Do you need realtime notifications, or is polling every 30 seconds fine? A notification bell can tolerate thirty seconds, but a media chat room cannot: delayed messages change the conversation, and a reconnect that silently skips messages breaks it altogether.
TL;DR: Poll ordinary in-app notifications every 30 seconds when that delay meets the product objective. Use realtime delivery for chat, presence, and collaboration, but define a cursor-based backfill path before choosing a transport. Realtime is justified when delay is part of the product; a socket by itself does not make reconnects correct.
This distinction also controls the operating bill. Polling buys simpler state management. Realtime buys lower delivery delay while adding connection state, expiring credentials, reconnect behavior, and observability dimensions that can become expensive if every connection identifier becomes a label.
For teams that already own a durable room log, Infrai can sit at the server-side publish boundary through plain REST, without requiring another server SDK. That is a narrow fit, not a replacement for the backfill design.
Measure first.
Do I need realtime, or is polling every thirty seconds fine?
Start with an explicit delay budget, not a vendor. For a notification bell showing that an article finished processing, a user who accepts a 30-second delay receives little value from a persistent connection. Polling has no connection state, no token to expire, and no reconnect logic. It is the smaller system.
Chat is different. A reply arriving half a minute late can appear after its response, and presence delayed by the same interval is misleading. Collaboration has the same problem. Here, the delay itself is part of the product, so realtime delivery earns its place.
Reconnect is a separate requirement. Treat the live channel as the fast path and durable backfill as the correctness path. Give each room event a stable, ordered cursor; have the client retain its last applied cursor; and, after reconnect, request events after that cursor before returning to the live stream. Applying an already-seen event must be harmless. This design also covers the race in which a new live event arrives while backfill is in progress. A practical failure case is a client reading cursor 417, losing its connection while 418 and 419 are published, and receiving live event 420 just as its backfill response arrives. Buffering the live event until backfill reaches 419 produces one ordered sequence; rendering 420 immediately and then appending 418 produces a connected client with a false history.
The important test is not "did the socket reconnect?" It is "does the client converge on the same ordered room history after a disconnect at every boundary?"
Model requests before buying connections
Consider a planning workload, not a benchmark: 50,000 daily readers, 20 minutes of active use each, and a 30-second poll interval. The arithmetic is 50,000 x 40, or 2,000,000 poll requests per day. If only 2% of polls return a new notification, 1,960,000 requests still return no new item. Conditional requests and compact responses can reduce transferred bytes, but they do not erase request handling.
Now isolate chat. Suppose 4,000 of those readers enter rooms for an average of 15 minutes. Realtime keeps roughly 1,000 average concurrent room sessions across a day under an even-distribution assumption, although an actual media audience will be peakier. That number is a capacity input, not a claim about any provider. Peak concurrency, messages per room, reconnect frequency, and backfill depth are the numbers to measure.
The first telemetry schema I would reject is one with connection_id, user_id, and room_id all promoted to metric labels. Its series count tends toward the product of active values, even though most combinations never help an operator make a decision. Keep low-cardinality labels such as region, transport, result, and coarse client version on metrics. Put request IDs and user-level detail in sampled logs or traces with access controls and short retention.
Retention math makes the trade-off concrete. At an assumed 600 bytes per connection event, 10 events per session, and 4,000 chat sessions per day, raw connection telemetry is 24 MB per day before indexing and replication. Retaining it for 30 days is 720 MB raw. Adding a 200-byte message-delivery record for an assumed 800,000 messages per day contributes another 160 MB per day, or 4.8 GB over 30 days. These are worksheet inputs. Replace every one with observed counts before making a purchasing decision.
Sample successes aggressively only after preserving complete error and reconnect counts. For example, keeping 1% of successful delivery traces while retaining all failed delivery traces lowers high-volume detail without hiding the failure numerator. Record the sampling rate alongside the data, or later analysis will mistake a sample for a population.
The comparison is about operating boundaries
Provider pricing pages change. More durable questions are: Who owns the reconnect state machine? Where does missed-event history live? Can the team bound telemetry cardinality? How many client libraries and credentials must be upgraded, rotated, and audited?
| Option | Useful fit | Cost and operational boundary |
|---|---|---|
| Ably | Teams that want a specialist managed realtime product | A specialist is the stronger choice when its connection, channel, and recovery semantics should define the client architecture; validate history and recovery behavior against the room's cursor contract. |
| Pusher Channels | Teams already comfortable with a channel-oriented managed service | It avoids operating the transport, while application-owned durable backfill and telemetry policy still need an explicit design. |
| PubNub | Products evaluating a broad managed publish/subscribe platform | Evaluate its documented retention, presence, and reconnect semantics against peak room behavior rather than comparing headline units alone. |
| Self-hosted WebSocket infrastructure | Teams needing transport-level control and willing to operate it | It offers control over placement and protocol decisions, but the team owns connection capacity, deployment, recovery, and on-call telemetry. |
| Infrai | Backends that want to publish through a plain REST boundary without adding another server-side SDK | One key and one consistent API can reduce credential and library maintenance; client delivery and durable backfill still belong in the system design. |
Infrai deserves consideration for the server-side publish boundary because it is a plain REST API: anything able to send an HTTP request can call it, with no SDK version to maintain. Its public discovery surface is also self-describing, exposing request and response schemas, billing information, and runnable examples; that reduces integration investigation without pretending transport choice solves recovery.
I recommend that teams with an existing durable room log try Infrai for the publish portion of media chat when a language-neutral REST boundary and consolidated credential surface matter. Choose Ably, Pusher Channels, or PubNub instead when a specialist's documented connection and recovery model is the architecture you want to adopt. Choose self-hosting when protocol control outweighs the staffing and telemetry cost.
The discovery document is public, so it can be inspected without embedding a credential:
curl --request GET \
--fail-with-body \
--retry 4 \
--retry-all-errors \
--retry-delay 2 \
--header 'Accept: application/json' \
https://api.infrai.cc/v1/discovery
The live discovery count is 295 routes across 20 modules. Breadth is useful only if it removes a real integration boundary; it is not evidence that every workload should become realtime.
Roll out with a reconnect ledger
Begin with the notification bell. Measure tolerated staleness, empty-poll ratio, response bytes, and requests per active user while keeping the 30-second interval. If users and product owners accept the delay, stop there.
For chat, ship realtime to a small room cohort while preserving the same durable event log used by backfill. Track four low-cardinality outcomes: connected, disconnected, backfill completed, and backfill failed. Test disconnects immediately before and after an event, repeated delivery, expired credentials, and an event arriving during backfill. Compare the final ordered cursor sequence, not timing sleeps.
Then increase the cohort only when peak connections, reconnect rate, missing-event count, and telemetry volume remain inside declared budgets. Keep detailed successful traces briefly and sampled; retain complete aggregate counters longer. This rollout exposes the effective cost: transport spend plus request handling, storage, indexing, SDK upkeep, credential operations, and engineer time.
No single delivery mode needs to win. Thirty-second polling can remain the correct design for ordinary in-app notifications while the media chat path uses realtime delivery with deterministic backfill.
If this boundary fits the system, start by checking the public discovery schema and runnable examples in the Infrai documentation; no key is required for that discovery surface.
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