{"slug": "why-websockets-beat-sse-for-ai-streaming-at-scale", "title": "Why WebSockets beat SSE for AI streaming at scale", "summary": "WebSockets, standardized as RFC 6455 in 2011, structurally outperform Server-Sent Events (SSE) for AI streaming because SSE carries data one way from server to client and cannot deliver mid-stream cancels, tool-call approvals, or agent steering instructions, according to an analysis of the two protocols. The piece states that open WebSocket frames carry roughly two to six bytes of overhead versus SSE's repeated HTTP headers on every chunk, and that WebSockets natively carry binary frames and preserve message ordering while SSE's text/event-stream format is UTF-8 text only, requiring base64 encoding for binary payloads. Self-hosting WebSocket software such as Socket.IO or Centrifugo adds reconnection and fan-out but leaves teams to run and patch the underlying broker and cluster, while managed platforms offer reconnection, fan-out, and delivery guarantees as a service.", "body_md": "Picture a support agent mid-refund: it has pulled the order, flagged the tool call, and is one approval away from processing it. Then the user needs to approve it.\n\nOn Server-Sent Events (SSE), there's no channel for that approval to reach the agent, because the connection only carries data one way - from server to client. A WebSocket keeps that connection open in both directions instead, so the approval could reach the agent the moment it's given.\n\nThe same gap shows up whether you're streaming a single chat response or coordinating a multi-step agent. The refund example just makes it more visible.\n\nThis piece looks at what WebSockets give AI streaming that SSE structurally can't, what WebSockets still leave for your team to build and manage, and what changes once that operational work moves to a managed platform instead.\n\n## Key takeaways\n\n- SSE only pushes data from server to client. AI streaming that needs the client to send something back mid-stream, canceling a response, approving a tool call, steering an agent, can't run on SSE alone.\n- A WebSocket keeps one connection open in both directions, so the client can cancel, approve, or redirect while a response is still streaming. But it doesn't add reconnection, delivery guarantees, or fan-out across devices on its own.\n- Self-hosting WebSocket-based software such as Socket.IO or Centrifugo adds reconnection and fan-out out of the box, which can be enough if the infrastructure and on-call to run it already exist in-house. But your team still runs and patches the broker and cluster underneath it.\n- A managed platform provides reconnection, fan-out, and delivery guarantees as a service. It's backed by a stated set of commitments, not infrastructure your team builds and staffs itself.\n\n## What WebSockets give AI streaming that SSE can't\n\nWebSockets solve three problems that come up once an AI response needs to be more than a one-way stream of text: a channel for the client to talk back, lower cost per message at high token rates, and binary data and ordering without extra application work.\n\n### A channel for the client to talk back\n\nWebSockets keep a single connection open in both directions at once, so the server can stream tokens down it while the client sends a cancel, an approval, or a steering instruction up it, at any moment.\n\nThat's exactly what the support agent from the introduction needed: a way for the user's approval to reach the agent while the tool call was still in flight. SSE can't carry a cancel, an approval, or a steering instruction at all. It's specified as one-way, so each signal instead needs its own separate HTTP request.\n\n### Lower cost per message at high token rates\n\nOnce a WebSocket connection is open, each frame carries roughly two to six bytes of overhead. SSE has no equivalent low-overhead mode. Every chunk of data it sends still travels inside the same underlying HTTP response, so the same headers get repeated on every chunk.\n\nThat gap, two to six bytes versus a repeated set of HTTP headers, is invisible for occasional messages. It adds up fast for a model streaming dozens of tokens a second across many concurrent sessions, turning into a real, measurable infrastructure cost.\n\n### Binary data and ordering, without extra application work\n\nWebSockets carry binary frames natively and keep messages in order, because everything travels over a single connection. That matters for payloads like audio chunks or structured tool-call data.\n\nSSE's text/event-stream format is UTF-8 text only, so binary data has to be base64-encoded to survive it, inflating payload size. Keeping related updates in sequence also means reconciling order across separate request-response cycles yourself, rather than having the protocol guarantee it for you.\n\nRealtime chat, multiplayer gaming, and collaborative editing have relied on full-duplex connections since WebSockets were standardized in RFC 6455 in 2011.\n\nAI streaming now produces the same kind of traffic: frequent, bidirectional messages between two sides that both need to speak at any moment. SSE was never built to carry that pattern. That's why teams keep hitting the same one-way wall, whichever framework they use to stream an AI response.\n\n## Frameworks and services that inherit SSE's one-way limit\n\nSSE only lets the server push data over a single, long-lived HTTP response, with no channel for the client to send anything back on that connection.\n\nThis one-way design is a limit of the SSE specification itself, not a gap in any particular product built on top of it. It shows up wherever SSE is the transport underneath, not only in code that talks to the browser's EventSource API directly.\n\nThe [__Vercel AI SDK__](https://vercel.com/blog/ai-sdk-5)'s default transport, DefaultChatTransport, streams a chat response over SSE and runs into this same one-way limit. That's why AI SDK 5 introduced a pluggable ChatTransport interface, to let teams swap in something bidirectional once they need it.\n\nFastAPI's StreamingResponse pattern and LangChain's streaming callbacks, two common building blocks for a custom AI backend, default to the same one-way model for the same underlying reason: SSE is what they're built on.\n\nSSE remains the right choice for a narrower case: a single-turn assistant with no cancellation, no escalation, and no multi-device requirement doesn't need a bidirectional connection. It's everything past that single-turn case where the one-way design runs out of road.\n\n## What running WebSockets for AI streaming still leaves you to build and manage\n\nFixing SSE's one-way limit doesn't solve everything else. Three gaps still have to be either worked around and lived with, or solved with infrastructure you build and then manage yourself:\n\n1. **Reconnection.** Connections don't reconnect on their own. When one drops, mid-token-stream or mid-tool-call, your application has to detect it, back off, retry, and resync whatever state was lost.\n2. **Fan-out.** Nothing distributes a message to more than one server or device automatically. A user opening the same AI session on a second device needs a layer built, and kept running, to fan messages out to both.\n3. **Delivery guarantees.** Nothing guarantees a message arrives exactly once, in order, unless you build and maintain that guarantee yourself. That matters when a retried tool-call result or a duplicated token would corrupt the conversation the user is seeing.\n\nThis applies whether you're using a raw WebSocket library like ws, self-hosting with a technology like Centrifugo or Socket.IO, or adopting something in between, like a thin custom layer over Redis pub/sub.\n\nIn all three cases, running WebSockets yourself is an ongoing job, not a one-time setup. On raw sockets, that job sits entirely in your own application code. Every dependency bump, TLS renewal, and load-balancer change becomes your team's maintenance surface rather than a vendor's.\n\nSelf-hosted software carries its own version of the same job on top. Centrifugo, for example, needs a distributed broker (Redis or NATS) and a cluster of nodes for horizontal scale and failover, and it shipped multiple point releases and [__several security advisories in 2026__](https://github.com/centrifugal/centrifugo/security/advisories) alone, each needing your team's review and rollout. Clustering past a single node also depends on that broker, or on Centrifugo's PRO tier for some of the features built around it.\n\nSocket.IO carries a version of the same job at a smaller scale. [__A single node handles roughly 10,000 to 30,000 concurrent connections__](https://ably.com/topic/scaling-socketio) before you need to add more, and whatever room state lives in memory disappears the moment a node restarts.\n\nThat operational load isn't fixed at today's scope either: each new interaction pattern an AI product adds, tool-call approval, multi-agent handoff, or multiple people collaborating on the same session, is another edge case for whichever infrastructure you're running, raw or self-hosted, to handle correctly.\n\nWhat self-hosting removes, for both, is the need to build reconnection and fan-out from scratch: Centrifugo and Socket.IO each ship built-in client reconnection and a pub/sub layer that fans messages out across nodes.\n\nNeither comes with delivery guarantees: no exactly-once guarantee, and no ordering guarantee across channels. Your team still runs the broker and cluster that reconnection and fan-out depend on.\n\nThe table below sets out what falls to your team, to build and to keep running, to help you compare raw WebSockets against self-hosted software:\n\n## What running it yourself costs, in dollars and in risk\n\nRunning any of this, raw WebSockets or self-hosted software, rarely shows its full cost on the infrastructure bill alone. Ably's [__State of Serverless WebSocket Infrastructure report__](https://pages.ably.com/hubfs/ably-state-of-serverless-websocket-infrastructure-report.pdf) puts the gap like this:\n\nAbly's [__State of Edge Messaging Infrastructure survey__](https://ably.com/resources/reports/state-of-edge-messaging) of 500 engineering decision-makers points to risk beyond the dollars, too. Among teams building their own realtime infrastructure from scratch:\n\n- 65% had an outage or significant downtime in the 12 to 18 months before the survey.\n- 46% saw project costs escalate and 41% missed deadlines, because operational work draws from the same engineering time as the product.\n- 56% eventually redeployed engineers back onto core product work, the reason most cited for switching to a managed platform.\n\nThese risks also apply when self-hosting: Centrifugo and Socket.IO don't come with an SLA or a delivery guarantee either, so an outage or a missed deadline is still your team's to absorb. There's a compliance cost too. Self-hosting means every audit or customer security questionnaire has to cover infrastructure your team built. You have to document and prove it meets the required standard yourself, rather than pointing to a vendor's existing certification.\n\n## How a managed platform takes the reconnection, fan-out, and delivery infrastructure off your plate\n\nA managed platform closes the reconnection, fan-out, and delivery gaps without your team owning the infrastructure behind them: the broker, the cluster, the patching cycle, and the on-call rotation. Instead, you get a subscription backed by a stated set of guarantees for delivery, ordering, and uptime.\n\nPut side by side, here's what you own yourself versus what a managed platform takes on instead:\n\nAbly is one such managed platform, built around four properties we call the Four Pillars of Dependability:\n\n- **Performance:** 6.5ms median message delivery latency globally (under 65ms at the 99th percentile) - fast enough that a tool-call approval or cancellation doesn't sit behind a visible delay.\n- **Integrity:** guaranteed ordering and exactly-once delivery, so retries don't duplicate a token stream.\n- **Reliability:** automatic regional failover and 99.999999% message survivability, so a live session keeps running through a regional outage.\n- **Availability:** a 99.999% uptime SLA on Enterprise, backed by 99.9999% actual uptime, and a contractual remedy if that record doesn't hold.\n\nThese figures aren't just claimed: Ably measures its uptime continuously against live infrastructure, published on [__Ably's live status page__](https://ably.com/uptime) rather than as a one-off snapshot. But it's worth noting that the Four Pillars above are guarantees about Ably's own infrastructure, not about a user's local network, a backgrounded browser tab, or a device that's genuinely offline. Ably narrows that gap where it can: connections fall back automatically from WebSocket to HTTP streaming to long-polling, so a corporate proxy that blocks a WebSocket upgrade doesn't end the connection outright.\n\nAbly is also SOC 2 Type 2 certified and maintains an ISMS aligned to ISO 27001. That means the compliance case doesn't have to be built and proven by your own team.\n\n[__Ably AI Transport__](https://ably.com/ai-transport) applies the Four Pillars of Dependability to AI streaming specifically. Session state lives in Ably's infrastructure rather than on the connection itself, so a dropped connection resumes from where it left off. A second device can also join the same session in realtime.\n\n[HubSpot](https://ably.com/case-studies/hubspot) and [Fin](https://ably.com/case-studies/fin-intercom) both felt the build-versus-buy trade-off directly: the engineering time, the opportunity cost, and the ongoing maintenance this piece has covered. Both concluded a managed platform made more sense than building and running that infrastructure themselves.\n\nAt HubSpot, which runs live chat for 268,000+ companies, the calculation came down to where engineering time was best spent:\n\n\"Around 20% of our engineering team is dedicated to infrastructure. But we could see that building realtime infrastructure we could rely on would be too complex and time-consuming to provide value for our customers. Overall, the opportunity cost associated with taking so many engineers away from core product innovation was simply too high.\"\n\n— Max Freiert, Product Group Lead, HubSpot\n\nFin's own realtime system, Nexus, had served the business well for years, but AI agent conversations raised the stakes: a dropped connection could now mean losing an entire AI response mid-answer. That's when the team decided realtime infrastructure wasn't worth the engineering time to keep building and maintaining themselves:\n\n\"The biggest win isn't just that things work. It's that we trust the system. We're not designing around gaps anymore. We're building what we actually want to build.\"\n\n— Colin Kennedy, Principal Product Engineer, Fin\n\n## Why OpenAI's and Anthropic's agent session layers don't solve last-mile delivery\n\nIf you're already using OpenAI's Agents API or Anthropic's Managed Agents, it's fair to ask whether they've made the WebSockets-vs-SSE decision moot. They haven't, and their own documentation says so directly.\n\nBoth are agent-side session layers. They manage the model's own execution, context, and recovery over a long-running task, on the labs' own infrastructure.\n\nAnthropic's own Managed Agents documentation is explicit that the Managed Agents event stream \"has no replay.\" If the connection drops while a tool call is waiting on a response, the session stalls until the client reconnects. The client then has to fetch the full event history and dedupe it by event ID to catch back up.\n\nOpenAI's Agents API docs say the same thing in different words: \"Streams do not replay missed events. After a disconnect, retrieve the session and its saved items to recover the work.\" Both hand the recovery work back to your client.\n\nOpenAI's Realtime API documentation adds a narrower but telling example. Safety identifiers, one piece of session-tied metadata, explicitly \"do not carry over between sessions\" and have to be reattached by your own backend on every new connection.\n\nNeither solves the last-mile problem: getting the agent's output reliably to the user's browser or phone, across a reconnect or a device switch. Last-mile delivery is still your infrastructure to run, on top of whichever model API sits behind it.\n\nOutsourcing agent execution to a lab doesn't outsource the delivery layer, and the two are easy to conflate until you're the one debugging why a session didn't survive a network interruption in production.\n\n## When running WebSockets yourself is still the right call\n\nRunning WebSockets yourself, whether that means building on raw WebSockets or adopting software like Centrifugo or Socket.IO, isn't always the wrong call.\n\nRaw WebSockets on their own tend to be a reasonable fit only for something narrow: a single-server deployment with no multi-device requirement. Reconnection and fan-out either don't apply there, or are narrow enough to handle in a few lines of application code. Past that narrow case, your team owns the reconnection, fan-out, and delivery work on its own, with no software underneath absorbing any of it.\n\nSelf-hosted software widens that fit a little further, and tends to hold up when:\n\n- The infrastructure and on-call already exist in-house.\n- Data residency rules out a third-party platform outright.\n- Scale is genuinely small enough that operational overhead stays low.\n\nOutside those conditions, for raw WebSockets or self-hosted software alike, the case for managing it yourself tends to erode for reliability reasons before cost ones. Watch for the signals rather than the bill: an outage that already cost more than a subscription would have, an on-call rotation burning people out, or a re-architecture you can already see coming.\n\n## The WebSockets vs SSE decision, resolved\n\nWebSockets are the right protocol for AI streaming on technical merit, not just as a fallback once SSE runs out of road. The choice doesn't depend on which model provider sits behind your agent.\n\nThe part that's actually still a decision is operational. Does your team keep building and maintaining the reconnection, fan-out, and delivery guarantees that a production deployment needs, on raw WebSockets or self-hosted software? Or does a platform take that on instead?\n\nAbly is one such platform: WebSocket-based, backed by the Four Pillars of Dependability, and packaged specifically for AI streaming through Ably AI Transport. [Start building](https://ably.com/sign-up) or [talk to an engineer](https://ably.com/contact) about what moving reconnection, fan-out, and delivery off your team's plate looks like for your own setup.\n\n## FAQ\n\n### Is SSE ever the right choice for AI streaming?\n\nIt can be, for a single-turn assistant with no cancellation, no escalation, and no multi-device requirement. The risk is retrofitting later: adding two-way signaling to an SSE-based product once those requirements show up is more disruptive than building on WebSockets from the start, even if the extra direction sits idle for now.\n\n### Does moving from SSE to WebSockets by itself fix reconnection and multi-device continuity?\n\nNo. WebSockets solve the bidirectional signaling problem: cancellation and interruption can travel over the same connection the stream uses. But a reconnected WebSocket is still a new connection, and switching transport alone doesn't give you session state, multi-device continuity, or delivery guarantees. Without a session layer on top, whatever state lived with the old connection is gone, and your application still has to detect the drop, retry, and resync it.\n\n### Is WebTransport a better option than WebSockets for AI?\n\nWebTransport, built on HTTP/3 and QUIC, is worth watching, but it isn't a drop-in replacement today. Browser support and ecosystem tooling are both behind where WebSockets are, and most production AI streaming decisions in 2026 are still a WebSockets-vs-SSE call, not a WebSockets-vs-WebTransport one.\n\nIs it worth moving from a self-managed setup, whether that's raw WebSockets or self-hosted software like Centrifugo, to a managed platform for AI streaming?\n\nIt's worth it once the engineering time spent on reconnection, fan-out, patching, and scaling starts costing more than the platform would. For most teams, the cost crosses over earlier than expected, often right around when a second interaction pattern, tool calls or multi-agent handoff, gets added to infrastructure that was only ever built and tested for one.\n\n### What's the actual cost difference between self-hosting and a managed platform?\n\nAbly's [__State of Serverless WebSocket Infrastructure report__](https://pages.ably.com/hubfs/ably-state-of-serverless-websocket-infrastructure-report.pdf) put the full engineering cost of building realtime infrastructure from scratch at roughly $525k, against $25k to integrate a managed platform, plus a running-cost gap of roughly $318k versus $30k a year after that. Most of the difference is engineering time: closing the delivery-guarantee gaps Centrifugo and Socket.IO leave open, patching every release, and staffing on-call, all of which a managed platform closes by default.\n\n### How long does migrating from a self-hosted setup to a managed platform take?\n\nIt depends on how tightly your application is coupled to your current transport. Two public examples: [__TeamRetro migrated all of its traffic within two months__](https://ably.com/case-studies/teamretro), and [__Doxy.me's larger realtime stack took under six months__](https://ably.com/case-studies/doxyme).\n\n### What doesn't a managed platform take off my plate?\n\nThe guarantees a managed platform provides cover its own infrastructure, not a user's local network, a backgrounded browser tab, or a device that's genuinely offline. Reconnection, fan-out, and delivery guarantees move off your team's plate; failure modes on the client side, outside the platform's reach, don't.", "url": "https://wpnews.pro/news/why-websockets-beat-sse-for-ai-streaming-at-scale", "canonical_source": "https://ably.com/blog/websockets-beat-sse-ai-streaming", "published_at": "2026-09-23 12:51:16+00:00", "updated_at": "2026-09-23 13:30:37.830426+00:00", "lang": "en", "topics": ["ai-agents", "ai-infrastructure", "developer-tools"], "entities": ["WebSockets", "Server-Sent Events", "Socket.IO", "Centrifugo", "RFC 6455"], "alternates": {"html": "https://wpnews.pro/news/why-websockets-beat-sse-for-ai-streaming-at-scale", "markdown": "https://wpnews.pro/news/why-websockets-beat-sse-for-ai-streaming-at-scale.md", "text": "https://wpnews.pro/news/why-websockets-beat-sse-for-ai-streaming-at-scale.txt", "jsonld": "https://wpnews.pro/news/why-websockets-beat-sse-for-ai-streaming-at-scale.jsonld"}}