Webhooks, polling or both? Design SaaS updates around the freshness your users need

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A customer opens your SaaS dashboard and sees a document job marked “processing.” The source system finished it several minutes ago. The page loads, but the customer cannot tell whether to wait, download the result or contact support.

Choosing between webhooks and polling starts with that gap. How stale may the displayed status become? What happens when a change never arrives? Does the product need the latest state, or evidence of every transition that led there?

For many integrations, the answer is connector-specific. Use notifications where the source supports the changes you need; use polling where it does not; add reconciliation where the consequences of missed information justify a recovery path.

This guide follows Northline Documents, a fictional Toronto SaaS product that shows Canadian and US customers the progress of long-running document jobs. Its connectors, workload and incidents are invented teaching examples. The result is an architecture decision, request budget and recovery trace that your team can adapt without assuming every provider behaves alike.

1. Define the freshness promise before choosing a transport

Northline displays four source states: queued, processing, complete and failed. Its first release shows current status and a link to the finished document. It does not promise a complete audit trail of every intermediate transition. That distinction determines what recovery must achieve.

The product team proposes 60-second freshness for actively watched jobs and finding missed current-state changes within an hour of restored access after an interruption. These are fictional requirements, not measured performance or universal service levels. Is an hour of uncertainty acceptable for a waiting customer?

Separate three clocks. Source time is when the provider changes the job. Observation time is when your system receives or fetches evidence. Display time is when the customer’s view reflects that evidence. A quick HTTP response measures only part of the journey. A slow worker, stale cache or disconnected browser can leave the customer waiting after the update reaches your infrastructure.

Measure source-to-display delay where trustworthy source timestamps exist. Where they do not, report what you actually know: time since the last successful check, queue age and time since the last applied update. “Checked 20 seconds ago” does not mean “this state changed 20 seconds ago.” A source may itself expose changes late.

For Northline, a delayed status is inconvenient; an incorrect “complete” status can be more damaging if it exposes a result that is unavailable or belongs to another job. Freshness cannot outrank correct identity, authorization or source validation. Displaying “last confirmed processing; connection delayed” may be a better product choice than confidently showing an unverified result.

Write the consequence beside each requirement. If stale data merely postpones a download, a modest refresh interval may be enough. If a transition starts another consequential action, the design needs stronger effect controls and explicit recovery rules. Faster delivery does not settle those rules.

Finally, separate an ambition from a guarantee. A 60-second poll interval cannot by itself guarantee a displayed update within 60 seconds: scheduling, source visibility, pagination, network time and processing also consume the budget. Allocate room for them, test the whole path and describe degraded operation honestly.

2. Read the source contract, one connector at a time

Northline has two fictional source integrations. Connector A offers signed change notifications, a current-job lookup and an account-level current-job list for reconciliation. Connector B offers a paginated list of changed jobs, but no notifications. Neither capability should be inferred from the other.

For each connector, build a capability record with links to the exact API version and account edition being implemented. Record event coverage, identity fields, ordering promises, retry policy, replay access, retention, read limits, pagination, change cursors and deletion behaviour. Mark an unanswered question as unknown. “Probably supported” is not a recovery mechanism.

Event coverage needs particular care. A source may notify on completion but not on failure, cancellation, document replacement or permission changes. A notification endpoint existing does not establish that every relevant state transition is covered. Compare its event catalogue with Northline’s actual display requirements.

A notification may contain the full state or merely say that a job changed. In the latter case, Northline must fetch the job, and those reads belong in the budget. Even a full payload may be unsuitable as current truth if an older delivery arrives after a newer one. Decide whether to apply versioned payloads or treat notifications as prompts for authoritative reads.

For polling, ask what “changed since” means. Is the cursor a durable position in a change stream, a timestamp filter or a temporary pagination token? Does the source preserve a consistent result set while you traverse pages? Can a deleted job disappear before the client learns it existed? Does the API include a tombstone or another explicit deletion signal?

A missing record can mean deleted, unauthorized, filtered out, temporarily unavailable or absent from this page. Northline must not turn every disappearance into a deletion. Interpret response codes and collection semantics according to the actual source contract.

Also establish the scope of a limit. Twenty customer connections may have twenty independent allowances, one shared app allowance or overlapping limits. A request rate that looks harmless per tenant may exceed a shared boundary. Keep authentication identity, rate-limit scope and customer identity as separate fields in the capability record.

If Connector B cannot expose a required transition, no amount of webhook infrastructure on Northline’s side will create that missing source capability. The team must change the requirement, obtain source support or choose another integration boundary.

3. Choose the simplest transport that meets the documented need

Polling asks the source whether something changed on a schedule. Webhooks ask the source to notify a receiving endpoint when a subscribed event occurs. A hybrid combines notification-led updates with deliberate reads, often to recover current state.

Polling-only fits Connector B because it is the available mechanism. It can also fit a source with webhooks when changes are infrequent, the permitted staleness is generous and the team already has a reliable scheduled job. Operating one understandable polling path may be preferable to operating a receiver, subscription lifecycle, queue and recovery process for little user benefit.

Webhook-led delivery fits Connector A’s active-job experience if the relevant events arrive quickly enough and the receiver can keep up. It avoids asking every idle connection the same question repeatedly. But an event subscription is not evidence that the customer’s dashboard is current. Missing coverage, failed delivery and delayed processing remain possible.

Webhook-only is a defensible choice only when the required outcome and the source’s recovery guarantees support it. For example, a non-critical notification feature may tolerate an occasional lost update under an explicit product decision. Northline’s persistent status display has a different requirement: the team wants a path to discover a missed completion without waiting for another event.

Northline therefore chooses a provisional hybrid for Connector A: events drive the normal update path; hourly reconciliation checks for missed current-state changes. Connector B uses polling, with its interval selected against the freshness requirement and source limits.

Polling creates scheduled read work even when nothing changes, while webhooks introduce inbound validation and delivery handling. Hybrid recovery adds reads back into the webhook design and makes overlap intentional. Each route needs clear ownership of failures and a way to prevent one path from undoing another’s newer result.

Do not automatically add frequent polling “for safety” behind an event receiver. If it repeats the full polling workload while adding webhook infrastructure, it may provide little economic advantage. State exactly what reconciliation detects, how quickly it must detect it and which source endpoint can answer that question completely.

4. Calculate request work without inventing a price

Northline’s planning model has 20 customer connections. For this synthetic baseline, one page of the scheduled list endpoint covers every relevant job on each connection, so each check takes one outbound request. A source offering only per-job lookups needs a budget based on the number of jobs checked. Pagination, retries and separate job fetches are excluded from the baseline; actual source capabilities and rate limits must be verified.

There are 86,400 seconds in a 24-hour day. At a 60-second interval, each connection receives 86,400 ÷ 60 = 1,440 checks. Across 20 connections, that is 20 × 1,440 = 28,800 scheduled outbound requests per day.

At five minutes, the interval is 300 seconds. Each connection receives 86,400 ÷ 300 = 288 checks; 20 × 288 = 5,760 scheduled outbound requests per day. The difference is 23,040 scheduled requests, or exactly 80% of the one-minute baseline. That is a workload reduction, not an established invoice reduction.

For the third row, assume 400 actual changes produce 400 inbound notifications, with no duplicate deliveries in this baseline. Hourly reconciliation creates 20 × 24 = 480 outbound requests. Keep the 400 notifications and 480 reads in separate columns. Adding them produces 880 interactions of mixed types; it does not prove 880 billable API calls.

If every notification requires one job lookup, add 400 outbound fetches. The hypothetical outbound total becomes 480 + 400 = 880 reads, alongside 400 inbound notifications. This is a different scenario from the baseline. Reusing “880” without naming the units would conceal an important architectural assumption.

Now add a pagination sensitivity test. If each hourly reconciliation requires exactly three pages per connection, it uses 20 × 24 × 3 = 1,440 outbound requests. Add the same 400 event-triggered fetches and the model reaches 1,840 outbound reads. Actual page counts vary with source behaviour and workload; three is an illustrative input, not a benchmark.

Record retries, duplicate-triggered fetches, initial backfill and operator-triggered refreshes separately. A provider may charge per request, operation, task, data volume or subscription tier. Infrastructure and support also have their own costs. Until current billing terms and the actual workload are known, the honest output is a work budget with units, not a dollar-saving claim.

5. Understand what the polling interval does and does not predict

Under a simplified model, a change occurs at a uniformly distributed point between two successful polls. Assume immediate source visibility, perfectly regular polling and immediate processing. The wait until the next poll then ranges from nearly zero to nearly one full interval, with an expected delay equal to half the interval.

For 60-second polling, the model gives 30 seconds of expected detection delay. For 300-second polling, it gives 150 seconds, or 2.5 minutes. These are exact results under the stated assumptions, not measured latency and not a promise about any individual job.

Consider two invented changes after a poll at 10:00:00. One happens at 10:00:01 and waits 59 seconds for the 10:01:00 poll. Another happens at 10:00:59 and waits one second. The average across these two examples is 30 seconds, but two selected events do not establish a real-world distribution.

A nightly batch that always completes just after the scheduler runs breaks the uniform-arrival assumption. So does source visibility that lags the underlying change. If the poller is paused by rate limits, the next actual check may be much later than its configured time. Instrument actual gaps rather than inferring performance from the schedule setting.

Northline should separately measure scheduled start, actual start, successful source response, state commit and dashboard refresh. That decomposition reveals whether a longer interval, a backed-up queue or a browser refresh policy caused a missed freshness target. Increasing poll frequency will not fix a UI that never consumes the new state.

Hourly reconciliation has the same limitation. A missed event immediately after a successful pass could remain undetected for almost an hour before the next pass even starts. Reading pages and updating the UI adds time. If Northline requires recovery inside a hard 60-minute end-to-end window, scheduling the check every 60 minutes leaves no margin.

The team can choose a shorter reconciliation interval, prioritize actively watched jobs or relax the recovery requirement. Each choice belongs in the decision record. An on-demand refresh can help a waiting user, but it also needs permissions, rate limiting and request accounting. It should not become an unbounded “refresh until it works” loop.

6. Make the webhook receiver a durable boundary

For Connector A, Northline’s proposed receiving path is deliberately small: validate the delivery using the source’s documented method, establish the connector identity, durably record accepted work and acknowledge within the source’s deadline. Workers then fetch or apply state and update the read model used by the dashboard.

The key boundary is durable acceptance. If the endpoint acknowledges success before accepted work is safely recorded, a crash can lose the update while the sender believes delivery succeeded. If it waits for every downstream action before responding, slow processing can exceed the delivery deadline. The implementation must make its accepted-work guarantee explicit and test crash points around it.

A signature check authenticates the documented delivery mechanism; it does not automatically settle tenant authorization, schema compatibility, replay handling or business meaning. Resolve the source account through trusted subscription configuration. Do not let an arbitrary tenant field in an incoming payload choose which customer’s records the worker may change.

A provider-specific example makes the documentation requirement concrete. GitHub recommends HMAC-SHA256 validation using the X-Hub-Signature-256 header, the configured secret and the original payload. Its examples use timing-safe comparison, and its troubleshooting guidance warns against modified payloads or headers. These are GitHub instructions, not a universal signature format. GitHub signature validation

GitHub’s current guidance also requires a successful response within 10 seconds and identifies deliveries through X-GitHub-Delivery; requested redelivery retains the original identifier. Northline’s fictional Connector A must have its own documented deadline and identity semantics. GitHub webhook best practices

Keep secret material out of logs and URLs. Record enough non-secret context to investigate: connection, source job, delivery identity where available, validation outcome, acceptance time and processing result. Restrict payload retention to what the application and recovery process need, especially when documents or customer data could be included.

The receiver also needs bounded capacity. A burst of legitimate updates should not allow one customer to starve every other connection. Queue monitoring, per-connection fairness and backpressure belong beside validation in the operational design. An endpoint returning success while its oldest accepted work grows indefinitely is not meeting a freshness promise.

7. Prevent duplicates and old state from creating new damage

Northline receives event E-71 saying job J-204 reached version 12, complete. It commits that state. Later, the same event arrives again. Later still, event E-70 arrives with version 11, processing. Three deliveries do not represent three valid state changes to apply in arrival order.

A delivery or event identifier helps recognize a repeat within the source’s identity contract. A source version helps determine whether a job snapshot is newer than the stored one. Deduplicating E-71 does not stop a distinct but older E-70 from overwriting version 12.

Where the source provides a comparable per-job version, the proposed state update can reject versions older than the stored version and treat an equal version as already represented. Perform the comparison and write with concurrency control; a separate read-then-write check can race with another worker. Preserve a newer state even when polling and webhook processing meet at the same time.

Do not invent an ordering rule from status names. A provider might reopen a job, replace its result or move a previously failed job back into processing. Nor is an arrival timestamp necessarily a source version. If the source has no trustworthy ordering field, favour an authoritative current-state fetch and prevent overlapping fetches from applying stale responses. The exact strategy depends on that API’s consistency contract.

Deduplication is also different from exactly-once business effects. Suppose Northline later adds a customer email when a document completes. A worker could send the email and crash before recording that it did so. Retrying the event might send it again even though the event-processing table is otherwise well designed.

Treat state application and external effects as separate responsibilities. A transactional outbox can couple a committed local state change with a durable pending action. An external recipient that supports a stable idempotency key may help suppress repeated execution. Neither phrase alone proves end-to-end exactly-once effects: the actual transaction boundary, retention, retries and recipient behaviour must support the claim.

For the first release, Northline keeps the example narrower: update a status projection and expose the result link only after validation. Tests still include duplicate deliveries, reversed versions and concurrent reconciliation. If later requirements introduce notifications, billing or other side effects, reopen the decision rather than assuming the existing duplicate filter covers them.

8. Recover a missed update without inventing its history

At 09:10 UTC, Northline has confirmed J-204 at version 11, processing. At 09:12, the fictional source changes it to version 12, complete. Its notification fails to reach Northline. At 10:00, reconciliation reads the account-level current-job list and finds authoritative version 12. At 10:00:02, the dashboard projection is corrected.

This synthetic trace demonstrates current-state convergence. It does not prove that Northline observed every event between versions 11 and 12. During a longer outage, a current-state endpoint exposing only the latest state cannot reconstruct missing intermediate transitions or their precise times.

A product that needs complete event history requires a source that preserves retrievable history for the relevant period, plus a consumer that stores and checks it appropriately. A current snapshot can answer “what is true now?” while being unable to answer “exactly when did each transition occur?” The architecture decision must name which question the product promises to answer.

For Connector B, safe reconciliation also depends on cursor handling. Complete the required pages, durably apply the results and only then advance the checkpoint according to the source’s documented semantics. If page two fails, do not claim that the whole window is covered because page one succeeded. Retrying an overlap should be safe for already-applied records.

If a cursor expires, follow a documented reset or backfill path and mark freshness uncertain until coverage is restored. Do not substitute the current time as a new checkpoint and silently discard the gap. A full scan may recover existing current state, but it may still miss deleted records or transitions absent from the current collection.

Real providers also differ in recovery windows. GitHub does not automatically redeliver failed webhook deliveries; its documentation describes manual or programmed recovery, with delivery-management API availability differing by webhook type. Its documented redelivery workflow permits redelivery of deliveries from the previous three days. That workflow’s window is not a universal limit for other recovery mechanisms or webhook providers. GitHub failed deliveries · GitHub redelivery window

9. Operate the freshness promise, not just the endpoint

Northline needs visibility into both transport paths and the state they produce. Monitor unsuccessful source reads, invalid deliveries, accepted-work age, checkpoint age, state-application failures and reconciliation mismatches. Add user-visible freshness where the source permits trustworthy measurement. A healthy receiver alone cannot establish that the product is current.

Give each signal a response. If a subscription stops receiving events, first distinguish a quiet account from an expired subscription or disconnected source. If reconciliation discovers repeated mismatches, investigate event coverage and receiver failures before shortening every interval. If one connection has a growing backlog, isolate its workload while preserving progress for the others.

The request budget must also survive an outage. A restart that immediately polls every customer, backfills every page and retries every queued fetch can overload a shared limit. Spread normal schedules, prioritize recovery deliberately and respect the source’s documented backoff behaviour. Reserve capacity for the reads needed to repair state rather than consuming the whole allowance on routine checks.

GitHub illustrates why a single requests-per-hour number is insufficient: its REST limits depend on authentication and include additional secondary limits. Its documentation directs clients to response headers and specifies how to respond to limit errors. Those details must be verified for the actual provider and authentication mode being used. GitHub REST rate limits

Operational cost includes scheduled reads, event-triggered fetches, inbound handling, queue storage, processing, backfill and human investigation. Some costs rise with connections; others rise with changes, page count or incidents. Separate those drivers before comparing architectures. The cheapest-looking request total can conceal an expensive recovery procedure.

Before release, rehearse a missed event, duplicate event, older version, failed page, expired cursor, revoked authorization and stalled worker. Define the expected displayed state, recovery action and evidence for each. These are proposed tests, not executed results. Record what the actual implementation demonstrates and what remains dependent on the source.

For a Toronto team supporting customers across Canada and the US, ownership should cover the hours in which the product promises fresh information. Store operational timestamps consistently and display customer-facing times clearly. Geography does not change delivery semantics, but support coverage and customer expectations can expose an assumption that was invisible during a local daytime test.

10. Keep a decision record that can change with the product

Northline’s first decision is provisional because its source contracts and user requirements still need implementation testing. The record below captures the reasoning without pretending the fictional connectors have production evidence.

Attach the request ledger and test results to this record. Name the person responsible for the product requirement and the person responsible for the connector. Include the documentation version and date checked. When a source changes a subscription rule or a customer needs a stricter freshness commitment, the team can see what must be reconsidered.

Reopen the decision when event coverage changes, a required transition is missing, rate limits become restrictive, reconciliation repeatedly finds drift, page counts grow or the product starts triggering consequential side effects. Complete historical evidence changes what the integration must preserve, not just how quickly it refreshes.

The next useful output is a short implementation scope: source systems, acceptable staleness, event availability, recovery requirements, identity rules and unresolved constraints. A supported connector or a simple scheduled job may already meet that scope. Custom development is justified when the product’s needs and the available interfaces require it.

If your team needs help turning those decisions into a working SaaS integration, bring that record to a custom software development discovery conversation. Custom-scoped engagements; proposal after discovery.

The aim is a product whose status you can explain: what the source last confirmed, how the update reached the customer and how the system finds its way back when a message is missed.