Caching Astrology API Data: Save 95% of API Calls Without Losing Freshness

Most developers overpay for astrology API usage by 10-50x because they do not cache properly. Astrology data has a unique property that makes it one of the most cacheable categories of API data in existence: planetary positions change slowly, birth charts never change, and daily horoscopes are shared across millions of users with the same zodiac sign.

With the right caching strategy, you can serve 100,000 daily active users with fewer than 100 API calls per day for core astrology features. This guide covers the caching patterns that separate cost-efficient astrology products from the ones burning through API credits.

Why Astrology Data Is Uniquely Cacheable

Birth Charts Never Change

A birth chart is calculated from a fixed moment in time (your birth). The planetary positions at that moment will never change. Calculate once, cache forever. A birth chart API call made today returns the same result as the same call made 10 years from now.

Cache TTL: Infinite. Store permanently.

Daily Horoscopes Are Shared

A daily horoscope for Aries is the same for every Aries user on the planet for that day. There are only 12 zodiac signs. Your entire daily horoscope feature requires 12 API calls per day, regardless of whether you serve 100 users or 10 million.

Cache TTL: 24 hours. Refresh once daily.

Planetary Positions Change Slowly

The Moon (the fastest-moving body) changes sign roughly every 2.5 days. Other planets move even slower. Mars stays in a sign for about 6 weeks. Jupiter stays for about a year. Saturn stays for about 2.5 years. The "current planetary positions" endpoint can be cached for hours without meaningful accuracy loss.

Cache TTL: 1-6 hours depending on precision requirements.

Tarot Is the Exception

Tarot draws should be unique per reading. Each draw is a fresh randomized selection from the deck. Do not cache tarot draws. This is where most of your API usage will concentrate if you offer tarot features.

Cache TTL: Do not cache. Each draw is unique.

Cache Key Design

Good cache keys are the foundation of an effective strategy.

Pattern: {endpoint}:{params}:{date}

horoscope:aries:2026-02-25        // Daily horoscope
birthchart:1990-03-15:14:30:40.71:-74.01  // Birth chart (lat/lng)
compatibility:aries:scorpio        // Sign compatibility
planets:current:2026-02-25-14      // Planetary positions (hourly)
moonphase:2026-02-25              // Moon phase (daily)
numerology:life-path:1990-03-15    // Life path number

Key Principles

Include all parameters that affect the response. A birth chart cache key must include date, time, and coordinates. Missing any parameter causes cache collisions (returning wrong data for different inputs).

Include time granularity in the key. For daily data, include the date. For hourly data, include the hour. This ensures the cache naturally expires and refreshes.

Normalize inputs. Convert all coordinates to a consistent precision (e.g., 2 decimal places). Convert all dates to ISO format. Normalize sign names to lowercase. Inconsistent inputs create duplicate cache entries for the same data.

Cache Architecture Patterns

Pattern 1: In-Memory Cache (Simple, Single Server)

Best for: Small to medium apps, single server deployment.

// Simple in-memory cache with TTL
const cache = new Map<string, { data: any; expires: number }>();

function getCached<T>(key: string, ttlMs: number, fetcher: () => Promise<T>): Promise<T> {
  const entry = cache.get(key);
  if (entry && Date.now() < entry.expires) {
    return Promise.resolve(entry.data);
  }

  return fetcher().then(data => {
    cache.set(key, { data, expires: Date.now() + ttlMs });
    return data;
  });
}

// Usage
const horoscope = await getCached(
  `horoscope:aries:${today}`,
  24 * 60 * 60 * 1000,
  () => api.getDailyHoroscope('aries')
);

Pros: Zero infrastructure. Works immediately. No external dependencies.

Cons: Cache is lost on server restart. Cannot share across multiple server instances. Memory grows with cache size.

Pattern 2: Redis Cache (Scalable, Multi-Server)

Best for: Production apps with multiple server instances, high traffic.

import Redis from 'ioredis';
const redis = new Redis(process.env.REDIS_URL);

async function getCached<T>(key: string, ttlSec: number, fetcher: () => Promise<T>): Promise<T> {
  const cached = await redis.get(key);
  if (cached) {
    return JSON.parse(cached);
  }

  const data = await fetcher();
  await redis.setex(key, ttlSec, JSON.stringify(data));
  return data;
}

// Usage
const chart = await getCached(
  `birthchart:${date}:${time}:${lat}:${lng}`,
  60 * 60 * 24 * 365 * 10, // 10 years (effectively permanent)
  () => api.getBirthChart(date, time, lat, lng)
);

Pros: Shared across server instances. Survives server restarts. Built-in TTL management. Fast (sub-millisecond reads).

Cons: Requires Redis infrastructure. Additional operational complexity.

Pattern 3: Database Cache (Persistent, Queryable)

Best for: Apps that want to analyze cached data, build user profiles from astrological data, or need persistent storage without Redis.

CREATE TABLE astro_cache (
  cache_key TEXT PRIMARY KEY,
  data JSONB NOT NULL,
  created_at TIMESTAMP DEFAULT NOW(),
  expires_at TIMESTAMP NOT NULL
);

-- Query with TTL check
SELECT data FROM astro_cache
WHERE cache_key = 'horoscope:aries:2026-02-25'
AND expires_at > NOW();

Pros: Persistent. Queryable (analytics on cached data). No additional infrastructure if you already have a database.

Cons: Slower than Redis for high-frequency reads. Requires cleanup job for expired entries.

Pattern 4: CDN Edge Cache (Global, Fastest)

Best for: Apps serving users globally where latency matters, public-facing astrology content.

If you serve daily horoscopes through a public API or website, put a CDN (Cloudflare, Fastly, CloudFront) in front with cache headers:

Cache-Control: public, max-age=86400  // 24 hours for daily horoscopes
Cache-Control: public, max-age=31536000, immutable  // Birth charts

Pros: Fastest possible response times. Globally distributed. Reduces server load to near zero for cached content.

Cons: Only works for public (non-personalized) content. Cannot cache user-specific data.

Cache TTL Reference Table

Data Type	TTL	Reason	API Calls/Day (10K Users)
Birth chart	Permanent	Never changes	0 (after initial calc)
Daily horoscope	24 hours	Changes daily	12 (one per sign)
Moon phase	24 hours	Changes daily	1
Current planets	1-6 hours	Slow movement	4-24
Sign compatibility	7 days	Static interpretation	~11 (78 pairs / 7 days)
Transit forecast	24 hours	Daily relevance	12 (one per sign)
Numerology (Life Path)	Permanent	Never changes	0 (after initial calc)
Numerology (Personal Year)	365 days	Changes annually	0 (after initial calc)
Tarot draw	None	Must be unique	~500-2000
Dream symbol	30 days	Static interpretation	~10-50
I Ching hexagram reading	None	Should be unique per consultation	~100-500

Total daily API calls for 10K DAU: ~650-2600 (depending on tarot/I Ching usage)

Compare this to no-cache: potentially 10,000+ calls per day for the same feature set.

Advanced Caching Strategies

Prefetch on Schedule

Do not wait for the first user request. Prefetch cacheable data on a schedule:

// Cron job: 6 AM daily
async function prefetchDailyData() {
  const signs = ['aries', 'taurus', /* ... */ 'pisces'];

  await Promise.all(signs.map(sign =>
    getCached(
      `horoscope:${sign}:${today}`,
      86400000,
      () => api.getDailyHoroscope(sign)
    )
  ));

  // Also prefetch moon phase, planetary positions
  await getCached(`moonphase:${today}`, 86400000, () => api.getMoonPhase());
  await getCached(`planets:${today}`, 86400000, () => api.getPlanetaryPositions());
}

First user of the day gets instant response from cache instead of waiting for an API call.

Stale-While-Revalidate

Serve stale cached data immediately while refreshing in the background:

async function getStaleWhileRevalidate<T>(
  key: string,
  ttlMs: number,
  staleTolerance: number,
  fetcher: () => Promise<T>
): Promise<T> {
  const entry = cache.get(key);

  if (entry) {
    const age = Date.now() - entry.timestamp;

    if (age < ttlMs) {
      return entry.data; // Fresh
    }

    if (age < ttlMs + staleTolerance) {
      // Stale but tolerable - serve and refresh in background
      fetcher().then(data => cache.set(key, { data, timestamp: Date.now() }));
      return entry.data;
    }
  }

  // No cache or too stale - must wait
  const data = await fetcher();
  cache.set(key, { data, timestamp: Date.now() });
  return data;
}

Users never wait for API calls. They always get instant responses. Data freshness lags by at most staleTolerance milliseconds.

Cache Warming After Deployment

After deploying a new version (which clears in-memory cache), immediately warm the cache:

// On server start
async function warmCache() {
  console.log('Warming astrology cache...');
  await prefetchDailyData();
  console.log('Cache warmed: 12 horoscopes, moon phase, planets');
}

Cost Modeling

Without Caching

10,000 DAU, each checking horoscope once:

• 10,000 horoscope calls/day = 300,000/month
• Plus birth charts, compatibility, tarot = ~500,000/month
• RoxyAPI Business plan at $349/month (200K requests)
• Overage or Enterprise plan needed

With Caching

10,000 DAU, same features:

• 12 horoscope calls/day + birth charts only for new users + cached compatibility
• ~2,000 calls/day = ~60,000/month
• RoxyAPI Professional plan at $149/month (50K requests)
• Comfortably within plan with room to grow

Savings: 60-80% reduction in monthly API cost from caching alone. And the user experience is better (faster responses from cache).

Frequently Asked Questions

Q: Does caching affect the quality of astrology content? A: No. Birth charts are permanent calculations. Daily horoscopes change once per day. Planetary positions change slowly. Caching at appropriate TTLs delivers the same content the user would receive from a fresh API call. The only exception is tarot (which should not be cached) and I Ching (which should not be cached for consultations).

Q: What about tarot and I Ching? Can I reduce those API calls? A: Not through caching, since each reading should be unique. But you can reduce usage by offering tarot as a premium feature (reducing the number of draws) or by implementing a daily card draw limit per user. For I Ching, similarly limit consultations per day.

Q: How do I handle cache invalidation? A: Astrology data has natural invalidation patterns. Daily data expires at midnight. Birth charts never expire. Planetary positions expire hourly. Use TTL-based expiration aligned with data change frequency. You rarely need manual cache invalidation for astrology data.

Q: Should I cache on the client side, server side, or both? A: Both. Cache on the server side (Redis or in-memory) to reduce API calls. Cache on the client side (AsyncStorage, localStorage) to reduce server calls and enable offline access. Server-side cache serves all users. Client-side cache serves the individual user.

Q: What happens if the API is down? Should I serve stale cache? A: Yes. Stale astrology data is almost always better than no data. A daily horoscope from yesterday is better than an error message. A birth chart never goes stale. Implement fallback logic that serves cached data when the API is unreachable, with a visual indicator showing the data's age.

Q: How do I monitor cache performance? A: Track three metrics: cache hit rate (target 90%+), API calls per day (should decrease as cache warms), and response time (cached responses should be under 10ms). Log cache misses to identify patterns that could be pre-cached.

Start building efficient astrology features. Get an API key at RoxyAPI pricing, check the API documentation for response structures, or explore all products.