Ably vs Socket.IO: scaling, connections, and horizontal growth compared

• Socket.IO's state lives in memory on one server; Ably's doesn't. Scaling past a single server requires sticky sessions at the load balancer plus an adapter, typically Redis, to forward broadcasts between servers (source: socket.io/docs/v4/using-multiple-nodes). Ably's frontend connection layer is stateless instead, so any client can be routed to any available instance with no extra configuration.

• Fanout correctness depends on the Redis adapter staying up, on Socket.IO. If the Redis connection drops, delivery falls back to only the clients already on the current server, per Socket.IO's own documentation (source: socket.io/docs/v4/redis-adapter). Ably's inter-region message routing is peer-to-peer, with no broker in the path to fail.

• Socket.IO publishes no connection ceiling of its own. Capacity depends entirely on one process's memory, CPU, and event loop headroom. Ably's concurrent connection and channel limits instead scale with package tier, from 200 on Free to unlimited on Enterprise, with no architectural ceiling on throughput (source: ably.com/pricing).

• Adding capacity doesn't rebalance existing load, on Socket.IO. A new server accepts new connections, but existing ones stay wherever they landed until something forces a reconnect. Ably uses progressive hashing instead, so a new instance absorbs load in stages rather than taking a full share immediately.

Socket.IO holds each connection's data, including its room memberships, in the memory of whichever server process accepted it. That's simple and fast for a single server, but nothing about the design anticipates more than one server. There's no shared state layer built in, which is why multi-server deployments need sticky sessions and an adapter bolted on, rather than configured as a first-class feature.

Ably's frontend connection layer holds no client-specific state, so any instance can serve any client. Channels are placed using consistent hashing, and fanout to subscribers happens in two tiers: once to each frontend server with subscribers for a channel, then out to that server's individual connections. A spike in one channel's subscriber count doesn't concentrate load on a single process.

This stateless-frontend, consistent-hashing design is why the rest of this page's comparisons hold: Socket.IO's scaling mechanics are something your team configures and operates. Ably's are a property of the platform.

No. Socket.IO's documentation on using multiple nodes is explicit that once you run more than one server, "you have to make sure that all requests associated with a particular session ID reach the process that originated them." That's sticky sessions: a load balancer configuration (cookie-based or IP-hash-based) that pins each client to the server it first connected to, for the lifetime of that connection.

Socket.IO's docs describe a few ways to implement this, none of which is provided out of the box:

• nginx, HAProxy, or Traefik, configured for session affinity.

• Node's cluster module, paired with a package like @socket.io/sticky.

• Disabling HTTP long-polling and using WebSocket (or WebTransport) only, which removes the need for sticky sessions entirely, since each client holds one persistent connection to one server. The tradeoff, per Socket.IO's own docs, is reduced compatibility: clients on networks that block WebSocket have no fallback.

Ably's connection processing is stateless at the frontend layer: a load balancer can route any client to any available instance. The system also rebalances connections in the background for even load. There's no session-affinity configuration to build or maintain.

A team running Socket.IO behind a standard cloud load balancer, with sticky sessions configured on IP hash, scales from two servers to six during a traffic spike. Every client with an already-established connection is fine, until something forces a reconnect.

A load balancer failover, a deployment, or a connection-draining event can all force one. When that happens, the client risks landing on a different server that doesn't have its room membership or session state.

The Redis adapter (or an equivalent) can forward that state, but only if it's already in place, and only for adapters that support Socket.IO's connection state recovery feature. Per Socket.IO's own compatibility table, the classic Redis (pub/sub) adapter doesn't support it at all; the separate Redis Streams and MongoDB adapters do.

A team already committed to the plain Redis adapter for fanout is left choosing between session continuity and horizontal scaling, rather than getting both, unless it re-evaluates which adapter it's running.

Rooms in Socket.IO exist in the memory of whichever server the connected sockets are attached to. With a single server, this is simple. Across multiple servers, the Redis adapter publishes broadcasts to a Redis pub/sub channel that every other Socket.IO server subscribes to, and each of those servers then delivers to its own locally-connected clients.

This works, but Redis pub/sub is not persistent. Socket.IO's own docs confirm no data is stored in Redis, and if the Redis connection drops, "packets will only be sent to the clients that are connected to the current server." A room can silently fragment into per-server islands during a Redis outage, with no record of what was missed.

Ably's fanout is tiered without a message broker in the path. A channel's core process sends a published message once to each frontend server that has subscribers for that channel, and each frontend then delivers to its own connections. Cross-region delivery works the same way, peer-to-peer between regions. There's no separate pub/sub layer to provision, secure, or lose.

Socket.IO doesn't have a built-in mechanism for rebalancing existing connections onto new capacity. A new server joins and starts accepting new connections, but existing ones stay wherever they landed. Ably's platform scalability documentation describes progressive hashing specifically to avoid the "thundering herd" problem this can create elsewhere. A new instance claims hash positions gradually (for example 10%, then 20%, then 30%) rather than taking its full allocation immediately, so load shifts in stages instead of all at once.

Connection scaling and message fanout play out concretely in a few common scenarios: a sudden viral spike in one room, a phased product rollout, and a multi-destination AI agent fanout.

A community platform's most active room goes from a few hundred concurrent users to fifty thousand within an hour after a creator goes viral.

On Socket.IO, the servers holding that room's sockets need enough memory and CPU to handle the connection count, and the Redis adapter needs to keep relaying every message to however many other nodes hold subscribers. If that room's connections are concentrated on servers that are now overloaded, there's no automatic redistribution: a human has to intervene, or the deployment has to be over-provisioned in advance.

On Ably, the channel is placed via consistent hashing, and the two-tier fanout architecture means the spike in subscriber count doesn't concentrate load onto a single process.

A B2B SaaS platform ships a live activity dashboard to a 200-account beta, then turns it on by default for its entire 40,000-account customer base.

On Socket.IO, this growth means re-architecting the server topology, adapter choice, and load balancing strategy more than once, likely at multiple points along the way: the sticky-session and Redis-adapter setup that covered beta traffic doesn't hold at full rollout.

On Ably, the same channel and connection model scales from one to the other without an application-level re-architecture.

A support product streams an AI agent's response to the customer's browser, to a supervisor dashboard that's monitoring the live conversation, and to a compliance logging pipeline, all subscribing to the same session simultaneously. This is a fanout problem, not only a connection-count problem: the same tokens need to reach three destinations with the same ordering guarantees.

On Socket.IO, each of these three subscribers needs to be a room member on whichever server (or servers, via the adapter) the session is running on, with delivery timing dependent on adapter relay.

On Ably, multi-subscriber fan-out to the same channel is native: the UI, the dashboard, and the logging integration can all subscribe independently and see the same ordered stream. Outbound integrations (Kafka, Kinesis, webhooks) can also be attached to the channel without custom relay code.

• You're running a single server, or a small, fixed number of servers, where connection counts stay well within one process's memory and CPU headroom.

• Your team already operates Redis (or is comfortable operating it) for other parts of the stack, so adding the adapter is incremental rather than new infrastructure.

• Your traffic growth is predictable and gradual enough that you can plan and test topology changes (sticky sessions, adapter configuration, server counts) ahead of demand rather than reacting to spikes.

• You want full control over the load balancing and fanout strategy and are willing to own its correctness, including during failover events.

• Realtime is one feature among several in your product, and the engineering cost of building and maintaining the scaling layer is proportionate to how central realtime is to the business.

• Your connection or subscriber counts are unpredictable, spike-prone, or growing quickly enough that pre-provisioning a fixed topology isn't realistic.

• A single channel or room can attract very large numbers of concurrent subscribers, and you need fanout to that many recipients to stay reliable without a broker you have to run yourself.

• You need horizontal scaling and session continuity at the same time, rather than trading one for the other based on which adapter features are compatible with which capability.

• You're building AI-powered features where a single agent response has to reach multiple observers (the end-user UI, a supervisor view, an audit or analytics pipeline) with consistent ordering. You don't want to build and operate that fanout layer yourself.

• You'd rather your engineering time go into product features than capacity planning, load balancer configuration, and adapter operations.

Socket.IO gives you the primitives and leaves the scaling architecture to you. Sticky sessions or an adapter, capacity planning, and rebalancing strategy are all decisions your team owns and operates. Ably takes that layer off your plate by building horizontal scaling into the platform itself, in exchange for running on managed infrastructure rather than your own servers.

The decision test: if your connection and subscriber counts are stable and within a scale you've already tested, owning the scaling layer is a reasonable trade for the control it gives you. If growth is unpredictable, or a single spike in one room or channel could be large enough to threaten the rest of your service, the operational cost of owning that layer tends to grow faster than expected.

No. A single Socket.IO server holds all connection and room state in memory. To run more than one server, you need to configure sticky sessions at your load balancer (so a client's HTTP long-polling requests keep hitting the same server) and add an adapter to forward broadcasts between servers. Ably's connection handling is stateless at the frontend layer, so any client can be routed to any available instance without extra configuration.

The Redis adapter uses Redis's pub/sub mechanism to forward broadcasts between Socket.IO server instances so a message published on one server reaches clients connected to another. Socket.IO's own docs note that if the Redis connection is severed, packets are only delivered to clients on the current server. That makes the adapter a dependency your fanout correctness relies on, and it has to be scaled and kept available in its own right. Ably's message routing between regions is peer-to-peer with no equivalent broker in the path.

Socket.IO doesn't publish a connection ceiling because it depends entirely on your server's memory, CPU, and event loop capacity. Each connection and its room memberships live in that process's memory. Ably's concurrent connection and channel limits scale with package tier instead, from 200 on Free to unlimited on Enterprise, with no architectural ceiling on throughput (source: ably.com/pricing).

You need to update your load balancer configuration (or client-side server list) and, if using the Redis adapter, the new node subscribes to the shared pub/sub channels. There's no built-in mechanism for gradually shifting existing connections onto the new server. Capacity is added, but rebalancing load onto it is left to your infrastructure. Ably uses progressive hashing: a new instance claims hash positions gradually, absorbing load in stages rather than taking a full share immediately.

Adding servers solves the "too many connections for one process" problem, but fanout to a large audience on one channel or room is a separate challenge. Socket.IO's rooms are held in the memory of whichever server(s) hold the relevant sockets, with the adapter relaying broadcasts. Ably uses a two-tier fanout architecture: a channel's message is sent once to each frontend server with subscribers, which then delivers to its own connections. That means a channel with a very large number of subscribers doesn't bottleneck on a single process.

Socket.IO doesn't publish a connection or room-size limit because there isn't a platform-level one to publish: capacity is bound entirely by a single process's memory, CPU, and event loop headroom, and a room's membership lives in the memory of whichever server holds those sockets. In practice, teams avoid hitting that ceiling by scaling out early (sticky sessions plus a Redis, Redis Streams, or MongoDB adapter, per Socket.IO's own docs), sizing servers for peak rather than average concurrency, and monitoring event-loop lag as a leading indicator before connections start timing out. None of that raises the ceiling, it just delays hitting it. Ably sidesteps the question rather than answering it: connection and channel limits scale with package tier (200 concurrent on Free up to unlimited on Enterprise), with no single-process ceiling to plan around in the first place.