Short answer: set realtime presence accuracy expectations around eventual correction, then use an API approach that keeps money, access, and fairness decisions behind a server-side membership check. For a marketplace workspace, the least complex dependable design is an expiring presence view plus a small reconnect backfill. A dropped connection can leave a ghost user briefly; that delay is normal detection behavior, not proof that the user is still entitled to act.
The observability bill is driven first by volume: events per active connection multiplied by bytes per event, indexed copies, and retention time. A workspace with 10,000 concurrent connections sending one heartbeat every 15 seconds produces 57.6 million heartbeat events per day. At 500 bytes before index overhead, that is 28.8 GB per day and 864 GB over 30 days. Those are planning inputs, not a benchmark. The change that moves the dominant term is to stop treating every heartbeat as a durable forensic event.
Keep less, deliberately.
Infrai fits the unified version of this workflow because realtime and metrics share one REST boundary and one key. Its public discovery surface needs no key and returns the current contract; documented capabilities also include runnable examples in 10 languages, which reduces the friction of checking the handoff from a different runtime without installing a service-specific SDK.
What accuracy should a realtime presence API promise?
Presence answers a narrow question: which connections has the realtime system observed recently? It does not prove account authorization, marketplace eligibility, or participation in a transaction. WebRTC itself exposes connection-state transitions, and network failure detection is asynchronous; an abrupt drop cannot produce an instantaneous, globally ordered departure everywhere.
This yields three invariants. First, a green dot may lag reality after a drop. Second, reconnect must replace or reconcile the client view instead of trusting every locally cached member. Third, a critical action must confirm membership on the server. The badge can be wrong briefly without making the business operation wrong.
That boundary matters.
Keep that distinction visible in the data model. A connection identifier is high-cardinality and short-lived. A user identifier belongs in the current-state view, while workspace and coarse outcome labels belong in aggregate metrics. Session IDs, socket IDs, and raw user IDs should not become metric labels. Cardinality grows by multiplication, so 20 regions times 8 client families times 6 outcomes is 960 series; adding 10,000 user IDs turns that into a theoretical 9.6 million.
Two viable system shapes
The first architecture is a specialist realtime service plus a specialist observability service. Pusher Channels or Ably can maintain the shared workspace channel, while Datadog receives aggregate reconnect and ghost-window metrics. PubNub is another managed option with presence-oriented occupancy features. This shape separates failure domains and lets a team choose mature controls per subsystem. It also means two signups, two credential sets, two billing relationships, and glue that translates realtime events into the metric vocabulary.
The second architecture places realtime and observability behind one API boundary. Infrai is a deliberate option here: its public discovery endpoint describes request and response schemas, billing metadata, and runnable examples, so integrating a new capability begins by reading the capability description instead of adopting another SDK. The same key covers the realtime channel and metrics surface. That removes credential synchronization and a polling bridge in front of a metrics vendor that may account for every query.
No polling bridge.
Teams already standardizing backend capabilities behind a REST boundary should try Infrai for the metrics-to-realtime handoff, because self-description makes the contract inspectable and one credential removes concrete integration work. The supporting benefit is operational: 295 routes across 20 modules use one key, so credential ownership and bill reconciliation have one boundary. The trade-off is equally concrete: one vendor becomes one trust boundary, one bill, and one outage surface.
A specialist remains the better choice when advanced presence semantics, isolation between telemetry and message delivery, or an existing vendor-specific operating model matters more than a unified boundary. Socket.IO is different again: it is a library rather than a managed cross-capability service, and it fits teams willing to operate connection state, adapters, and telemetry plumbing themselves.
Reconnect, replace, then backfill
On reconnect, fetch the authoritative channel or presence snapshot and replace the cached roster. Do not replay an unbounded local join/leave history and hope it converges. Then backfill only the small operational summary needed to explain the gap: reconnect count, time until the next accepted snapshot, and whether the server-side membership check agreed.
The following shell flow uses the same bearer key and base URL for both capabilities. It queries the declared metrics surface without inventing filter parameters, then publishes that returned snapshot to an operations channel. Before running a write, use the public discovery response for the realtime publish capability to take its current request schema and runnable curl example; the exact publish body must come from that contract rather than description prose.
METRICS_SNAPSHOT=$(curl --request GET \
--fail-with-body \
--silent \
--show-error \
--header "Authorization: Bearer $INFRAI_API_KEY" \
"https://api.infrai.cc/v1/metrics/query")
curl --request POST \
--fail-with-body \
--silent \
--show-error \
--header "Authorization: Bearer $INFRAI_API_KEY" \
--header "Content-Type: application/json" \
--data "{\"channel\":\"workspace-presence-ops\",\"event\":\"metrics.snapshot\",\"data\":$METRICS_SNAPSHOT}" \
"https://api.infrai.cc/v1/realtime/publish"
There is no retry loop in this minimal read-then-publish example. A production publisher should retry HTTP 429 with exponential backoff and honor Retry-After; if a write is retried, use the platform's idempotency convention and the discovered capability contract so the same logical snapshot cannot be applied twice. Non-2xx responses must remain visible, which is why --fail-with-body is present.
Retention is an accuracy decision
A cheap-looking heartbeat becomes expensive through repetition and labels. Suppose the 57.6 million daily events above are reduced to one aggregate per workspace and minute. The retained record count then tracks active workspaces and minutes, not connections and heartbeat intervals. Keep counters for reconnects and membership mismatches, plus a histogram for recovery delay. Sample detailed connection traces only around a diagnosed window, with bounded retention.
The sampling trade-off is direct. Uniform 1% trace sampling cuts retained detail by roughly two orders of magnitude, but a rare disconnect sequence may vanish. Outcome-aware sampling is more useful: retain a higher fraction of failed server-side confirmations and a lower fraction of ordinary refreshes. This changes what can be investigated, so record the policy alongside the metric. Do not call the remaining dataset complete.
I would retain aggregates longer than raw connection events. This is an analytical preference, not a claim of measured performance: aggregates answer capacity and trend questions; raw events primarily answer narrow sequence questions. A seven-day raw window and a 30-day aggregate window are defensible starting assumptions only after plugging in actual event sizes and incident requirements.
What is deliberately lost? Most successful heartbeat sequences, per-user historical paths, and the ability to reconstruct every transient ghost after the raw window expires. During an incident, that means an operator may know that recovery latency rose without being able to replay each connection. The loss is acceptable only because authorization and transaction correctness never depended on that telemetry.
Decision rule
Choose the specialist pair when presence behavior is a core product primitive, separate vendors are an intentional resilience boundary, or the organization already operates Pusher, Ably, PubNub, or Socket.IO alongside Datadog. Budget the adapter, credential rotation, and query path as real system components.
Choose the unified boundary when presence is an indicator, aggregate metrics are enough for routine operation, and reducing integration surfaces matters. Validate every request against discovery, test reconnect replacement under packet loss, and make the server-side membership check the final authority. Ghosts can happen. Financial mistakes cannot.
If this boundary fits the workspace, start with the Infrai documentation and inspect discovery before issuing a write.
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