A real-time data feed API is the backbone of any feed product. It is the part customers integrate, benchmark, and blame when the experience falters. For FeedsAI.com, credibility rests on the data feed API being boringly reliable while staying flexible enough to cover new sources. This playbook covers the essentials for an API that delivers signal, not surprises.
Nail the contract before scaling throughput
The API contract sets the tone for every integration. Clarity beats velocity.
- Stable routes and payloads. Version endpoints with /v1, /v2 rather than breaking existing paths. Keep payload shape stable even as new fields appear.
- Canonical fields. Require id, type, title, summary, timestamp, source, confidence, and trace headers. Optional fields include entities, tags, and embeddings.
- Filtering discipline. Offer query parameters for topic, entity, geography, and minimum score. Avoid exotic filters that are hard to maintain.
- Pagination and ordering. Default to time-based pagination with opaque cursors. Clients should not guess offsets; your API should tell them where to resume.
Design the ingestion loop for observability
No real-time data feed API survives without visible ingestion.
- Ingress buffers. Stage incoming events in queues that support backpressure. This keeps spikes from knocking over downstream workers.
- Schema validation early. Validate payloads at ingest and reject bad items with explicit reasons. Store failed samples for investigation.
- Clock hygiene. Normalize timestamps to UTC, store ingest time separately, and track delay between source timestamp and ingest time.
- Dual writes for safety. Persist to durable storage and a fast cache so you can serve hot paths even if one layer fails.
Streaming versus polling
Customers will want both streaming and traditional fetch models. Offer each with predictable behavior.
- Server-sent events or WebSockets. Provide an optional real-time stream for low-latency consumers. Authenticate with the same tokens as the REST API.
- Long polling fallback. Offer efficient polling with conditional requests. Return 304 when nothing changed to cut down on bandwidth.
- Replay controls. Allow clients to replay from a cursor or timestamp. Cap replays to prevent abuse, and expose a clear error when the window is exceeded.
- Rate limits that make sense. Publish limits per token and per IP. Expose remaining quota in headers so clients can adjust gracefully.
Reliability and SLA transparency
The phrase real-time data feed API implies speed. Publish the numbers and back them up.
- Latency budgets. Set an explicit target, such as p95 under 4 seconds from source to API availability. Track it by source and by route.
- Status endpoints. Provide a /status and /metrics endpoint that returns uptime, queue depth, and error ratios. Keep it free and unauthenticated.
- SLA documentation. Offer a readable SLA that covers uptime, latency, and data completeness. Align sales promises with engineering realities.
- Synthetic tests. Run canary checks that pull known test feeds and verify payload shape and timing. Alert early before customers notice.
Security and governance for enterprise buyers
A real-time data feed API will be judged on security posture.
- Token models. Issue scoped tokens with clear roles: read-only, manage sources, manage webhooks. Support short-lived tokens for sensitive routes.
- IP and VPC options. Offer IP allowlists by default and private connectivity for high-assurance customers.
- PII handling. Mark fields that may contain personal data and provide redaction options at query time.
- Audit logging. Log authentication events, parameter changes, and webhook deliveries. Make logs exportable for customer compliance teams.
Delivery quality: retries, ordering, and duplicates
Even with perfect ingest, delivery can falter without clear rules.
- Ordering rules. Promise ordering within a source and topic, not globally. Include sequence numbers to make gaps obvious.
- Retry policy. Retry failed webhook deliveries with backoff and eventually move items to a dead letter queue with human-visible alerts.
- Deduplication hints. Provide stable IDs and a checksum hash so clients can avoid double-processing.
- Content fingerprints. Offer a content hash in headers to detect silent changes during replays.
Documentation and examples that shorten integration time
Documentation is part of the product. Treat it as seriously as the code.
- Quick starts. Provide curl and language snippets that subscribe to the real-time data feed API, fetch a page, and verify signatures.
- Runbooks. Publish operator guides for queue depth, backlog clearing, and webhook replay workflows.
- Error catalog. Document every error code with the action to take. Include sample payloads so developers can test locally.
- Playground. Offer a sandbox token and a small public feed so prospects can validate within minutes.
Operate like a feed company
A domain like FeedsAI.com sets a high bar. Operating discipline proves you can meet it.
- Run incident reviews that produce customer-facing notes, not just internal fixes.
- Keep a changelog for API breaking changes and planned deprecations.
- Offer a named on-call rotation for enterprise accounts with clear escalation paths.
- Track adoption metrics: time to first event, webhook success rate, and retries. Improve them monthly.
Delivering a real-time data feed API is about more than speed. It is about trust, clarity, and accountability. Pair these practices with the FeedsAI.com brand and you get a platform buyers can depend on.