Why Monoliths Are Dying: Embracing Microservices, Serverless, and Meta‑Frameworks

The Rise and Fall of Monoliths

Decades of rapid change have shifted developers from simple monolithic codebases—often a mix of HTML/CSS front‑ends and Java back‑ends—to distributed microservice architectures.

Why Adopt Microservices?

Smaller services are easier to maintain.

Team friction is reduced; each team can own a service.

Development speed increases without heavyweight legacy constraints.

Teams can pick the best tool for each task (e.g., Node.js for JSON, Rust for performance, Ruby for Ruby‑only teams).

Cognitive load drops because developers only need to understand a subset of the code.

Common Misconceptions About Microservices

Code duplication : Shared logic may appear in multiple repositories, causing divergence.

Complex transaction handling : Coordinating transactions across services often requires patterns like Saga or event sourcing.

Increased cognitive load : Developers must understand not only their service but also its interactions.

Higher failure surface : Issues such as database connections, network latency, caching, or exceptions become more prevalent.

Ultimately, architectural choices depend on the specific context.

Balancing Monoliths and Microservices

A well‑designed, highly decoupled system can be broken into four parts:

UI (front‑end)

BFF (Backend For Frontend)

Traditional backend (Backend For Database, BFD)

Database

Typical technology stack for this model includes:

Front‑end frameworks (Angular, React, Vue, Svelte, etc.)

BFF implemented with a suitable technology (REST API, GraphQL on Node.js, etc.)

Traditional backend (REST API or high‑performance gRPC server)

One or more databases (relational, document, graph, or search engines)

Guidelines for sizing each part:

At least one front‑end; can scale arbitrarily.

One front‑end typically maps to one BFF.

One traditional backend can be split into N microservices, or remain a single monolith.

At least one instance of each database type; a medium‑scale app might need three different databases. N = (2 * UI) + (1 * BFD) + (3 * DB) The goal is to keep the total component count (N) as low as possible.

Enter the Serverless Monolith Era

Rise of Front‑End Meta‑Frameworks

Frameworks such as Next.js, Remix, and SvelteKit combine UI and BFF into a single codebase, enabling serverless deployment on modern cloud platforms.

N = META-FRAMEWORK + (1 * BFD) + (3 * DB)

This reduces the front‑end technology count by one.

Serverless Database Era

Database‑as‑a‑Service (DaaS) and Backend‑as‑a‑Service (BaaS) solutions like Firebase and Supabase aim to eliminate backend code, letting developers write queries directly in the BFF.

Firebase’s limitations include:

It is a single‑model document database.

It only supports one‑directional graph traversal.

Supabase offers a PostgreSQL‑based relational alternative but remains single‑model.

SurrealDB introduces a true multi‑model database with its own query language, often referred to as a “meta‑database.”

N = META-FRAMEWORK + META-DATABASE

This further reduces the overall technology count.

Additional Tips: Single‑Repository Architecture

Like microservices, a monolithic approach benefits from the right toolbox to address common constraints such as large failure domains, long build times, and limited cross‑team isolation.

Tools like turborepo can help manage a single repository containing front‑end, back‑end, and database migration scripts, keeping the codebase clean and maintainable.