Vue or React – which one do you choose? We brought together experts to discuss the strengths and weaknesses of both technologies.
Some developers bet on React for its flexibility, scalability, and powerful ecosystem. Others vote for Vue for its simplicity, clean architecture, and easy entry into the profession. So what is the fundamental difference? How do both tools perform in real-world projects? What are their pros and cons? And what ultimately becomes the deciding factor for a team?
📌 Vasily Vanchuk, Lead Expert at T‑Bank
📌 Nikita Zuyanov, Senior UI Developer at Ozon
📌 Denis Chernov, Lead Developer at SM Lab
📌 Ivan Malyugin, Head of Platform Development at Beeline
📌 Semyon Levenson, Head of Client Development at Yandex ID
What you agree or disagree with – find in our cards.
Some developers bet on React for its flexibility, scalability, and powerful ecosystem. Others vote for Vue for its simplicity, clean architecture, and easy entry into the profession. So what is the fundamental difference? How do both tools perform in real-world projects? What are their pros and cons? And what ultimately becomes the deciding factor for a team?
📌 Vasily Vanchuk, Lead Expert at T‑Bank
📌 Nikita Zuyanov, Senior UI Developer at Ozon
📌 Denis Chernov, Lead Developer at SM Lab
📌 Ivan Malyugin, Head of Platform Development at Beeline
📌 Semyon Levenson, Head of Client Development at Yandex ID
What you agree or disagree with – find in our cards.
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Need help taking your EdTech platform to the next level? Check out how we handled it. Write to Evrone: mail@evrone.com
Kinderlime: scaling a platform for 30,000+ daycare centers.
Kinderlime automates private daycare and preschool management in the US: contactless check-in, payments, staff management, enrichment programs. Before they launched, the early childhood education sector was one of the least digitized. By the time they came to us, the platform was already running in 30,000+ centers.
The client had a distributed team (India, California, San Jose) and a working Rails app. But as the business grew, the Rails monolith started to slow down. The goal was to separate the frontend and backend via a REST API and rebuild the frontend based on new mockups.
What we did:
Built a library on top of Redux and Axios that generates API requests, reducers, and response handling. Saved hundreds of lines of repetitive code.
Built a custom form builder from scratch: custom fields, multi-step forms.
Added a CRM with "Insights" dashboards — pipelines, funnels, conversion analytics.
Built an email builder: custom campaigns, mailing list management, automated emails to parents of enrolled children (renders correctly in Outlook, Gmail, Apple Mail).
Tech stack: React, Redux, Webpack, Stripe, CircleCI, AWS, Intercom, React DND. We used Cypress for the first time on this project and achieved 90% end-to-end test coverage.
The result: seamless communication across three time zones, a strong engineering team integrated with the product team, and a modern interface that helped Kinderlime attract even more customers.
If your business needs a scalable EdTech platform – reach out.
You can read more about this case here: https://evrone.com/cases/kinderlime
Kinderlime: scaling a platform for 30,000+ daycare centers.
Kinderlime automates private daycare and preschool management in the US: contactless check-in, payments, staff management, enrichment programs. Before they launched, the early childhood education sector was one of the least digitized. By the time they came to us, the platform was already running in 30,000+ centers.
The client had a distributed team (India, California, San Jose) and a working Rails app. But as the business grew, the Rails monolith started to slow down. The goal was to separate the frontend and backend via a REST API and rebuild the frontend based on new mockups.
What we did:
Built a library on top of Redux and Axios that generates API requests, reducers, and response handling. Saved hundreds of lines of repetitive code.
Built a custom form builder from scratch: custom fields, multi-step forms.
Added a CRM with "Insights" dashboards — pipelines, funnels, conversion analytics.
Built an email builder: custom campaigns, mailing list management, automated emails to parents of enrolled children (renders correctly in Outlook, Gmail, Apple Mail).
Tech stack: React, Redux, Webpack, Stripe, CircleCI, AWS, Intercom, React DND. We used Cypress for the first time on this project and achieved 90% end-to-end test coverage.
The result: seamless communication across three time zones, a strong engineering team integrated with the product team, and a modern interface that helped Kinderlime attract even more customers.
If your business needs a scalable EdTech platform – reach out.
You can read more about this case here: https://evrone.com/cases/kinderlime
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A typical situation: a company has an internal knowledge base, but employees still go to HR with questions. Because messaging a real person in chat is faster than searching for answers in the wiki.
At Evrone, we embedded an RAG-based AI assistant directly into the Evrone ERP personal account. The chat is on every page – employees ask questions and get answers without switching between windows.
On the technical side: the frontend is built with React, communication runs through WebSockets (asynchronous exchange and real-time response streaming). The backend uses Python with FastAPI – we chose a lightweight framework for performance.
The wiki is written in Markdown. We split the text into chunks based on headings (header-based splitting). Semantic search uses vector embeddings – employees can phrase questions naturally, and the model searches by meaning.
We use Qwen as the LLM. We had already used it before for a smart resume parser. On this project, we tested several models, including popular open-source and a number of Russian solutions. We evaluated response quality, inference speed, and token cost. Qwen showed the best results across all factors: it follows instructions accurately, understands Russian-language queries well, and is more cost-effective than the other options.
The system automatically tracks changes in the wiki: a Python script runs on a schedule or via a Webhook trigger, then rebuilds the vector index. A custom React-based admin panel allows adjusting the number of chunks, the LLM, the system prompt, and generation parameters on the fly.
Security: the infrastructure is in-house, so no data leaks occur. Topic filtering ensures the model only answers questions related to company documentation. Rate limiting is applied at the API level.
The architecture is universal: it can be adapted for customer support, a consultant on a website, or an AI agent inside your own ERP.
You can learn more about how we did this here.
At Evrone, we embedded an RAG-based AI assistant directly into the Evrone ERP personal account. The chat is on every page – employees ask questions and get answers without switching between windows.
On the technical side: the frontend is built with React, communication runs through WebSockets (asynchronous exchange and real-time response streaming). The backend uses Python with FastAPI – we chose a lightweight framework for performance.
The wiki is written in Markdown. We split the text into chunks based on headings (header-based splitting). Semantic search uses vector embeddings – employees can phrase questions naturally, and the model searches by meaning.
We use Qwen as the LLM. We had already used it before for a smart resume parser. On this project, we tested several models, including popular open-source and a number of Russian solutions. We evaluated response quality, inference speed, and token cost. Qwen showed the best results across all factors: it follows instructions accurately, understands Russian-language queries well, and is more cost-effective than the other options.
The system automatically tracks changes in the wiki: a Python script runs on a schedule or via a Webhook trigger, then rebuilds the vector index. A custom React-based admin panel allows adjusting the number of chunks, the LLM, the system prompt, and generation parameters on the fly.
Security: the infrastructure is in-house, so no data leaks occur. Topic filtering ensures the model only answers questions related to company documentation. Rate limiting is applied at the API level.
The architecture is universal: it can be adapted for customer support, a consultant on a website, or an AI agent inside your own ERP.
You can learn more about how we did this here.
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Atom makes electric cars. The company is growing fast, and at some point, off-the-shelf HR systems stopped providing the flexibility they needed – too rigid, impossible to tailor to their own processes. So Atom decided to build their own platform. We had already worked with them on the frontend for a concept car, so the client came back to us.
The stack is classic: backend on Ruby on Rails, frontend on React, Postgres, and RestAPI. This allows us to launch the product quickly and develop it with a small team. The system consists of five modules. Employee evaluations are based on criteria or open-ended questions, involving managers, peers, and the employees themselves. For managers, we built aggregated questionnaires, and each employee has a page showing a competency matrix and growth areas.
Goal setting works with KPIs and OKR decomposition. The process is asynchronous – employees don't have to wait for their manager. The system automatically generates development plans based on evaluations. The employee profile brings everything together: financial information, start date, tenure, and evaluation results. The company structure module stores all reporting relationships and is integrated with 1C and the recruitment system.
The design was developed through in-depth interviews with managers. We looked at solutions from other domains and followed a modular approach with a calm visual style. The project is already live and allows Atom to manage HR processes with just a few employees.
If your HR system no longer meets your needs – we'll build one that fits you perfectly.
More details👉 here.
The stack is classic: backend on Ruby on Rails, frontend on React, Postgres, and RestAPI. This allows us to launch the product quickly and develop it with a small team. The system consists of five modules. Employee evaluations are based on criteria or open-ended questions, involving managers, peers, and the employees themselves. For managers, we built aggregated questionnaires, and each employee has a page showing a competency matrix and growth areas.
Goal setting works with KPIs and OKR decomposition. The process is asynchronous – employees don't have to wait for their manager. The system automatically generates development plans based on evaluations. The employee profile brings everything together: financial information, start date, tenure, and evaluation results. The company structure module stores all reporting relationships and is integrated with 1C and the recruitment system.
The design was developed through in-depth interviews with managers. We looked at solutions from other domains and followed a modular approach with a calm visual style. The project is already live and allows Atom to manage HR processes with just a few employees.
If your HR system no longer meets your needs – we'll build one that fits you perfectly.
More details
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If you've worked with large Django projects, you probably know how quickly things can get hard to navigate.
How to get rid of the pain in large Django projects? Evrone developer Pavel Kutsenko decided to explore this and built next•dj.
Here's how he explains the problem and the solution:
Pavel started the experiment in the summer of 2025. In March of this year, versions 0.4.0 and 0.4.1 were released. The key update is the component system – a reusable UI block now lives in one place, with props, slots, and dependency injection. They also added a global NEXT_FRAMEWORK configuration, similar to DRF, keeping all settings in a single place.
The project is already on GitHub. It's a layer on top of Django that reflects this approach and might be useful if you've run into similar issues.
Come try it, fork it. More details on our website: https://evrone.com/blog/next-dj
How to get rid of the pain in large Django projects? Evrone developer Pavel Kutsenko decided to explore this and built next•dj.
Here's how he explains the problem and the solution:
"The real pain shows up on large projects – too many views, templates and handler functions scattered across different locations, and maintaining a 100k+ codebase becomes a nightmare. My project next•dj is built around file-routing architecture, where each block has its own URL prefix (on large projects, you sometimes have a microfrontend for different parts of the site). It includes static files (version 0.5.0 introduced the ability to store and process them right next to the page and component), form logic, templates, and context handler functions – what else do you need to be happy?"
Pavel started the experiment in the summer of 2025. In March of this year, versions 0.4.0 and 0.4.1 were released. The key update is the component system – a reusable UI block now lives in one place, with props, slots, and dependency injection. They also added a global NEXT_FRAMEWORK configuration, similar to DRF, keeping all settings in a single place.
The project is already on GitHub. It's a layer on top of Django that reflects this approach and might be useful if you've run into similar issues.
Come try it, fork it. More details on our website: https://evrone.com/blog/next-dj
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Viju is a streaming service. We designed its interface.
The main problem: users spend too much time choosing what to watch. They scroll, get tired, and leave. Not because the content is bad, but because the interface doesn't help.
Together with the product team, we decided to try an unconventional approach. Instead of standard genres – short looped video fragments of films, which we called "kinoms". In a couple of seconds, viewers grasp the atmosphere and decide whether they want to watch. No spoilers.
These fragments can be used to create playlists not by genre but by emotion. For example, "Autumn and rain outside the window". This makes it possible to connect films that are far apart in genre but fit a certain mood.
Before prototyping, we built functionality maps (mind maps) across three dimensions: the structure of sections, the UI/UX of those sections, and their information content. Then we created "gray" prototypes to test scenarios before starting design and development. This helped us drop complex or unimportant features early on.
We developed a unified design system: modular grids, typography, buttons, inputs, colors, dimensions. We took into account the specifics of each platform. For Smart TV – remote control navigation and its own guidelines. For mobile devices – differences across iOS and Android versions. For browsers – a separate mobile version, since that's where the main flow of advertising traffic comes from.
We tested with dozens of people who were already used to other streaming services. We implemented emotional tags, collected feedback, and prioritized the solutions.
If you need an interface that helps retain users instead of putting obstacles in their way – reach out. We'll tell you how we designed the design system for Viju and think about how we can help with your project.
More details on this case: https://evrone.com/cases/viasat-design
The main problem: users spend too much time choosing what to watch. They scroll, get tired, and leave. Not because the content is bad, but because the interface doesn't help.
Together with the product team, we decided to try an unconventional approach. Instead of standard genres – short looped video fragments of films, which we called "kinoms". In a couple of seconds, viewers grasp the atmosphere and decide whether they want to watch. No spoilers.
These fragments can be used to create playlists not by genre but by emotion. For example, "Autumn and rain outside the window". This makes it possible to connect films that are far apart in genre but fit a certain mood.
Before prototyping, we built functionality maps (mind maps) across three dimensions: the structure of sections, the UI/UX of those sections, and their information content. Then we created "gray" prototypes to test scenarios before starting design and development. This helped us drop complex or unimportant features early on.
We developed a unified design system: modular grids, typography, buttons, inputs, colors, dimensions. We took into account the specifics of each platform. For Smart TV – remote control navigation and its own guidelines. For mobile devices – differences across iOS and Android versions. For browsers – a separate mobile version, since that's where the main flow of advertising traffic comes from.
We tested with dozens of people who were already used to other streaming services. We implemented emotional tags, collected feedback, and prioritized the solutions.
If you need an interface that helps retain users instead of putting obstacles in their way – reach out. We'll tell you how we designed the design system for Viju and think about how we can help with your project.
More details on this case: https://evrone.com/cases/viasat-design
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Migrating from Ruby to Go: pros, cons, and how to get it right
A Ruby project worked steadily for years, but now the load is growing, response time is increasing, and colleagues are advising a switch to Go.
Let's explore this with Alexander Kirillov, a DevOps expert and mentor in the Evrone DevOps internship program.
What Go offers. Compilation to machine code – high performance under load. Goroutines simplify parallel computing, unlike Ruby with its GIL. Static typing catches errors at compile time. The final binary includes all dependencies – no runtime environment or gem management needed.
The downsides. The Ruby ecosystem (Rails, Active Record, gems) is significantly richer. Many things in Go would have to be written from scratch. Strict rules: an unused variable will prevent compilation. No classes or inheritance – only structs and interfaces. Migrating a large project takes months, plus retraining the team.
How to decide. Assess the real problems. If performance is no longer satisfactory – Go might help. If everything runs smoothly, the switch may not be worth the effort. The best approach: rewrite one microservice in Go first, rather than starting a revolution.
Want to try Go but unsure? We can help with migration – from a test prototype to a full project rewrite.
More details in the article: https://evrone.ru/blog/articles/from-ruby-to-go
A Ruby project worked steadily for years, but now the load is growing, response time is increasing, and colleagues are advising a switch to Go.
Let's explore this with Alexander Kirillov, a DevOps expert and mentor in the Evrone DevOps internship program.
What Go offers. Compilation to machine code – high performance under load. Goroutines simplify parallel computing, unlike Ruby with its GIL. Static typing catches errors at compile time. The final binary includes all dependencies – no runtime environment or gem management needed.
The downsides. The Ruby ecosystem (Rails, Active Record, gems) is significantly richer. Many things in Go would have to be written from scratch. Strict rules: an unused variable will prevent compilation. No classes or inheritance – only structs and interfaces. Migrating a large project takes months, plus retraining the team.
How to decide. Assess the real problems. If performance is no longer satisfactory – Go might help. If everything runs smoothly, the switch may not be worth the effort. The best approach: rewrite one microservice in Go first, rather than starting a revolution.
Want to try Go but unsure? We can help with migration – from a test prototype to a full project rewrite.
More details in the article: https://evrone.ru/blog/articles/from-ruby-to-go
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SberMegaMarket – 5 million products, 8 thousand sellers. At this scale, minor architectural flaws turn into real pain.
We joined as Go developers, mostly working on tasks for the SEO and advertising departments.
Reviews system.
We didn't just build a comment board – we built an engine with validation. A user writes a review, and the system checks: was it a real purchase? Is the content unique? Does it meet the "helpfulness" criteria? If yes – bonus rubles are credited. Everything is tied to Elasticsearch.
Photo gallery in reviews.
Previously, media was scattered. To see photos of a specific product, you had to dig through all the reviews. We aggregated everything into a single stream and created a unified access point.
Seller dashboard.
Vendors and brand representatives asked for the ability to respond to reviews – it didn't exist. We designed an account with this capability.
Advertising services.
Two of them: a campaign generator for Yandex.Direct and Google Ads, and an XML feed generator (products, prices, categories). Everything connects to external systems via API.
Brand aliases.
A brand can have a dozen spelling variations – Latin, Cyrillic, uppercase, lowercase. Previously, these sat statically in the database with no way to edit them. We built full CRUD – create, read, update, delete.
Stack.
Go (primary language), Elasticsearch (for reviews), Postgres, MS SQL, Redis, Vue.js, Gin, Go kit.
This isn't magic – just solid engineering. When the load is high, small things matter. We know how to handle that.
More technical details in the case study.👉 Link in the post.
We joined as Go developers, mostly working on tasks for the SEO and advertising departments.
Reviews system.
We didn't just build a comment board – we built an engine with validation. A user writes a review, and the system checks: was it a real purchase? Is the content unique? Does it meet the "helpfulness" criteria? If yes – bonus rubles are credited. Everything is tied to Elasticsearch.
Photo gallery in reviews.
Previously, media was scattered. To see photos of a specific product, you had to dig through all the reviews. We aggregated everything into a single stream and created a unified access point.
Seller dashboard.
Vendors and brand representatives asked for the ability to respond to reviews – it didn't exist. We designed an account with this capability.
Advertising services.
Two of them: a campaign generator for Yandex.Direct and Google Ads, and an XML feed generator (products, prices, categories). Everything connects to external systems via API.
Brand aliases.
A brand can have a dozen spelling variations – Latin, Cyrillic, uppercase, lowercase. Previously, these sat statically in the database with no way to edit them. We built full CRUD – create, read, update, delete.
Stack.
Go (primary language), Elasticsearch (for reviews), Postgres, MS SQL, Redis, Vue.js, Gin, Go kit.
This isn't magic – just solid engineering. When the load is high, small things matter. We know how to handle that.
More technical details in the case study.
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There is no single "right" framework in Go. And that's perfectly fine.
Some need a lightweight router with nothing extra. Others need a heavy MVC with ORM, authorization, and code generation. The choice depends not on trends but on the task: microservices, highload, monolith, MVP, or enterprise.
A Go framework doesn't make code "magically better." It just takes care of the routine: routing, JSON handling, validation, logging. That saves development hours and reduces the number of bugs. Plus, a new developer will get up to speed faster if the project uses a popular tool.
Next up – a quick overview of the most common options. What works for which task, how they differ, and where they might shine or just get in the way. A detailed breakdown is available in the article on our website: https://evrone.com/blog/best-golang-frameworks-2025
Some need a lightweight router with nothing extra. Others need a heavy MVC with ORM, authorization, and code generation. The choice depends not on trends but on the task: microservices, highload, monolith, MVP, or enterprise.
A Go framework doesn't make code "magically better." It just takes care of the routine: routing, JSON handling, validation, logging. That saves development hours and reduces the number of bugs. Plus, a new developer will get up to speed faster if the project uses a popular tool.
Next up – a quick overview of the most common options. What works for which task, how they differ, and where they might shine or just get in the way. A detailed breakdown is available in the article on our website: https://evrone.com/blog/best-golang-frameworks-2025
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