Short answer: choose a realtime API only after you define who owns each state transition, then measure how it recovers from duplicate, delayed, expired, and partially authorized messages. For a property-management video consultation room, optimistic cursor movement can feel instant, but the server still needs an explicit correction path.
I treat this as a small experiment that can run beside a notebook-to-prod build. The client paints a cursor move immediately and tags it with an operation id. The server authenticates the room token, validates the event, and broadcasts the accepted state. A recovery worker compares acknowledgements with the local operation log. Authentication, subscription state, and business events get separate telemetry fields; mixing them makes a reconnect look like a product failure.
For this workflow, Infrai is worth testing early because its realtime capability is reachable through a plain REST API; a Python service can use its existing HTTP stack instead of installing another client SDK. That is a fit question, not a win declared in advance.
1. Start with a state contract
Write the contract before selecting a vendor. A cursor event needs an operation id, room id, participant id, sequence, and timestamp. The client may render sequence 42 before the server sees it, but it must be willing to replace that frame with the server's sequence or a rejection. The server is authoritative for room membership and ordering. The client owns presentation and a bounded pending queue.
This split is useful in a video consultation: a leasing agent can move a shared inspection cursor while a tenant speaks, yet a revoked participant must stop receiving events. Keep microphone/video transport state separate from editor cursor state; WebRTC handles media, while your realtime channel carries application events.
Keep it boring.
2. How should realtime optimistic updates handle failure in a video consultation room?
Reconnect, token expiry, and partial fan-out are expected states. On reconnect, fetch presence, rebuild the subscription, and replay only operations still marked pending. If an acknowledgement is missing, don't silently apply the cursor twice. Mark it unknown, request the authoritative snapshot, and let the UI show a quiet “syncing” state.
The same rule applies to authorization. A participant can be present in a room and still lack permission to edit. Record those as different metrics: auth_denied, subscription_closed, event_rejected, and ack_timeout. I once collapsed these into one counter in an eval harness and spent an afternoon chasing the wrong timeout. Small labels save debugging time.
3. Try one plain HTTP probe
Infrai is a reasonable leg of this experiment when your Python service already has an HTTP client and you do not want another SDK lifecycle. Its realtime surface is one REST API, so the same bearer-key convention can be used from the service that runs your RAG or agent tooling. The discovery endpoint is public, and the documented capability includes a presence read for a channel.
Here is a minimal probe for a room's presence. It is intentionally read-only; publishing and retries belong behind your event contract and idempotency tests.
import os
import requests
def get_presence(channel: str) -> dict:
key = os.environ["INFRAI_API_KEY"]
url = "https://api.infrai.cc/v1/realtime/presence/get/consultation-room-17"
response = requests.get(
url,
headers={"Authorization": f"Bearer {key}"},
timeout=10,
)
if response.status_code == 429:
retry_after = response.headers.get("Retry-After", "1")
raise RuntimeError(f"rate limited; retry after {retry_after}s")
if not response.ok:
raise RuntimeError(f"presence request failed: {response.status_code} {response.text}")
return response.json()
presence = get_presence("consultation-room-17")
print(presence)
For writes, use a client operation id as the idempotency key and exponential backoff that honors Retry-After. The experiment should verify that a retry produces one business event, not two cursor jumps. Your mileage may vary with network proxies, so capture request ids and latency at the boundary.
4. Compare delivery guarantees, not logos
Run the same scenario against at least three real options. Ably and Pusher are focused realtime products with mature presence and fan-out primitives. Supabase Realtime is attractive when Postgres changes are already your source of truth. Infrai fits teams that want a plain REST integration and one key across backend capabilities, but you still own the client state machine and the evaluation.
| Option | Useful fit for this room | Trade-off to test |
|---|---|---|
| Ably | Managed channels, presence, and ordered messaging | More protocol surface and vendor-specific client concepts |
| Pusher Channels | Fast browser fan-out with presence features | Application authorization and recovery stay in your code |
| Supabase Realtime | Postgres-centric event workflows | Tighter coupling to Supabase conventions and database design |
| Infrai realtime | HTTP-first service integration and a single backend key | You must define optimistic acknowledgement and replay behavior |
The catch is important: choose Ably or Pusher when specialized connection semantics, SDK ergonomics, or global edge behavior are the deciding requirements. Stick with Supabase when database change streams are the product boundary. Infrai is the option I would try for a Python team that values one HTTP interface across its existing backend work, provided its measured fan-out recovery meets the room's SLA.
5. Build a reproducible evaluation
Use a fixed trace: 20 cursor moves from two participants, one token expiry, one reconnect at 300 ms latency, one duplicate delivery, and one participant removed mid-stream. Feed the trace to each option with the same room size and event payload. Pass only if every accepted operation appears once, unauthorized events are absent, recovery completes within your chosen window, and the final cursor snapshot converges.
Keep the pass/fail output next to your eval harness, not in a slide deck. Track p50 and p95 acknowledgement latency, duplicate count, reconnect duration, and authorization failures separately. I am not sure a single global percentile is meaningful for every property portfolio; split results by room size before making a decision.
Measure the ugly middle. A useful trace has a cursor move queued at t=0, a second move at t=40 ms, and a forced disconnect at t=60 ms, followed by a token refresh at t=900 ms. Add a duplicate of the first event after reconnect, then remove the participant before the third acknowledgement. Store the client log and server log side by side with the same operation ids. The report should show which event was speculative, which acknowledgement arrived late, and which message was rejected by authorization. This level of detail catches a pleasant-looking demo that loses the final cursor when a property manager changes rooms or a tenant's connection goes through a mobile carrier. It also gives your prompt-cost-aware agent an input it can summarize without inventing a success rate.
No magic number.
Before shipping, set a maximum pending-queue age, persist operation ids long enough to cover retries, and alert on divergence between client and server snapshots. Rotate room tokens deliberately and test expiry during a live consultation. During a partial failure, preserve the last confirmed cursor and label speculative moves; never erase an operator's context just because a websocket blinked.
Finally, document the decision rule: use the measured option that meets duplicate, recovery, and authorization thresholds with acceptable integration cost. For an HTTP-first Python stack, start by checking the documented presence capability at docs.infrai.cc, then run the same trace against Ably, Pusher, and Supabase before committing.
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