cloud with rapid scaling
• geo-distribution

Define standards for every layer from the outset - from the container to the SLO alert.

This will make the platform manageable, secure, and cost-effective.

Context. By 2019, CIS's largest electronics retailer, M.Video-Eldorado, faced a typical situation for fast-growing chains:

More than 850 stores and about 30 million customers.

A sharp rise in online sales after the pandemic - in 2020, e-commerce share grew from about 15% to 40%.

The business was actively moving to an omnichannel model, where customers needed to buy seamlessly online and offline, with any returns, delivery, and payment options. The IT system was monolithic: updating any part required a full deployment.

Time to release a new feature - up to 3 weeks.

Peak sales events like Black Friday caused outages due to overload in individual modules - the catalog and cart.

According to internal audit data, outages during promotions cost the company up to 1.5% of monthly revenue - tens of millions of rubles. Project goal.

Create a new IT architecture that will: release customer features quickly - speed up time-to-market by 3x; scale the system for peak loads without increasing infrastructure costs; improve resilience and reduce downtime risk; simplify omnichannel scenarios: a single cart, delivery, bonuses, and returns. Implementation.

The company moved to a microservices architecture in stages. Stage 1 - audit and pilot:

Conducted an IT landscape audit: more than 100 functional modules.

Identified candidate services:

• product catalog
• cart
• payment
• account dashboard

The cart was chosen for the pilot - the most loaded and frequently changed component.

A 7-person team built the first microservice on Java + Spring Boot and deployed it in containers. Stage 2 - scaling:

After a successful pilot, the following services were added:

• catalog
• recommendations
• Promotions
• bonus system

Each team of 5-8 people was responsible for its own service under the "You build it - you run it" model.

CI/CD was set up, and deployment was automated. Stage 3 - integration and API gateway:

An API gateway was introduced as a single access point for the frontend and mobile apps.

REST and Kafka were used for data exchange between services.

Start the move to microservices with one critical module and build CI/CD and observability in parallel - this will deliver business value quickly and reduce risk.

Autonomous product teams and an API-first approach can dramatically speed up releases and minimize downtime without a full system overhaul.

Context. By 2020, one of the largest transport and logistics networks

Create a new architecture that will: move status updates closer to real time; reduce the time to launch changes from 45 days to under 1 week; ensure reliable marketplace integrations; reduce downtime and incident volume; prepare the platform for further digitalization. Implementation.

The company adopted microservices gradually.

Stage 1 - strategy and design:

Conducted a landscape audit and identified 5 domains for microservices:

• orders
• routes
• Pricing
• Notifications
• customers

Created a 3-year roadmap and target SLA/metrics.

Core technologies were defined: Kubernetes, an event bus, and centralized observability. Stage 2 - Notifications pilot:

Implemented a notification microservice and integrated it with the existing system via API.

They reduced notification delivery latency from 5 minutes to 30 seconds.

Deployment and observability patterns were refined. Stage 3 - scaling:

Extracted the "Routes", "Fare Calculation", and "Tracking" services from the monolith.

Deployed containers in Kubernetes and used an internal Tier III data center in St. Petersburg.

Introduced Kafka as an event bus and standardized contract schemas.

Case lesson. In logistics, speed and observability matter more than the monolith's versatility.

To achieve near real-time results without overhauling the entire core, start with the event-driven domain, establish CI/CD and monitoring practices, then gradually extract high-load services.

Context. Home appliance and cookware brand Polaris sells through Ozon and Wildberries,

Yandex Market, SberMegaMarket, and your own e-shop.

At the start: product cards for each channel were filled in manually, and requirements differed; data in ERP, Excel, and cloud files did not match; edits - descriptions, photos, attributes - took 2-3 days; errors led to SKU blocks and lost sales; there was no single source of truth for product data. Project goal.

Set up a centralized product data management system and integrate it with ERP and marketplaces to eliminate manual duplicate entry; ensure data validation and completeness before export; speed up publishing and updating product cards; easily connect new platforms without changing the core. Implementation.

The project was built on Pimcore + ESB, with microservice-based integration. Stage 1 - project analysis:

Requirements were collected from the marketing, content, logistics, and sales teams.

Analyzed the storage structure in ERP and marketplace specifications.

Designed the information model and configured the base Pimcore classes.

Stage 2 - Pimcore integration and a unified product card:

A unified product card form was developed in Pimcore: the content manager fills in one set of fields - the system adapts it to each channel's format automatically.

Implemented reference data and attribute mapping rules so an administrator can change the mapping without code.

Added automatic mapping updates through additional microservices within the ESB environment.

Stage 3 - service bus and marketplace exchange:

WSO2 ESB was chosen as the bus, RabbitMQ as the message broker, and ELK for logging and dashboards.

Configured the inbound flow: events in Pimcore (SKU creation/update) -> enrichment with prices/stock from ERP -> RabbitMQ (a separate queue for each marketplace).

The outbound layer was implemented: for each marketplace, a separate microservice adapter handles export, statuses, and writing IDs/statuses back to Pimcore.

A single product card and message queues reduce manual work and errors.

Centralize product data and move integrations into microservices through the bus - connecting new marketplaces will shift from a project to a routine operation.