These are the main approaches when building real-time applications:

1. Tinybird (real-time analytics backend platform)
2. Socket.io / WebSocket frameworks (connection management)
3. Apache Kafka + Stream Processing (event-driven architecture)
4. Firebase / Supabase (Backend-as-a-Service with real-time)
5. Ably / Pusher (managed real-time messaging infrastructure)
6. Custom stack (event bus + CDC + serving layer)
7. GraphQL Subscriptions (real-time data over GraphQL)

Building a real-time app requires architecture where data flows with low latency, maintains consistency under failures, and scales without concurrent connections destroying your backend. Real-time applications combine three layers: client transport (WebSockets, SSE), event backbone (Kafka, NATS, Redis Streams), and data serving layer for fast queries.

They solve user experience through immediate updates. For many teams, they also create new operational problems when assembling infrastructure from components.

Here's what actually happens: You need to build a real-time application. You evaluate approaches and start with WebSocket frameworks because they promise persistent bidirectional connections for pushing updates to clients.

So you implement WebSocket server with Socket.io or similar framework. Configure connection handling, reconnection logic, and room/namespace management. Build event emitters broadcasting updates to connected clients. Design authentication and authorization for WebSocket connections.

Six months later, you have working real-time connections pushing updates to browsers. You also discover several painful realities:

Connection scaling complexity—sticky sessions complicate load balancing; thousands of concurrent connections require careful resource management and connection state externalization.

Event ordering challenges—messages from different sources arrive out of order; maintaining consistency across distributed clients requires partitioning strategies and sequence tracking.

Data freshness versus query performance—pushing raw events works initially; serving aggregated metrics or dashboards requires separate query infrastructure with materialized views or OLAP databases.

State management under failures—WebSocket disconnections lose state; implementing resume-from-last-event requires buffer management and Last-Event-ID patterns.

Backend event coordination—multiple application servers need shared event bus (Kafka, Redis Pub/Sub) to broadcast updates; deploying message broker adds operational complexity.

Multi-tenant isolation—ensuring tenant A never sees tenant B's events requires careful room management and authorization checks per message.

Someone asks: "Why does our real-time dashboard lag when we have 1000 users?" or "How do we serve aggregated metrics in real-time without expensive queries per user?" The answer reveals what WebSocket frameworks actually provide—connection transport, not complete real-time application infrastructure.

The uncomfortable reality: most teams building real-time apps don't need different WebSocket libraries—they need to separate connection management from data serving and choose platforms purpose-built for their specific real-time pattern.

This article explores approaches to build real-time apps—when WebSocket frameworks solve transport versus when event-driven architectures handle backend complexity, when managed services eliminate infrastructure operations versus when custom stacks provide control, and when your actual requirement is real-time analytics serving rather than general-purpose messaging.

1. Tinybird: When Your Real-Time App Is Really a Real-Time Analytics Backend

Let's start with the fundamental question: are you building a real-time app because you need WebSocket connections, or because you need to serve real-time analytics data at scale?

Most teams building real-time apps have confused connection transport with analytics serving—they need real-time data APIs, not just WebSocket frameworks.

The WebSocket versus analytics serving distinction

Here's the pattern: Your team needs a real-time application. You evaluate WebSocket frameworks because you want live updates in dashboards, operational metrics, or customer analytics.

That's true for the transport layer. WebSockets deliver updates to browsers.

What WebSocket frameworks don't solve:

Real-time data ingestion—WebSockets push to clients; you still need infrastructure ingesting events from Kafka, webhooks, databases, or applications continuously.

Aggregated metrics serving—pushing raw events works initially; serving pre-aggregated dashboards (user counts, revenue metrics, system health) requires separate analytical infrastructure.

Sub-second query latency—real-time dashboards need fast queries on recent data; executing aggregations per WebSocket connection creates backend query storms.

Materialized view management—maintaining pre-computed metrics updated as events arrive requires orchestration beyond WebSocket broadcasting.

Multi-tenant data isolation—ensuring users only see their data requires query-time filtering and authorization, not just connection-level room management.

Scalable data serving—thousands of concurrent dashboard viewers need optimized serving infrastructure, not database queries per WebSocket message.

WebSocket frameworks provide connection transport. They don't provide real-time analytics serving optimized for dashboards and metrics APIs.

One team described their experience: "We built real-time dashboards with Socket.io broadcasting updates. Performance collapsed at 500 concurrent users—backend couldn't handle query load. We needed real-time analytics serving, not just WebSocket connections."

How Tinybird actually solves real-time analytics backends

Tinybird is a real-time analytics platform that provides the backend for real-time applications serving dashboards, metrics, and operational analytics—the data layer powering live updates without WebSocket complexity.

You stream events from Kafka, webhooks, databases, or applications. Tinybird ingests them continuously with schema validation. You write SQL defining metrics and aggregations. Those queries become production APIs with sub-100ms latency serving pre-aggregated data.

No WebSocket server operations. Build frontend consuming JSON APIs with any real-time library (Socket.io for transport) getting data from Tinybird APIs.

Sub-100ms serving latency. Incremental materialized views maintain aggregations updated as data arrives; API requests read pre-computed results.

Real-time data freshness is essential for user-facing analytics. Streaming ingestion makes events queryable in milliseconds versus batch processing delays.

Automatic pre-aggregation. Materialized views update incrementally without manual orchestration or cache invalidation logic.

Multi-tenant isolation. API authentication with tenant filtering at query execution versus complex room management per connection.

Scalable concurrent serving. Columnar storage and vectorized execution handle thousands of concurrent API requests efficiently.

One team using both explained: "Socket.io handles WebSocket connections to browsers. Tinybird APIs provide the real-time data—aggregated metrics, filtered by tenant, served in milliseconds. WebSockets push updates; Tinybird delivers the data to push. Separating transport from serving delivered 10x better results."

The architectural difference

WebSocket framework approach: Manage persistent connections, broadcast messages, handle reconnection. Build separate infrastructure for data ingestion, aggregation, and serving to populate messages sent through WebSockets.

Tinybird approach: Real-time analytics APIs serving pre-aggregated data continuously updated from streaming sources. Frontend uses any transport (WebSockets, SSE, polling) consuming Tinybird APIs.

This matters because building real-time analytics backend is the hard part—ingestion, aggregation, serving at scale. WebSocket transport is commodity once you have data APIs.

When Tinybird Makes Sense for Real-Time Apps

Consider Tinybird for real-time app backend when:

• Your app is real-time dashboards, operational metrics, or customer analytics versus chat or collaboration
• Aggregated data serving (counts, sums, averages, time-series) is primary requirement
• Sub-second data freshness with streaming ingestion matters for user experience
• Multi-tenant serving at scale requires query-optimized infrastructure
• Separation of concerns—data APIs (Tinybird) versus connection transport (WebSocket library)

Tinybird might not fit if:

• Your app is chat, messaging, or collaboration requiring bidirectional communication patterns
• Raw event streaming to clients without aggregation is sufficient
• Low traffic where database queries per WebSocket connection work fine
• You're building general-purpose real-time infrastructure not analytics-focused

If your real-time app serves aggregated metrics, operational dashboards, or customer analytics, Tinybird provides the backend. For other real-time patterns, different approaches optimize better.

2. Socket.io / WebSocket Frameworks: Connection Management

Socket.io and similar WebSocket frameworks provide the most common starting point for building real-time apps—managed persistent connections with automatic reconnection.

What WebSocket frameworks provide

Socket.io and alternatives deliver connection management with developer-friendly APIs:

Bidirectional communication through WebSocket protocol with HTTP long-polling fallback.

Automatic reconnection with configurable retry strategies and exponential backoff.

Rooms and namespaces for organizing connections and broadcasting to subsets.

Event-based messaging with typed events versus raw WebSocket frames.

Authentication integration through middleware and connection handshake.

Broadcasting patterns sending messages to all clients, specific rooms, or individual connections.

The infrastructure complexity beyond connections

WebSocket frameworks solve transport while leaving backend architecture to you:

Event source coordination—multiple application servers need shared message bus (Redis Pub/Sub, Kafka) for cross-server broadcasting.

Connection state management—sticky sessions or externalized state required for horizontal scaling.

Data serving infrastructure—queries, aggregations, and materialized views separate from connection management.

Backpressure handling—slow clients accumulate buffers; requires connection throttling or disconnection.

Message ordering—distributed systems can deliver messages out of order requiring sequence numbers or partitioning.

When Socket.io makes sense for real-time apps

Choose Socket.io or WebSocket frameworks when:

• Bidirectional communication (chat, collaboration, gaming) is core requirement
• Connection management with rooms and broadcasting simplifies architecture
• Event-based messaging provides cleaner API than raw WebSocket handling
• You're building complete backend separately—event bus, data serving, aggregations

Socket.io solves connection transport. It doesn't eliminate backend complexity—event sourcing, data aggregation, serving optimization.

3. Apache Kafka + Stream Processing: Event-Driven Architecture

Apache Kafka with stream processing (Flink, Kafka Streams, ksqlDB) represents event-driven architecture for real-time apps—events as system backbone.

What Kafka + stream processing provides

Kafka delivers distributed event streaming with processing capabilities:

Partitioned topics enabling parallel processing while maintaining order per partition key.

Event persistence with configurable retention for replay and recovery.

Stream processing through Flink (stateful computations), Kafka Streams (embedded processing), or ksqlDB (SQL on streams).

Exactly-once semantics (with configuration) for transactional event processing.

Change Data Capture (Debezium) capturing database changes as events without dual writes.

Consumer groups distributing processing across instances with automatic rebalancing.

The operational complexity trade-off

Kafka as real-time app foundation provides event-driven power with infrastructure burden:

Cluster operations—ZooKeeper/KRaft, broker management, topic configuration, partition rebalancing.

Partition key design—determines ordering guarantees and parallelism; wrong keys break ordering or create hot partitions.

Schema evolution—managing event contracts (Avro, Protobuf) across producer/consumer versions.

State management—stream processing with Flink requires state backends, checkpointing, and recovery strategies.

Serving layer separate—Kafka stores and processes events; you still need databases or APIs for query serving.

When Kafka makes sense for real-time apps

Choose Kafka + stream processing when:

• Event-driven architecture with multiple consumers reacting to same events
• Stream processing (windowing, joins, aggregations) required for real-time computation
• Event sourcing patterns where events are source of truth
• High-throughput event ingestion (millions of events/second) justifies operational complexity
• Replay capability for reprocessing historical events matters

Kafka solves event backbone. It doesn't eliminate serving layer—APIs, dashboards, queries require additional infrastructure.

In event-driven architectures, Kafka often sits between upstream producers and the downstream system, ensuring ordered delivery and reliable processing before data reaches serving layers or analytics platforms.

4. Firebase / Supabase: Backend-as-a-Service with Real-Time

Firebase and Supabase provide Backend-as-a-Service with built-in real-time capabilities—managed databases with automatic client synchronization.

What BaaS platforms provide for real-time

Firebase and Supabase deliver integrated backend with real-time features:

Real-time database syncing data automatically to connected clients when data changes.

Authentication integrated with real-time subscriptions and security rules.

Client SDKs (JavaScript, iOS, Android) handling connection management, offline support, and synchronization.

Security rules defining read/write permissions at document/row level.

Serverless functions for backend logic triggered by data changes or HTTP requests.

Managed infrastructure eliminating database operations, scaling, and connection handling.

The BaaS limitations for complex real-time

BaaS platforms optimize rapid development with architectural constraints:

Document/relational model—Firebase NoSQL or Supabase PostgreSQL; complex aggregations require client-side computation or Cloud Functions.

Client-side queries—real-time subscriptions return raw data; pre-aggregation requires Functions or separate processing.

Cost at scale—charged per operation, bandwidth, or concurrent connections; high-traffic apps can accumulate costs.

Limited stream processing—no built-in Kafka-style event processing for complex transformations.

Vendor lock-in—platform-specific APIs and architectures complicate migration.

When BaaS makes sense for real-time apps

Choose Firebase or Supabase when:

• Rapid prototyping and quick time-to-market matter more than infrastructure control
• Simple real-time sync—documents, chat messages, presence—versus complex analytics
• Small to medium scale—hundreds to thousands of concurrent users versus millions
• Mobile/web apps where client SDKs and offline support provide value
• Startup phase before custom infrastructure investment justified

BaaS solves simple real-time sync. It doesn't optimize for complex analytics, high-scale serving, or custom architectures.

5. Ably / Pusher: Managed Real-Time Messaging Infrastructure

Ably and Pusher provide managed real-time messaging as infrastructure service—WebSocket connections, pub/sub, and presence managed for you.

What managed real-time services provide

Ably and Pusher deliver real-time infrastructure without self-hosting:

Global edge network within modern cloud computing environments terminating WebSocket connections close to users, reducing latency.

Pub/Sub channels with subscription management, authentication, and authorization.

Presence tracking which users are online in channels.

Message history storing recent messages for clients connecting late.

Webhooks notifying your backend of channel events (subscriptions, messages).

Client libraries for web, mobile, and server with automatic reconnection and fallback transports.

Guaranteed delivery with message queuing when clients temporarily disconnect.

The managed service trade-offs

Managed real-time platforms provide operational simplicity with cost and control limitations:

Consumption-based pricing—charged per connection, message, or bandwidth; costs scale with usage.

Limited customization—platform handles connections and routing; custom logic requires webhooks to your backend.

Backend data serving separate—Ably/Pusher transport messages; you build data aggregation and serving infrastructure.

Vendor dependency—migration requires reimplementing real-time infrastructure if leaving platform.

When Ably/Pusher makes sense for real-time apps

Choose managed real-time services when:

• Operational simplicity preferred over managing WebSocket infrastructure
• Global distribution with edge presence matters for user experience
• Predictable pricing at your scale acceptable versus self-hosted costs
• Core competency is application logic, not real-time infrastructure operations
• Backend data serving handled separately (databases, APIs, analytics platforms)

Managed services solve connection infrastructure. They don't eliminate backend complexity—event processing, data aggregation, query serving.

6. Custom Stack: Event Bus + CDC + Serving Layer

Custom architecture assembling event bus, change data capture, and serving layer represents maximum control approach.

What custom stack provides

Building from components delivers tailored architecture:

Event bus (Kafka, NATS, Redis Streams) chosen for specific requirements (throughput, persistence, ordering).

CDC tools (Debezium) capturing real-time change data capture events from databases without dual writes.

Stream processing (Flink, custom services) for transformations, aggregations, windowing.

Serving layer (PostgreSQL, ClickHouse®, Tinybird) optimized for query patterns.

Connection layer (custom WebSocket servers, SSE endpoints) designed for your specific transport needs.

Complete control over architecture, technology choices, and optimization strategies.

The engineering investment reality

Custom stacks provide flexibility with development and operational burden:

Backend infrastructure—deploying and managing event bus, stream processors, databases, and connection servers.

Integration complexity—connecting components, ensuring message flow, handling failures across systems.

Operational expertise—monitoring distributed systems, debugging event flows, managing state and recovery.

Development time—months to build what managed services or platforms provide integrated.

Ongoing maintenance—upgrades, security patches, performance tuning across multiple systems.

When custom stack makes sense

Choose custom architecture when:

• Specific requirements unavailable in existing platforms or managed services
• Scale demands justify engineering investment in optimized infrastructure
• Engineering expertise available to build and operate distributed real-time systems
• Control over costs through infrastructure optimization versus platform pricing
• Competitive advantage through custom real-time capabilities

Custom stacks solve maximum flexibility. They require significant engineering investment versus leveraging platforms or managed services.

7. GraphQL Subscriptions: Real-Time Data Over GraphQL

GraphQL Subscriptions provide real-time queries using GraphQL protocol—extending GraphQL beyond request/response to event streams.

What GraphQL Subscriptions provide

GraphQL Subscriptions deliver real-time GraphQL with familiar API patterns:

Subscription operations alongside queries and mutations in GraphQL schema.

Event-driven updates pushing changed data to subscribed clients.

Type safety with GraphQL schema defining subscription payloads.

Filtering through GraphQL arguments limiting events clients receive.

Multiple transports typically WebSockets but also SSE or other protocols.

Apollo Server / Relay and other frameworks providing subscription implementations.

The GraphQL-specific considerations

GraphQL Subscriptions as real-time solution provide API consistency with implementation complexity:

Backend event sources—subscriptions need pub/sub infrastructure (Redis, Kafka) triggering updates.

N+1 query problems—subscriptions executing database queries per event can create performance issues.

Complexity over REST+SSE—GraphQL subscriptions add protocol complexity versus simple SSE endpoints.

Scaling challenges—subscription state management across servers requires coordination.

Limited to GraphQL apps—only valuable if already using GraphQL for queries/mutations.

When GraphQL Subscriptions make sense

Choose GraphQL Subscriptions when:

• GraphQL API already exists and consistency across queries/mutations/subscriptions matters
• Type safety and schema validation provide value for real-time data
• Complex data requirements—GraphQL selection sets define exactly what data to push
• Engineering team has GraphQL expertise and infrastructure

GraphQL Subscriptions solve real-time within GraphQL ecosystems. They don't eliminate backend event infrastructure or serving optimization.

Decision Framework: Choosing How to Build Real-Time App

Start with real-time pattern

Real-time analytics serving? Tinybird provides complete backend for dashboards and metrics APIs.

Chat or collaboration? WebSocket frameworks (Socket.io) or managed services (Ably, Pusher) handle messaging.

Event-driven architecture? Kafka + stream processing for complex event flows and processing.

Rapid prototyping? BaaS (Firebase, Supabase) accelerates development for simple real-time sync.

GraphQL ecosystem? GraphQL Subscriptions extend existing GraphQL APIs with real-time.

Evaluate operational capabilities

Engineering resources limited? Managed services (Ably, Pusher, BaaS) or platforms (Tinybird) reduce operational burden.

Have distributed systems expertise? Custom stacks or Kafka architectures leverage existing knowledge.

Prefer zero infrastructure? BaaS platforms eliminate all backend operations.

Need maximum control? Custom architectures provide flexibility with engineering investment.

Consider scale requirements

Thousands of concurrent connections? Most approaches handle this scale with proper architecture.

Millions of events/second? Kafka + stream processing designed for high throughput.

Complex aggregations? Real-time analytics platforms (Tinybird) optimize serving versus query-per-connection.

Global distribution? Managed services (Ably, Pusher) provide edge networks; custom requires CDN and geo-distribution.

Calculate total cost honestly

Include:

Platform fees (managed services, BaaS) or infrastructure costs (self-hosted Kafka, databases).

Engineering time for development, integration, and ongoing operations.

Operational overhead—monitoring, incident response, performance tuning.

Opportunity cost of building infrastructure versus product features.

A platform costing 3x self-hosted infrastructure might deliver 10x faster with 1/5 engineering effort—lower total cost.

For teams building dashboards or performance monitoring systems, platforms like Tinybird can even serve as a Google Analytics alternative, providing real-time metrics and data APIs without the latency of traditional analytics tools.

Frequently Asked Questions (FAQs)

What's the difference between WebSocket frameworks and real-time platforms?

WebSocket frameworks (Socket.io) manage persistent connections and message broadcasting—transport layer. Real-time platforms (Tinybird for analytics, Firebase for sync) provide complete backend including data ingestion, processing, and serving. Choose frameworks when building custom backend; choose platforms when backend complexity justifies integrated solution.

Do I need Kafka to build real-time apps?

Not always—Kafka excels at high-throughput event streaming and complex processing but adds operational complexity. For simpler real-time apps (dashboards, notifications, presence), lighter solutions (Redis Streams, NATS, managed services) often sufficient. Use Kafka when event-driven architecture, stream processing, or massive scale justified.

Can I use Tinybird with Socket.io?

Yes—excellent combination. Socket.io handles WebSocket connections to browsers. Tinybird provides real-time analytics APIs serving aggregated data. Frontend polls Tinybird APIs (or uses webhooks triggering Socket.io broadcasts) getting fresh metrics to push through WebSocket connections. Separating transport (Socket.io) from data serving (Tinybird) delivers better architecture.

How do I handle WebSocket scaling?

Avoid sticky sessions—externalize connection state to Redis or database allowing any server to handle connections. Use pub/sub (Redis, Kafka) for cross-server message broadcasting. Consider managed services (Ably, Pusher) eliminating scaling complexity. For high scale, implement connection pooling and backpressure handling.

What about real-time without WebSockets?

Server-Sent Events (SSE) provide simpler alternative for server-to-client updates with automatic reconnection and Last-Event-ID resume. Polling works for lower-frequency updates. GraphQL Subscriptions over WebSockets if using GraphQL. Choose based on bidirectional needs (WebSockets) versus unidirectional (SSE) and operational complexity tolerance.

Should I build custom or use managed services?

Depends on scale and engineering resources. Managed services (Ably, Pusher, BaaS) accelerate development and reduce operations—choose when connection management isn't competitive advantage. Custom stacks provide control and cost optimization at scale—choose when engineering expertise available and requirements justify investment.

How does real-time analytics differ from chat?

Real-time analytics serves aggregated metrics, dashboards, operational data—requires data ingestion, materialized views, query optimization (Tinybird). Chat delivers individual messages between users—requires connection management, presence, message history (Socket.io, managed services). Different patterns need different architectures.

Most teams building real-time apps discover they're solving different problems.

The question isn't "which WebSocket framework is best?" The question is "what real-time pattern am I building and what backend infrastructure does it require?"

If your requirement is real-time analytics serving (dashboards, metrics, operational data):

Tinybird solves complete backend—streaming ingestion, incremental materialized views, instant APIs, sub-100ms serving—without building data infrastructure.

If your requirement is bidirectional messaging (chat, collaboration, gaming):

Socket.io for self-managed connections. Ably/Pusher for managed infrastructure. Both require building backend event coordination and data serving separately.

If your requirement is event-driven architecture:

Kafka + stream processing provides distributed event backbone with complex processing capabilities requiring operational expertise.

If your requirement is rapid prototyping:

Firebase/Supabase BaaS platforms integrate authentication, database, and real-time sync eliminating infrastructure decisions initially.

If your requirement is GraphQL consistency:

GraphQL Subscriptions extend existing GraphQL APIs with real-time while requiring backend pub/sub infrastructure.

For maximum control:

Custom stacks assembling event bus, CDC, stream processing, and serving layers provide flexibility with significant engineering investment.

The right approach to build real-time app isn't the most popular framework or managed service. It's matching your specific real-time pattern—analytics serving, messaging, event processing, data sync—with architectures purpose-built for those requirements.

Choose based on what you're actually building: real-time analytics needs data platforms optimized for serving aggregations at scale. Real-time messaging needs connection infrastructure with pub/sub coordination. Don't force single solution to solve all real-time patterns—they have different architectural requirements and optimal tools.

Remember that "real-time" isn't single technology decision—it's architectural pattern requiring choices across transport (WebSockets, SSE), event backbone (Kafka, Redis, managed services), and data serving (databases, analytics platforms, custom APIs). Separate concerns and choose best tool for each layer rather than one-size-fits-all approach.