The most cost-effective way is usually:

1) Don’t power pages directly from the raw API

Instead, build a cached data layer between your pages and the financial data provider.

Why:
If every page load triggers multiple API calls, costs and rate-limit issues explode fast. A cache lets you:

• serve most requests without hitting the API
• batch/refresh data on a schedule
• reduce duplicate requests for the same symbol

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2) Use a tiered cache strategy

For stock pages, most data can be split by freshness:

• Static-ish data (company profile, sector, exchange): refresh daily/weekly
• Delayed market data (daily OHLC, fundamentals): refresh every few minutes to hours
• Near-real-time data (quotes, bid/ask): cache very briefly, maybe 5–30 seconds
• Intraday charts: precompute and store, don’t regenerate on request

Typical stack:

• Redis for hot, short-lived cache
• Postgres/MySQL or a document DB for persisted snapshots
• Object storage / CDN for chart payloads or pre-rendered assets

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3) Pre-fetch and batch by symbol

Instead of fetching per page view:

• poll in bulk for your most-viewed symbols
• refresh only the fields you actually display
• use provider endpoints that support batch queries

This cuts costs because one bulk request can replace many single-symbol requests.

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4) Choose a pricing model that matches your usage

Financial data APIs often charge by:

• request volume
• number of symbols
• real-time entitlement
• market/data package

Usually cheapest at scale is:

• paid plan with generous batch limits
• daily snapshot/fundamentals endpoint
• separate provider for real-time quotes only if needed

If your pages don’t need tick-level freshness, avoid real-time plans.

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5) Serve “good enough” freshness

Most stock pages don’t need sub-second updates.

A cost-effective pattern:

• quotes: refresh every 15–60 seconds
• intraday chart data: every 1–5 minutes
• fundamentals: daily
• company metadata: weekly/monthly

If users can tolerate a slight delay, you save a lot.

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6) Make pages data-light

Reduce how much you fetch:

• don’t load every chart timeframe upfront
• lazy-load secondary widgets
• show only top metrics initially
• paginate news/filings
• avoid fetching unused endpoints

The fewer API calls per page, the cheaper the system.

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7) Use an internal “symbol snapshot” table

A very scalable pattern is to maintain a table like:

• symbol
• last_price
• day_change
• volume
• market_cap
• pe_ratio
• updated_at

Then your page reads from that table, while a background job keeps it fresh.
This turns expensive request-time API calls into predictable background jobs.

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8) Monitor cache hit rate and API spend

At scale, the biggest savings often come from:

• improving cache hit rate
• reducing duplicate symbol fetches
• identifying low-traffic symbols that don’t need frequent refresh

Track:

• requests per symbol
• page views per endpoint
• cache hit ratio
• API cost per 1,000 page views

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Best practical architecture

If I had to recommend one cost-effective setup:

• Background workers poll the API in batches
• Redis serves hot values
• Database stores snapshots and historical data
• CDN caches page assets and chart JSON where possible
• Frontend reads from your own backend, not the external API directly

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Rule of thumb

The cheapest approach at scale is:

batch + cache + precompute + background refresh

Not:

fetch live from the API on every page load

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If you want, I can sketch a reference architecture or help compare different financial data providers’ pricing models for this use case.