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Microservices Architecture Development

Pharos Production designs and builds Microservices architectures that replace monolithic applications with independently deployable, scalable services.

  • 90+ engineers
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  • 13+ years in business
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Reviewed and updated
Last reviewed July 6, 2026 by Dmytro Nasyrov, Founder and CTO. Content reflects Pharos Production delivery data as of the review date. Editorial policy.
Dmytro Nasyrov - Founder and CTO of Pharos Production

Reviewed by Dmytro Nasyrov

Founder and CTO

23+ years in custom software development. Led 110+ projects across FinTech, healthcare, Web3 and enterprise, ISO 27001-aligned team.

What is microservices development?

Microservices development is the architectural approach of building applications as a collection of loosely coupled services that communicate over well-defined APIs, each owned by a small team and deployable independently. It covers service decomposition (bounded contexts from domain-driven design), API design (REST, gRPC, event-driven), service discovery, distributed tracing, circuit breakers, saga patterns for distributed transactions and the operational discipline to run dozens of services reliably. Microservices are not a silver bullet - they trade single-service simplicity for independent scaling, team autonomy and failure isolation. Pharos has built and migrated microservices architectures for FinTech, high-load consumer, SaaS and crypto platforms since 2018.
Authoritative citations 12 sources
  1. DORA State of DevOps Report The Google DORA State of DevOps annual report defines the four key software delivery metrics (deployment frequency, lead time for changes, mean time to restore, change failure rate) that we instrument on every production engagement to benchmark delivery performance. dora.dev
  2. Stack Overflow Developer Survey The Stack Overflow Developer Survey documents language, framework, database and tooling adoption across tens of thousands of engineers annually, and we use the trend lines to validate stack choices against hiring pool depth for each client. survey.stackoverflow.co
  3. ThoughtWorks Technology Radar The ThoughtWorks Technology Radar tracks tools, platforms, techniques and languages across adopt, trial, assess and hold rings twice yearly, and is a cross-check we use to validate architectural recommendations against industry consensus. thoughtworks.com
  4. Google SRE Book The Google SRE book codifies service-level objectives, error budgets, incident response and postmortem culture that our production readiness gates adopt directly when handing over a platform to a client operations team. sre.google
  5. Martin Fowler bliki Martin Fowler's bliki is the most cited reference for enterprise architecture patterns including microservices, strangler fig, CQRS, event sourcing and refactoring, which shapes how we describe and implement architecture decisions in ADRs on every client engagement. martinfowler.com
  6. Gartner Custom Application Services Magic Quadrant Gartner publishes multiple Magic Quadrant reports covering custom application services, digital engineering and outsourced development that identify market leaders, completeness of vision and niche specialists across the global software services industry. gartner.com
  7. ISO 27001 Information Security Standard ISO 27001:2022 defines the internationally recognized information security management system requirements that Pharos Production operates under, shaping the control framework we inherit and extend for client software engagements. iso.org
  8. OWASP Top 10 The OWASP Top 10 ranks the highest-impact web application security risks and is the single most cited threat reference for application security programs, which every Pharos build is reviewed against before production release. owasp.org
  9. NIST Secure Software Development Framework NIST SSDF SP 800-218 defines secure development practices including threat modelling, SBOM generation, vulnerability disclosure and supply chain controls, which we treat as the baseline Software Development Lifecycle checklist on every client engagement. csrc.nist.gov
  10. CNCF Cloud Native Landscape The CNCF Cloud Native Landscape maps the full cloud-native ecosystem across orchestration, runtime, observability, security and database categories, useful reference material we consult when validating platform choices for client Kubernetes and service mesh engagements. landscape.cncf.io
  11. Accelerate by Forsgren, Humble, Kim Accelerate distills the multi-year DORA research program into the book-length case for DevOps practices correlated with high-performance software delivery, and is the single most cited academic reference for the delivery metrics we ship inside every client engagement. itrevolution.com
  12. IEEE SWEBOK The IEEE Software Engineering Body of Knowledge codifies the professional knowledge areas covering requirements, design, construction, testing, maintenance, configuration management and engineering economics that underpin every professional software services engagement. computer.org
What we do not do
  • Teams with fewer than 10 engineers where the operational tax outweighs the benefits
  • Greenfield projects where a modular monolith would ship faster and be easier to refactor
  • Microservice migrations without a clear bounded-context analysis
  • Projects that assume microservices means "better architecture" without a specific problem to solve

Microservices development at Pharos Production at a glance

  • Microservices projects: 12+ microservices projects since 2018 (greenfield, monolith-to-microservices migrations, event-driven refactors, service extraction)
  • Stack: Go, Java/Spring, Node.js, Elixir/Phoenix, Python, gRPC, Kafka, NATS, PostgreSQL, Redis, Kubernetes, Istio, OpenTelemetry
  • Patterns: Bounded contexts from DDD, outbox pattern for event sourcing, saga for distributed transactions, circuit breakers, bulkheads, CQRS where it fits
  • Observability: Distributed tracing from day one (OpenTelemetry → Tempo/Honeycomb/Datadog), service maps, golden signals SLOs, runbooks per service
  • Pricing: Architecture review from $10,000; migration projects $60,000-$250,000+; greenfield microservices $100,000-$500,000+
  • Timeline: Discovery + bounded context analysis 2-4 weeks; migration 3-9 months with incremental cutover; greenfield 4-8 months
  • Default recommendation: Modular monolith for teams under 10 engineers; extract services only when a specific problem justifies the operational tax
  • Honest scope: We decline microservices projects for small teams and greenfield work without bounded contexts identified

Our approach to distributed systems

Microservices projects follow Pharos Verified Delivery with distributed-systems-specific gates: discovery includes bounded context analysis, service boundary design and migration strategy; build includes distributed tracing from day one, circuit breakers and saga patterns for distributed transactions; production readiness covers service-to-service contracts, observability and failure mode testing; support includes quarterly architecture reviews and operational runbook maintenance.

Pharos Verified Delivery 4-phase methodology with typical durations and deliverables
  1. Phase 01 / 04

    Paid Discovery

    2-4 weeks
    • Technical validation
    • Architecture proposal
    • Scope refined estimate
    82% on-schedule with discovery
  2. Phase 02 / 04

    Iterative Build

    2-week sprints
    • Working demos every sprint
    • CTO review at milestones
    • ADRs documented
    Transparent progress tracking
  3. Phase 03 / 04

    Production Readiness

    • Monitoring and alerting
    • Security audit Pen test
    • Runbooks and rollback
    ISO 27001 aligned
  4. Phase 04 / 04

    Support

    Ongoing
    • Security patches
    • Performance tuning
    • 4h SLA response
    Continuous improvement

Pharos Verified Delivery applied to 110+ production applications since 2013

Microservices engagements we shipped

Three distributed-systems engagements including one where the answer was "stay a monolith."

Strangler-fig migration

Q4 2024 · Sportsbook platform, global
Before

Monolithic Rails backend hit p99 latency of 4.2 seconds during peak betting windows. 14 production incidents in the last quarter. Engineering team afraid to deploy on Sunday mornings.

After

Selective extraction of 3 services (odds, bet placement, settlement) to Go with the remaining Rails monolith as the fallback. p99 latency dropped to 180ms. Zero peak-window incidents in 4 months post-cutover. Deployment frequency went from weekly to multiple times per day.

We extracted only the 3 services that owned 85% of request volume and left the rest as a Rails monolith. We did not "rewrite to microservices" - we solved a specific performance problem with targeted extraction. Cutover used request shadowing for 2 weeks before traffic was switched.

Event-driven refactor

Q1 2025 · FinTech ledger, EU
Before

Synchronous request chains across 7 microservices. One slow downstream caused cascading timeouts. Reconciliation reports lagged 18 hours behind real-time.

After

Kafka event bus with idempotent consumers and outbox pattern. Reconciliation lag down to 90 seconds. Cascading failures eliminated. Each service deployable independently.

The outbox pattern is the key - every state change writes to a local ledger table inside the same database transaction, then a separate process publishes to Kafka. No dual-write inconsistency, no lost events on crash.

Monolith-first approach

Q2 2025 · Series-A startup, US
Before

Client was preparing to build a "microservices-native" platform from day one. 5-engineer team. No specific bounded contexts identified. Investor pressure to use modern architecture.

After

Pharos recommended a modular monolith with clean module boundaries as the starting point. Platform shipped in 4 months instead of 9. Team velocity 3x higher than the microservices plan would have delivered. Plan to extract services when team reaches 15+ engineers.

We wrote a 2-page decision memo explaining why microservices for a 5-person team would fail. Saved the client ~$400K in wasted infrastructure and engineering time. The modular monolith ships clean service boundaries that can be extracted later when the team grows - no rewrite needed.

Client names anonymized under NDA. Full case studies at /cases/.

When microservices are not the answer

We decline roughly 30% of RFPs we receive. Forcing a bad fit costs both sides 3-6 months and damages outcomes. Here is how we think about scope:

Projects we decline
  • Teams with fewer than 10 engineers (the operational tax outweighs benefits)
  • Greenfield projects where a modular monolith would ship faster and refactor easier
  • Microservice migrations without clear bounded contexts identified
  • Projects that assume microservices means "better architecture" without a specific problem
  • "Microservices-native" platform rewrites for investor optics rather than technical need
We recommend modular monoliths when they fit

Microservices solve specific problems: independent scaling, team autonomy across 10+ engineers, failure isolation between bounded contexts. They create specific problems: operational overhead, distributed transaction complexity, network latency, observability cost. For teams under 10 engineers and products without proven scale pressure, a modular monolith is the right starting architecture. We have talked clients out of microservices rewrites many times - the operational tax is real and it bites silently.

Pharos original research

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Backend Development at Pharos Parent engagement model for backend engineering including microservices, event-driven architectures and migrations. Continue reading

Pharos Production microservices portfolio observations

Observations from 23 microservices engagements delivered 2019-2026 across FinTech, SaaS, healthcare and logistics.

  • Architectures with platform teams sized to 1 platform engineer per 15 service developers maintained DORA "High" or "Elite" metrics; below that ratio, metrics degraded within 9 months.

  • Migrations from monolith to microservices took 12-18 months on average; rushed migrations (under 6 months) had a 3 of 4 rollback or re-consolidation rate in our portfolio.

  • Contract testing adoption from day one reduced cross-service integration defects by 4.6x versus retrofit contract testing.

  • Teams of 6 to 10 engineers delivered initial microservice architecture (5 to 8 services) in 16 to 24 weeks when bounded contexts were documented upfront.

Observations reflect the Pharos Production microservices portfolio 2019-2026 and do not generalise beyond surveyed engagements.

Microservices architecture outlook 2026-2027

Microservices in 2026 are a mature architectural choice, not a fashion. The consensus has converged: bounded contexts, event-driven integration, service meshes for observability and platform teams to absorb the operational tax. The industry now actively warns against microservices for teams without platform maturity; modular monoliths are returning as the default for startup and sub-100 engineer teams.

  • Martin Fowler and Sam Newman publish continuing updates on bounded context decomposition, coupling analysis and distributed transactions; their patterns remain the canonical reference[5].

  • ThoughtWorks Radar 2024 moves "microservices as default" to "Hold" for teams under 100 engineers; modular monolith moved to "Adopt"[3].

  • CNCF Landscape 2025 continues to dominate the microservice operational stack (Kubernetes, Istio, Linkerd, OpenTelemetry, Argo CD)[10].

  • DORA 2024 confirms elite performers use microservices at scale but require platform teams of 3+ engineers per 50 service developers[1].

How to evaluate a microservices architecture in 90 days

Before committing to a microservices architecture, run this 8-point readiness audit. Teams below 6 of 8 passing typically produce distributed monoliths, not microservices.

  1. Bounded context map

    Existing domain decomposed into 3 to 12 bounded contexts; each owns its data and has explicit public API.

  2. Platform team sized

    Minimum 3 platform engineers per 50 service developers; internal developer platform with golden paths.

  3. Contract testing

    All service-to-service contracts covered by consumer-driven tests; breaking changes surface in CI[3].

  4. Observability baseline

    OpenTelemetry traces, metrics and logs on every service; correlation IDs end to end[10].

  5. Service mesh or equivalent

    mTLS, retry policies, circuit breakers and traffic management consistent across services.

  6. Deployment independence

    Each service deployable independently; no coordinated release trains for bug fixes.

  7. Data ownership clarity

    No shared database across services; saga or event-driven patterns for cross-context transactions[5].

  8. On-call and SLOs

    100 percent of user-facing services with SLOs and error budgets; documented paging ownership[4].

Teams below 6 of 8 should consider a modular monolith first; microservices require operational maturity that is expensive to bootstrap mid-build.

Lesson from production: the distributed monolith

A SaaS customer migrated from a Ruby monolith to 28 Go microservices in 2022 over 14 months. Post-migration metrics looked worse: lead time increased 3x, incident frequency doubled and on-call burn drove 2 senior engineers to leave. Root cause: services were split by repository, not by bounded context. 22 of 28 services shared a primary database and required coordinated releases for any meaningful change. The team built a distributed monolith with microservice operational tax. We spent 6 months consolidating the 22 coupled services back into 4 bounded-context services, kept 6 independent services with clear ownership and added contract tests. Lead time recovered to pre-migration levels, then exceeded them by 40 percent within 3 quarters. The lesson: microservices boundaries must match domain boundaries, not team or repository boundaries.

How distributed-systems metrics are counted
Microservices metrics counted: production migrations and greenfield builds with measurable latency, deployment and incident baselines. Latency improvements measured against client-reported pre-engagement baselines. Deployment frequency measured from actual CI/CD history, not estimates. Last reviewed: July 2026. Editorial policy.
Operational trade-offs
Pharos Production builds microservices architectures. Microservices are an architectural choice with real trade-offs - we do not recommend them as a default. Operational outcomes depend on team capacity and long-term ownership post-engagement.

Published record

Published Pharos research

Technical articles, comparison guides and methodology deep-dives we write from our own delivery experience.

Platforms we work with

Trusted by Coinbase, Consensys, Core Scientific, MicroStrategy, Gate.io and 10+ more Web3 and enterprise platforms

16+ partners

Our 16 technology partners include:

  • Consensys
  • Gate Io
  • Coinbase
  • Ludo
  • Core Scientific
  • Debut Infotech
  • Axoni
  • Alchemy
  • Starkware
  • Mara Holdings
  • MicroStrategy
  • Nubank
  • Okx
  • Uniswap
  • Riot
  • Leeway Hertz
  • Consensys
  • Gate Io
  • Coinbase
  • Core Scientific
  • Debut Infotech
  • Axoni
  • Alchemy
  • Starkware
  • Mara Holdings
  • MicroStrategy
  • Nubank
  • Okx
  • Uniswap
  • Riot
  • Leeway Hertz

About the founder and CTO

Dmytro Nasyrov

Dmytro Nasyrov

Founder and CTO Pharos Production

Ask the founder a question

I design and build reliable software solutions - from lightweight apps to high-load distributed systems and blockchain platforms.

PhD in Artificial Intelligence, MSc in Computer Science (with honors), MSc in Electronics & Precision Mechanics.

  • 13 years in architecture of great software solutions tailored to customer needs for startups and enterprises

  • 23 years of practical enterprise customized software production experience

  • Lecturer at the National Kyiv Polytechnic University

  • Doctor of Philosophy in Artificial Intelligence

  • Master's degree in Computer Science, completed with excellence

  • Master's degree in Electronics and precision mechanics engineering

Choose your cooperation model

Pharos Production offers three project models, MVP, Full-fledged Production and Full-cycle Development, priced from $10,000 to $80,000. An MVP prototype takes about 3 months.

MVP
MVP sprint

Scoped MVP with core user flows, clean codebase and production-ready deployment.

$10,000 - $24,000
Popular choice
Production
Production release

Full-feature build, QA, CI/CD and post-launch stabilization with SLA-backed support.

$24,000 - $50,000
Full-cycle
Full-cycle platform

End-to-end engagement: discovery, architecture, build, DevOps, QA and long-term evolution.

$50,000 - $100,000

Prices vary based on project scope, complexity, timeline and requirements. Hourly rates range from $35 to $75 depending on role and seniority. Contact us for a personalized estimate.

Interaction models for staff augmentation, dedicated teams and outsourcing

Request staff augmentation

Need extra hands on your software project? Our developers can jump in at any stage - from architecture to auditing - and integrate seamlessly with your team to fill any technical gaps.

Popular choice

Hire dedicated experts

Whether you're building from scratch or scaling fast, our engineers are ready to step in. You stay in control, and we handle the code.

Outsource your project

From first line to final audit, we handle the entire development process. We will deliver secure, production-ready software, while you can focus on your business.

187+ technologies

Technologies, tools and frameworks we use

Our engineers work with 187+ technologies across blockchain, backend, frontend, mobile and DevOps - chosen for production reliability and performance.

Our engineers work with 187+ technologies across 10 categories: Frameworks, AI, Blockchains, DevOps, Clouds, Databases, Brokers, Tests, Programming, UI/UX.

  • Frameworks: Spring Boot, Erlang OTP, NodeJS, Phoenix, NestJS, Django, FastAPI, Express.js, React, Next.JS, Svelte, Angular, Vue.js, Remix, Astro, Nuxt.js, iOS, Android, Flutter, React Native, Capacitors, Ionic, Swift, Kotlin, Java, Dart
  • AI: OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama, Mistral AI, Cohere, Ollama, xAI Grok, LangChain, LangGraph, CrewAI, AutoGen, Hugging Face, PyTorch, TensorFlow, scikit-learn, LlamaIndex, Keras, XGBoost, LightGBM, OpenCV, spaCy, ONNX Runtime, Pinecone, Weaviate, Qdrant, Chroma, pgvector, Milvus, FAISS, MLflow, Weights & Biases, DVC, Kubeflow, AWS SageMaker, Azure ML, Google Vertex AI, NVIDIA Triton, Airflow, Ray Serve, vLLM, OpenAI Agents SDK, Claude MCP, Semantic Kernel, Haystack
  • Blockchains: Ethereum, TON, Corda, Tron, Hedera, Stellar, Consensys GoQuorum, Solana, Arbitrum, Binance Smart Chain (BSC), Sei, Celo, Hyperledger, MultiversX, IOTA, Polkadot, Aptos, Neo, Flow, Algorand, Avalanche, EOS, Optimism, Polygon, Cosmos, Sui, Tezos, Ontology, Fantom, NEAR Protocol, VeChain, Base, IPFS, Amazon Managed Blockchain, Amazon QLDB, IBM Blockchain, Oracle Blockchain
  • DevOps: Kubernetes, Terraform, Docker, Istio, Prometheus, Grafana, Jenkins, ArgoCD, Ansible, GitHub Actions, GitLab CI, Pulumi, Datadog, New Relic, Vault
  • Clouds: Amazon Web Services, Azure, Google Cloud, Cloudflare, Vercel, DigitalOcean
  • Databases: PostgreSQL, MySQL MariaDB, Redis, Cassandra, Neo4J, MongoDB, Elasticsearch, Solr, Ignite, ClickHouse, TimescaleDB, DynamoDB, Supabase, CockroachDB, ScyllaDB
  • Brokers: Kafka, RabbitMQ, Flink, Apache Pulsar, Amazon SQS, Amazon SNS, NATS
  • Tests: Postman, Appium, Cucumber, Selenium, JMeter, Cypress
  • Programming: Solidity, FunC, Rust, GoLang, Elixir, Erlang, C++, Java, JavaScript, TypeScript, Scala, Python, C#, .NET, PHP, Ruby, Dart, SQL
  • UI/UX: Figma, Zeplin, InVision, Sketch, Miro, Marvel, Balsamiq, Photoshop, Illustrator, XD, After Effects, Corel Draw

Frameworks

Backend Frameworks 8

Spring Boot
Spring Boot
Erlang OTP
Erlang OTP
NodeJS
NodeJS
Phoenix
Phoenix
NestJS
NestJS
Django
FastAPI
Express.js

Front End Frameworks 8

React
React
Next.JS
Next.JS
Svelte
Svelte
Angular
Angular
Vue.js
Remix
Astro
Nuxt.js

Mobile Apps Frameworks 10

iOS
iOS
Android
Android
Flutter
Flutter
React Native
React Native
Capacitors
Capacitors
Ionic
Ionic
Swift
Swift
Kotlin
Kotlin
Java
Java
Dart
Trusted & Certified

Partnerships and awards

Recognized on Clutch, GoodFirms and The Manifest for software engineering excellence

  • Partner1
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  • Partner4
  • Partner5
14+ industry awards

An approach to the development cycle

The Pharos Delivery Framework divides every project into 2-week sprints. After each sprint we hold a retrospective, deliver a progress report and plan the next sprint. This methodology is why agile projects are 3x more likely to succeed than waterfall (Standish Group CHAOS Report, 2024).

  1. Team Assembly

    Our company starts and assembles an entire project specialists with the perfect blend of skills and experience to start the work.

  2. MVP

    We'll design, build and launch your MVP, ensuring it meets the core requirements of your software solution.

  3. Production

    We'll create a complete software solution that is custom-made to meet your exact specifications.

  4. Ongoing

    Continuous Support

    Our company will be right there with you, keeping your software solution running smoothly, fixing issues and rolling out updates.

Architecture insights

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Skip glossary

Microservices Architecture Glossary 7

Bounded Context
A domain-driven design boundary within which a specific model applies, defining the scope and ownership of a single microservice and its data store.
API Gateway
A single entry-point layer that routes client requests to downstream microservices, enforcing authentication, rate limiting and request transformation across the system.
Service Mesh
An infrastructure layer that manages service-to-service communication with sidecar proxies, providing mutual TLS, circuit breaking, retries and distributed tracing without modifying application code.
Strangler-Fig Pattern
An incremental migration strategy that routes specific traffic paths to new microservices while the legacy monolith continues serving the remainder, avoiding a risky big-bang rewrite.
Event-Driven Architecture
A messaging pattern in which services emit and consume domain events via a broker like Kafka, enabling loose coupling and eventual consistency across service boundaries.
Saga Pattern
A data consistency technique for distributed systems that sequences local transactions across services and uses compensating transactions to handle failures without two-phase commits.
Sidecar Proxy
A co-deployed container that intercepts all network traffic to and from a microservice, used by service meshes such as Istio to enforce policy without code changes.

Frequently asked questions about Microservices Architecture Development

Last updated:

  • Copy link Copies a direct link to this answer to your clipboard.

    Default: modular monolith. Split into services when one of these things happens: (1) independent scaling needs (one component has 10x the load of others), (2) independent deployment needs across teams of 10+ engineers, (3) a clear bounded context that benefits from a different runtime. Do not use microservices for teams under 10 engineers, greenfield projects without proven scale pressure or "modern architecture" as a goal in itself. We have talked clients out of microservices rewrites many times.

  • Copy link Copies a direct link to this answer to your clipboard.

    Bounded contexts from domain-driven design. During discovery we map the business capabilities (not the database tables) and draw service boundaries around cohesive capabilities that change together.

    Services that frequently need each other's data synchronously are a smell - they probably should be one service. We write the boundary decisions in ADRs the client owns.

  • Copy link Copies a direct link to this answer to your clipboard.

    Prefer eventual consistency with saga patterns when possible - cleaner than distributed ACID. For operations that need strong consistency across services, use the outbox pattern (write to local DB + event table in one transaction, then publish asynchronously).

    Two-phase commit across services is a last resort and usually signals a boundary problem that should be refactored instead.

  • Copy link Copies a direct link to this answer to your clipboard.

    Distributed tracing from day one via OpenTelemetry - no exceptions. Every request carries a trace ID that surfaces in logs, metrics and error reports.

    Per-service SLOs on the golden signals (latency, traffic, errors, saturation) with error budgets. A service map showing real runtime dependencies, not just architectural intent. Runbooks per service alerting next to the alarms.

  • Copy link Copies a direct link to this answer to your clipboard.

    Strangler-fig pattern. Identify the 2-3 services that would benefit most from extraction (usually scale-critical or independently-deployable). Extract one at a time with request shadowing (new and old serve the same traffic for a validation window) before traffic cutover. Keep the monolith as the fallback for the rest. We never recommend big-bang microservices rewrites - they fail most of the time.

  • Copy link Copies a direct link to this answer to your clipboard.

    Architecture review + decision memo from $10,000. Migration of 2-4 services from a monolith $60,000-$250,000+. Greenfield microservices platform $100,000-$500,000+ depending on complexity. Ongoing operational costs are typically 2-4x higher than equivalent monolith because of distributed tracing, service mesh, multi-service CI/CD and more on-call surface area.

  • Copy link Copies a direct link to this answer to your clipboard.

    There is no magic number. The right answer is "as few as possible while still getting the specific benefits you need." Amazon famously uses Conway's Law ("team size × team count = service count approximately") - if you have 5 teams of 8 engineers each, you probably need 5-15 services, not 50.

    We have seen 20-engineer teams running 60 microservices collapse under the operational weight.

  • Copy link Copies a direct link to this answer to your clipboard.

    We decline microservices projects for teams under 10 engineers, greenfield work without clear bounded contexts, migrations without a specific problem to solve and "microservices-native" rewrites for investor optics. We have written decision memos explaining why clients should NOT rewrite to microservices - sometimes that memo IS the deliverable.

The Pharos takeaway on microservices

Microservices architecture in 2026 is measurable: bounded contexts, contract testing, observability and platform maturity. Pharos Production builds microservice architectures for teams that have or will have the platform capability to operate them, and recommends modular monoliths when that bar is not met.

Dmytro Nasyrov, Founder and CTO at Pharos Production
Dmytro Nasyrov Founder & CTO Let's work together!

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