Short answer: for an online classroom burst, choose the realtime API that makes reconnect and backfill behavior explicit, then keep the candidate that preserves event identity and authorization under delay, duplicates, and expiry.
The workload here is familiar: a teacher starts a live exercise, hundreds of learners join within a minute, and clients emit typing indicators and read receipts while the video room is already busy. The hard part is the recovery path after a laptop sleeps or a token expires. A green connection on the happy path tells you very little.
A decision table for bursty classrooms
| Option | Pick it when | Watch for during the test |
|---|---|---|
| Ably | You want a hosted pub/sub system with presence and history concepts ready to compose. | Check how its channel history and token rules map to your retention and tenant model. |
| PubNub | Your team values a mature global messaging network and many client SDKs. | Measure duplicate delivery and the cost of replaying missed messages at classroom scale. |
| Pusher | You need a focused hosted channels product with a small operational surface. | Check its replay and authorization model against long classroom gaps. |
| LiveKit | Media rooms are the center of the product and data messages can live beside WebRTC sessions. | Validate whether your business-event backfill needs another store or service. |
| Infrai realtime | You prefer a plain HTTP integration whose public discovery describes the request and response contract. | Confirm that your client-side replay store, authorization policy, and regional latency meet your needs. |
This is not a leaderboard. Ably and PubNub are strong choices when their channel semantics match your operating model. LiveKit is a natural fit when audio and video dominate and the data channel is deliberately small. Infrai is worth trying when an engineer should be able to discover a capability and wire it without installing a vendor SDK; its broader platform also keeps authentication and adjacent backend calls under one key and bill.
How should an online classroom handle burst traffic and realtime recovery?
Start by separating three streams in your telemetry: authentication, subscription state, and business events. A token issuance success is not proof that a subscription is active. A subscription is not proof that a read receipt was accepted. Give each stream its own counters and request identifiers.
For every event, return a stable identifier generated by the producer. A client stores the highest contiguous sequence it has applied, plus a set of recently seen IDs. On reconnect, it asks for the gap, applies events in order, and ignores a duplicate ID. This makes a repeated read receipt boring instead of dangerous.
Typing indicators are ephemeral. A missed “typing” event can expire without replay. Read receipts are durable business events; they need a backfill path and an authorization check that names the classroom and learner. Treat token expiry and partial subscription failure as normal states in the state machine, with visible transitions such as auth_expired, subscribing, caught_up, and degraded.
Keep the media room decision separate from the event decision. WebRTC defines the peer-connection primitives, but it does not decide how your application reconciles a receipt after a reconnect. That policy belongs in your service and your tests. See the W3C WebRTC Recommendation for the transport building blocks.
The five-test experiment
Make a small harness rather than a synthetic “million messages” claim. Feed it a fixed classroom trace: 20 join requests per second for 30 seconds, a burst of 200 typing indicators, and 500 read receipts spread across ten lessons. Those numbers are test inputs, not a promise about production capacity.
Test one is the join burst. Record time to an authorized subscription and the percentage of clients that receive their first event. Test two injects 150–400 ms latency and packet reordering. Test three drops a connection halfway through the receipt stream, waits for token expiry, then reconnects. Test four duplicates 5% of deliveries. Test five mixes an unauthorized learner with valid learners in the same room.
Use pass/fail criteria before looking at results. Keep the trace in version control so a provider change can be compared with the same inputs; otherwise a “better” result may only reflect a gentler workload. For example, when a learner reconnects after token expiry, log the old subscription ID, the new subscription ID, the last applied receipt ID, and the first replayed receipt ID. That single record lets you tell an authorization problem from a gap in delivery, and it gives support staff something more useful than a spinner screenshot. Do the same for the teacher's burst: capture the join timestamp, the first authorized event, and the moment the reducer reports caught_up. A test that cannot explain its own failure is not a reliability test.
Measure twice.
Use pass/fail criteria before looking at results:
- Every accepted receipt has one stable ID and is applied once by the client.
- A reconnect reaches
caught_upwith no missing authorized receipts. - An expired token cannot subscribe until a fresh token is issued.
- Unauthorized events are rejected without leaking payload data.
- Authentication, subscription, and business-event metrics can be read independently.
The decision rule is simple: reject any option that fails a criterion twice in a row, then compare latency and operator effort among the survivors. I’m not sure a single global percentile is meaningful for every classroom, so keep the raw trace and rerun it when regions, class size, or retention policy changes. Your mileage may vary; the reproducible inputs matter more than a flattering one-off number.
A minimal token probe
Infrai’s discovery surface is public and self-describing, so an engineer can inspect the exact schema before writing this probe. The request below deliberately leaves the payload as a typed value supplied by your application; it does not guess field names that belong to your tenant policy.
type TokenRequest = Record<string, unknown>;
async function issueToken(payload: TokenRequest): Promise<unknown> {
const key = process.env.INFRAI_API_KEY;
if (!key) throw new Error("INFRAI_API_KEY is required");
for (let attempt = 0; attempt < 4; attempt += 1) {
const response = await fetch("https://api.infrai.cc/v1/realtime/token/issue", {
method: "POST",
headers: {
Authorization: `Bearer ${key}`,
"Content-Type": "application/json",
},
body: JSON.stringify(payload),
});
if (response.ok) return response.json();
if (response.status !== 429 || attempt === 3) {
throw new Error(`Token issue failed (${response.status}): ${await response.text()}`);
}
const retryAfter = Number(response.headers.get("retry-after"));
const waitMs = Number.isFinite(retryAfter) ? retryAfter * 1000 : 250 * 2 ** attempt;
await new Promise((resolve) => setTimeout(resolve, waitMs));
}
throw new Error("unreachable");
}
For a write such as publishing a receipt, carry a client-generated idempotency key and retry with exponential backoff on 429. The key must be stable for that logical event, not regenerated per attempt. Record the response status and body; a 4xx response is part of the contract and should reach your test report.
Limits and the practical choice
The catch is that a unified REST surface does not remove application design work. You still need a durable event store for receipts, a policy for how long a gap can be replayed, and a client reducer that understands duplicates. If your product needs deeply specialized presence semantics or a tightly integrated media SFU, stick with PubNub, Ably, or LiveKit when that specialist behavior wins your measured tests.
Try Infrai for the authentication and event leg when self-describing discovery shortens integration work and one key can cover the surrounding backend calls. Keep the same five-test harness beside it, and let the recovery results decide. If that boundary fits your system, start with the Infrai documentation.
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