The most cost-effective setup is usually:

1) Prefer polling over streaming unless you truly need sub-second delivery

For “near-real-time,” polling every 1–5 minutes is often much cheaper and simpler than a persistent streaming connection or high-frequency webhook infrastructure.

• Cheap enough for most news/sentiment use cases
• Easier to retry, deduplicate, and batch
• Lets you control API usage and avoid overfetching

2) Use a two-stage pipeline

Instead of running sentiment on every article immediately and individually:

1. Ingest headlines/snippets first
2. Filter for relevance
3. Run sentiment only on items that matter

This reduces API calls and compute costs a lot.

Example filter criteria:

• company/ticker match
• keywords
• source reliability
• recency window
• duplicate similarity check

3) Batch requests wherever possible

If the sentiment API supports batching multiple texts in one request, use it.

Benefits:

• lower per-item overhead
• fewer network calls
• better throughput
• often cheaper in billed units

4) Deduplicate aggressively

News APIs often return the same story via syndication or updates.

Use:

• normalized URL
• title similarity
• source + timestamp
• content hash

This can cut costs dramatically.

5) Cache results

Cache sentiment for:

• identical article text
• repeated headlines
• same URL/story ID

If the same story reappears, reuse the previous sentiment score.

6) Use a lightweight sentiment model first, expensive one second

A cost-effective pattern:

• Step 1: cheap lexical or rules-based sentiment / fast model
• Step 2: only send ambiguous or high-value items to a premium API/model

This “cascade” approach reduces spend while preserving quality where it matters.

7) Store and process incrementally

Keep a watermark for the last fetched timestamp and only request new items since then.

• avoids reprocessing
• reduces API calls
• simplifies operational logic

8) Choose an API plan with predictable pricing

If your volume is stable:

• fixed monthly tier is often cheaper than per-call
• negotiate enterprise pricing if usage grows
• watch for hidden costs:
  • per-article limits
  • enrichment fees
  • historical lookup charges
  • webhooks/streaming premiums

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Best practical architecture

A very cost-effective near-real-time stack looks like this:

1. Scheduler runs every 1–5 minutes
2. News API fetches recent items since last watermark
3. Deduplication + relevance filter
4. Batch sentiment API call on filtered items
5. Store results in DB/cache
6. Push alerts/streams to downstream users if needed

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When streaming is worth it

Use true streaming only if:

• latency matters a lot
• volume is low to moderate
• the vendor’s stream pricing is reasonable
• you need instant alerting on breaking news

Otherwise, polling is usually the best cost/performance tradeoff.

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Rule of thumb

If you want near-real-time with low cost, the most cost-effective approach is:

poll every 1–5 minutes + deduplicate + filter + batch sentiment analysis + cache results

If you want, I can also sketch a concrete low-cost architecture for AWS/GCP/Azure or recommend a design based on your expected article volume.