WebSocket hosting is the practice of running applications that keep persistent, two-way connections open between clients and servers.
For developers building real-time applications, WebSocket hosting changes the way infrastructure behaves. A normal HTTP request connects, receives a response, and ends. A WebSocket connection can stay open while the browser and server exchange messages whenever either side has something to send. Raff Technologies gives small teams full root access, Docker-ready Linux VMs, unmetered bandwidth, NVMe SSD storage, and fast deployment, which makes it practical to start with a simple WebSocket server and scale only when connection pressure becomes real. Raff’s Linux VM page lists deployment in under 60 seconds, 9 Linux distributions, full root access, unmetered bandwidth, and plans from $3.99/month. Raff Linux VM
This guide belongs in Raff’s real-time application and infrastructure planning cluster. Raff already covers reverse proxies, load balancers, single-server vs multi-server architecture, auto-scaling, background work models, and performance bottlenecks. This guide focuses on the missing decision: when WebSockets are the right communication model, what makes WebSocket hosting different, and how persistent connections affect scaling and cost.
WebSockets Change the Shape of Server Load
The WebSocket API allows a browser and server to open a two-way interactive communication session, so the client can send messages and receive server responses without repeatedly polling for updates. MDN WebSocket API
That sounds like a small technical detail, but it changes the hosting model.
With normal HTTP, server load is mostly about request rate, response time, CPU, database work, and bandwidth. With WebSockets, you still care about those things, but you also care about how many clients stay connected at the same time.
A WebSocket application can have low request volume but high connection count. For example, a live dashboard might have 5,000 users connected, even if each user receives only a few updates per minute. A chat app might have fewer users but more frequent messages. A multiplayer game might have lower payload size but stricter latency needs.
| Hosting metric | Why it matters for WebSockets |
|---|---|
| Concurrent connections | Each connected client consumes server resources |
| Message rate | Frequent messages increase CPU and network pressure |
| Message size | Larger payloads increase bandwidth and memory pressure |
| Connection duration | Long sessions make capacity planning different from HTTP |
| Latency | Real-time apps feel broken when messages arrive late |
| State handling | Multi-server WebSocket apps need shared session or message state |
| Reconnection behavior | Many clients reconnecting at once can create traffic spikes |
The main lesson: WebSocket hosting is not only about serving requests; it is about managing live connections.
WebSockets Are Not Always the Right Real-Time Model
WebSockets are powerful, but they are not the only way to deliver live updates.
Some applications can use normal HTTP polling. Some can use long polling. Some only need Server-Sent Events. Some need WebSockets because both client and server must send messages at any time.
The WebSocket protocol, defined in RFC 6455, provides full-duplex communication over a single TCP connection. Its relationship to HTTP is mainly the opening handshake, where an HTTP request upgrades into the WebSocket protocol. RFC 6455
That full-duplex behavior is useful, but it also adds operational responsibility.
| Communication model | Best for | Watch out for |
|---|---|---|
| HTTP polling | Simple periodic checks | Wasteful at high frequency |
| Long polling | Occasional updates with broad compatibility | More request overhead than WebSockets |
| Server-Sent Events | Server-to-client updates like feeds or dashboards | One-way from server to browser |
| WebSockets | Two-way real-time communication | Persistent connection scaling and state management |
| WebRTC data channels | Peer-to-peer low-latency data | More complex networking and signaling |
The decision should start with direction and frequency.
If the server only needs to push updates to the browser, Server-Sent Events may be enough. If the client and server both need to send messages frequently, WebSockets are usually a better fit. If the update frequency is low, polling may be simpler and cheaper to operate.
The WebSocket Hosting Decision Framework
Use this framework to decide whether WebSocket hosting fits your application and how much infrastructure planning it needs.
| Workload pattern | Best model | Why it fits | Hosting concern |
|---|---|---|---|
| Live dashboard with frequent updates | WebSockets or SSE | Users need fresh state without refreshing | Connection count and update fan-out |
| Chat application | WebSockets | Both sides send messages in real time | Presence, message delivery, reconnection |
| Multiplayer game | WebSockets or custom real-time protocol | Low-latency interaction matters | Latency, regional hosting, tick/update rate |
| Notification bell | SSE or polling | Mostly server-to-client updates | Simpler than full WebSockets |
| Stock, crypto, or metrics stream | WebSockets | Continuous updates and low latency | Message rate and bandwidth |
| Collaborative editor | WebSockets | Multiple users need shared live state | State synchronization and conflict handling |
| Background job status page | SSE, polling, or WebSockets | Depends on update frequency | Avoid overbuilding if updates are rare |
| Webhook receiver | HTTP | External systems send discrete events | WebSockets are usually unnecessary |
A practical rule: use WebSockets when bidirectional, low-latency, persistent communication is central to the product experience.
If real-time behavior is only a small convenience, simpler models may be easier to host and maintain.
Persistent Connections Change Capacity Planning
A WebSocket server must keep many connections alive at the same time.
That does not automatically mean WebSockets are expensive. An idle WebSocket connection may use very little CPU. But each connection still consumes memory, file descriptors, event loop capacity, and network tracking. When message volume rises, CPU and bandwidth can grow quickly.
The hosting cost depends on five main variables:
| Variable | Why it affects cost |
|---|---|
| Concurrent users | More open connections require more server capacity |
| Messages per second | Higher message rate increases CPU and network usage |
| Payload size | Larger messages increase bandwidth and memory pressure |
| Fan-out pattern | One message sent to many clients multiplies work |
| Connection lifetime | Long-lived sessions require stable process and network handling |
Example: 1,000 connected users receiving one small update per minute is a very different workload from 1,000 connected users receiving 20 updates per second.
The user count is the same. The cost is not.
This is why WebSocket pricing and sizing should not be based only on “monthly visitors.” It should be based on active connections, message frequency, payload size, and uptime expectations.
Reverse Proxies Need WebSocket Awareness
Many WebSocket applications sit behind a reverse proxy such as Nginx, Caddy, Traefik, or another edge layer.
That reverse proxy may handle TLS, host routing, path routing, request filtering, and forwarding traffic to the application process. Raff already has a guide explaining the difference between a reverse proxy and a load balancer, including TLS termination, request routing, and when each layer matters. Reverse Proxy vs Load Balancer
WebSockets need special attention because the connection starts as HTTP and then upgrades. Nginx’s WebSocket proxy documentation explains that WebSocket proxying uses the HTTP/1.1 protocol switch mechanism, and that the Upgrade header is hop-by-hop, so it must be handled correctly between proxy and upstream. Nginx WebSocket Proxying
For a decision-stage guide, the important point is not the exact configuration. It is that WebSocket hosting needs the edge layer to preserve the upgrade behavior and not treat the connection like a short HTTP request.
| Proxy concern | Why it matters |
|---|---|
| HTTP upgrade support | WebSocket traffic must switch protocols correctly |
| Timeouts | Idle connections may be closed too aggressively |
| TLS termination | Secure WebSockets usually use wss:// |
| Path routing | WebSocket endpoints may need separate routing |
| Connection limits | Edge limits can cap concurrency before the app does |
| Load balancing | Multi-node WebSocket apps need connection-aware routing |
A reverse proxy can be enough for a single WebSocket backend. A load balancer becomes more important when several backend servers need to share connection load.
Scaling WebSockets Is Different From Scaling HTTP
HTTP scaling is often stateless. If requests are independent, a load balancer can send each request to any healthy backend.
WebSockets are different because the client holds a live connection to one backend server. Once connected, future messages usually travel through that same connection. That means connection distribution, backend state, and reconnection behavior matter.
Raff’s single-server vs multi-server architecture guide explains that a single server is a common starting point for MVPs and moderate traffic, while multi-server architecture becomes useful when resource contention, scaling ceilings, deployment risk, or availability requirements increase. Single Server vs Multi-Server Architecture
For WebSockets, the scaling path often looks like this:
| Stage | Architecture | When it fits |
|---|---|---|
| Single VM | Web app and WebSocket server on one machine | MVP, internal tool, early product |
| Single VM with reverse proxy | Edge proxy routes HTTP and WebSocket paths | Cleaner TLS and routing |
| App plus worker processes | Background work separated from socket handling | Message processing grows |
| Multiple WebSocket servers | Connections distributed across nodes | Concurrency exceeds one VM |
| Shared message layer | Redis, pub/sub, broker, or database coordinates events | Users on different nodes need shared state |
| Regional or edge-aware design | Traffic moves closer to users | Latency-sensitive workloads |
The main scaling issue is state.
If all connected users are on one server, broadcasting messages is simple. If users are spread across five servers, a message created on one server may need to reach users connected to the others. That usually requires a shared message layer, pub/sub system, broker, or application-level coordination.
Sticky Sessions Are Helpful, But Not a Complete Plan
Sticky sessions mean a load balancer keeps sending the same client to the same backend server.
For WebSockets, this can help because a client’s connection is already tied to a specific server. But sticky sessions do not solve every scaling problem. If one backend receives too many long-lived connections, it can become overloaded. If a backend fails, those clients still need to reconnect somewhere else. If users on different backends need to receive the same event, the application still needs shared state or message fan-out.
| Scaling concern | Sticky sessions help? | Still needed |
|---|---|---|
| Keeping one client on one backend | Yes | Connection health and reconnect handling |
| Sharing events across servers | No | Pub/sub or shared message layer |
| Backend failure recovery | Partly | Reconnect strategy and state recovery |
| Uneven connection load | Partly | Load balancing and capacity monitoring |
| User presence across nodes | No | Shared presence store or coordination |
| Deployments without disconnecting everyone | Partly | Drain strategy and rolling deployment plan |
The practical rule is: sticky sessions help route connections, but shared state helps the application behave correctly.
A small team can start without a complex distributed design. But once WebSocket connections are spread across multiple backends, state must be handled deliberately.
WebSocket Cost Depends on Connections, Messages, and State
WebSocket hosting cost is not only the VM price.
A WebSocket workload can cost more than a normal web app when it needs more memory for connections, more CPU for message handling, more bandwidth for frequent updates, more monitoring, or more servers for availability.
Cost grows through:
| Cost area | WebSocket-specific reason |
|---|---|
| VM size | More connections and messages may need more RAM and CPU |
| Bandwidth | Frequent updates and large payloads increase transfer |
| Reverse proxy / load balancer | Edge routing and multi-node distribution may be needed |
| Shared message layer | Redis, broker, or database coordination may be required |
| Monitoring | Connection count, reconnects, latency, and message rate need visibility |
| Backups | Persistent state still needs protection |
| Development time | Reconnect logic, presence, and fan-out add complexity |
Raff’s cloud server cost guide explains that cloud server pricing should include compute, memory, storage, bandwidth, backups, licensing, and support rather than only the VM price. That same principle applies to WebSocket applications. Cloud Server Cost in 2026
For small teams, the cost question should be:
| Question | Why it matters |
|---|---|
| How many users are connected at once? | Drives memory and connection limits |
| How often do they receive messages? | Drives CPU and bandwidth |
| Are messages small or large? | Drives transfer and serialization cost |
| Do messages need to reach many users? | Drives fan-out complexity |
| Can users reconnect safely? | Affects reliability and deployment strategy |
| Does state need to be shared across nodes? | Adds infrastructure complexity |
A real-time feature that updates once every few minutes may be cheap to run. A real-time feature that streams high-frequency updates to thousands of users is a different infrastructure problem.
Reliability Depends on Reconnection Behavior
Every WebSocket application needs a reconnection story.
Connections can drop because of network changes, browser behavior, mobile devices moving between networks, proxies, server restarts, deployments, timeouts, or backend failures. A good WebSocket system assumes disconnection will happen.
The question is what happens next.
| Reliability concern | Better design question |
|---|---|
| Client disconnects | Can the client reconnect without user confusion? |
| Server restarts | Are clients routed back cleanly? |
| Message missed during reconnect | Does the client need replay, sync, or refresh? |
| Backend deploys | Can connections drain gracefully? |
| Mobile network changes | Does the client retry with backoff? |
| Duplicate messages | Can the app handle repeated events safely? |
For some apps, missing a message is acceptable because the next state update corrects the UI. For others, every event matters. A chat message, payment update, game action, or collaborative edit may need stronger delivery semantics.
This is why WebSocket reliability is partly an application design problem, not only a hosting problem.
Security Still Starts With Normal Web Security
WebSockets do not remove normal web security concerns.
Authentication, authorization, TLS, origin checks, input validation, rate limiting, logging, and abuse protection still matter. In some ways, they matter more because a WebSocket connection can stay open and continue sending messages after the initial handshake.
Security decisions include:
| Security area | WebSocket concern |
|---|---|
| TLS | Use secure WebSockets with wss:// for production |
| Authentication | Decide how clients prove identity before or during connection |
| Authorization | Users should only join rooms or channels they are allowed to access |
| Origin checks | Prevent unwanted browser origins from connecting |
| Rate limits | Limit connection attempts and message frequency |
| Message validation | Treat every inbound message as untrusted input |
| Logging | Record connection errors and suspicious message patterns |
| Abuse handling | Disconnect or throttle clients that misbehave |
Raff’s Cloud Security Fundamentals guide frames security as reducing exposure, controlling access, patching, backups, monitoring, and incident readiness. WebSocket hosting should follow the same discipline. Cloud Security Fundamentals
A WebSocket endpoint is not “just a socket.” It is a public application surface that needs the same security thinking as HTTP routes.
Observability Should Track Connection Health
Traditional web monitoring often focuses on response time, error rate, CPU, RAM, and HTTP status codes. WebSocket applications need additional signals.
Raff’s observability guide explains metrics, logs, and traces as the three signals small teams use to understand system behavior. Observability for Small Teams
For WebSockets, useful signals include:
| Signal | Why it matters |
|---|---|
| Active connections | Shows real-time capacity usage |
| Connection attempts | Reveals traffic spikes or abuse |
| Connection duration | Shows whether sessions are stable |
| Reconnect rate | Reveals network, timeout, or deployment problems |
| Messages per second | Shows application-level load |
| Average message size | Helps estimate bandwidth and memory pressure |
| Send latency | Shows whether updates are delayed |
| Dropped connections | Reveals reliability issues |
| Backend memory use | Shows connection overhead |
| Queue or pub/sub lag | Shows fan-out or shared-state pressure |
The most important operational mistake is waiting until users complain. Real-time apps feel broken quickly when updates lag or connections repeatedly drop. Monitoring should show connection health before support messages arrive.
When a Single VM Is Enough
A single VM can be a good starting point for many WebSocket applications.
This is especially true for MVPs, internal dashboards, private tools, small chat systems, game prototypes, collaborative tools with limited users, and early SaaS features. Starting with one VM keeps the architecture understandable. The team can focus on connection behavior, message design, authentication, monitoring, and product fit before adding distributed complexity.
A single VM is usually enough when:
| Condition | Why it helps |
|---|---|
| Connection count is modest | One server can manage the active sessions |
| Message rate is low to moderate | CPU and bandwidth stay predictable |
| Downtime tolerance exists | Simpler deployment is acceptable |
| State is local or easy to rebuild | No cross-node coordination needed |
| Team is still validating product | Complexity would slow learning |
A single VM becomes risky when WebSocket traffic is central to revenue, connection counts grow, reconnect storms become painful, or downtime affects customers immediately.
The right move is not to scale early by default. It is to know which signal will tell you when one VM is no longer enough.
When WebSocket Apps Need Multi-Node Scaling
A WebSocket app usually needs multi-node scaling when one server can no longer handle the connection count, message rate, reliability requirement, or deployment risk.
Raff’s auto-scaling guide explains that scaling decisions should be based on measurable pressure, including CPU, RAM, disk I/O, latency, traffic patterns, and queue depth. WebSocket apps add active connections, message rate, and reconnect behavior to that list. Auto-Scaling VM Planning
| Scaling trigger | What it suggests |
|---|---|
| Active connections approach safe limit | Add capacity or optimize connection memory |
| Message processing creates CPU pressure | Move work to workers or add servers |
| Broadcasts delay under load | Add pub/sub or fan-out design |
| Deployments disconnect too many users | Use rolling deployments and connection draining |
| One VM is a single point of failure | Add redundant nodes |
| Users are geographically far away | Consider regional strategy or edge delivery |
| Reconnect storms overload server | Add backoff, capacity, and better restart behavior |
The key point: scaling WebSockets is not only “add another server.” It usually also means changing how messages, presence, sessions, and reconnection are coordinated.
How WebSocket Hosting Applies on Raff
Raff gives developers a practical environment for hosting WebSocket workloads because it provides server-level control without forcing a managed application platform.
A small team can start with a Node.js, Go, Python, Elixir, Java, or other WebSocket-capable application on a Linux VM. It can place a reverse proxy in front for TLS and routing, run the application directly or in Docker, and add a shared message layer later if the workload grows. Raff Linux VMs provide full root access, SSH key authentication, Docker-ready infrastructure, NVMe SSD storage, unmetered bandwidth, and deployment in under 60 seconds. Raff Linux VM
A practical Raff path looks like this:
| Stage | WebSocket hosting model |
|---|---|
| Prototype | One Linux VM, app process, simple monitoring |
| Early production | Reverse proxy plus WebSocket backend |
| Growing app | Separate app process, background workers, better observability |
| Multi-node | Load balancing, shared message layer, connection tracking |
| Business-critical | Redundancy, backups, incident response, deployment discipline |
The design rationale is simple: WebSocket hosting should start from the workload, not the architecture trend. If one VM is enough, keep it simple. If connection pressure grows, split the right layer. If messages need to reach users across servers, add shared state deliberately.
Aybars’ practical angle for this guide is that real-time infrastructure should stay boring. A WebSocket app feels impressive to users only when the connection layer is stable, measurable, and easy to reason about.
Common WebSocket Hosting Mistakes
Using WebSockets when polling would be enough.
If updates are rare, polling or Server-Sent Events may be simpler and cheaper to operate.
Ignoring connection count.
Monthly visitors do not tell you enough. Active concurrent connections matter more.
Treating WebSockets like normal HTTP requests.
Persistent connections affect timeouts, memory, process stability, and deployments.
Forgetting reverse proxy behavior.
The edge layer must support HTTP upgrade behavior and avoid closing connections too aggressively.
Scaling without shared state.
Multiple WebSocket servers need a way to coordinate messages, presence, and events.
Not planning reconnect behavior.
Clients will disconnect. A production app should expect it and recover cleanly.
Sending too much data too often.
Payload size and message frequency can turn a small real-time feature into a bandwidth and CPU problem.
Keeping real-time and heavy background work in the same process forever.
Background jobs can delay message handling and make the app feel unstable.
A Practical WebSocket Hosting Policy
A small-team WebSocket policy should be clear enough to guide architecture decisions before the app becomes fragile.
| Policy area | Recommended baseline |
|---|---|
| Communication model | Use WebSockets only when two-way persistent communication is needed |
| Starting architecture | Begin with one VM when connection count and message rate are modest |
| Reverse proxy | Ensure the edge layer supports WebSocket upgrade and sensible timeouts |
| Security | Use wss://, authentication, authorization, origin checks, and message validation |
| Observability | Track active connections, reconnects, message rate, latency, and errors |
| Scaling trigger | Add nodes when connection count, message rate, or reliability needs exceed one VM |
| Shared state | Add pub/sub or a broker when users on different nodes need shared events |
| Cost review | Estimate cost from connections, message frequency, bandwidth, and redundancy |
| Reliability | Define reconnect behavior, drain strategy, and recovery path |
The best policy is not the most complex one. It is the one your team can follow while the product grows.
WebSocket Hosting Is Real-Time Infrastructure Planning
WebSocket hosting is not just opening a socket in an application. It is infrastructure planning for persistent communication.
Use WebSockets when two-way real-time behavior is central to the product. Use polling or Server-Sent Events when they are simpler and good enough. Start with one VM when the workload is modest. Add reverse proxy discipline, observability, shared state, and multi-node scaling only when connection pressure and reliability needs make them necessary.
For related reading, this guide should link to Raff’s Reverse Proxy vs Load Balancer guide, Single Server vs Multi-Server Architecture guide, Auto-Scaling VM Planning guide, Performance Bottlenecks guide, Background Work Models guide, and Observability guide.
On Raff, the practical path is to host the first real-time workload simply, measure concurrent connections and message behavior, then scale the architecture only when the workload proves it needs more than one server.