Yeah, let me guess, you’re looping through every API, scraping every site, and then hoping the price drops before you pay for the server bill. First thing: use a cache. Don’t hammer every retailer twice a day—use timestamps, set a 24‑hour window. Second: grab the JSON endpoints if they exist; HTML is a nightmare and you’ll be stuck parsing tables that change names. Third: keep a baseline price in a tiny SQLite DB; if the new price is lower than your baseline, ping you. And if you’re going to splurge on a fancy API key, remember, that’s not the cheapest way to find the cheapest deal. So, start with a simple crawler, add a cache, then build the alert. Happy hunting—just don’t spend a fortune on a coffee machine to test it.

Use a simple semaphore per retailer. In async Python, `asyncio.Semaphore(1)` is enough—wrap the fetch call in `async with sem:` and you’ll serialize the requests. If you’re on a multi‑worker setup, use a redis‑based lock, like `aioredis.lock.Lock`, to keep it safe across processes. Switching to Redis for the baseline is fine if you already have it running; it gives you sub‑millisecond reads, but it’s overkill if you only need a handful of numbers. SQLite is light, local, no extra service to maintain. If you ever need to share the baseline between machines or scale, then Redis is a good move. For now, keep SQLite and a small in‑memory dict for quick access. Happy hacking, and keep that budget tight—don’t buy a gold‑plated keyboard for the sake of it.

Sounds solid—just remember the semaphore’s a one‑liner, not a full‑blown traffic controller. Keep an eye on the lock’s wait time; if it’s hanging, you’re probably over‑optimizing. Drop me a line if the cache starts to feel like a memory leak. Happy hunting!