My results after building a competitive analysis scraper - works but is slow and expensive.

That 30-45 second range per URL is exactly what I'm seeing too with similar setups. It's good to know it's not just me.

The "Wait for page load" tip from later posts is interesting. I hadn't thought to target a specific element like `#pricing-table` instead of waiting for everything. I'm going to try that next.

You mentioned your bill was higher than budgeted. Do you know which step used the most tokens? Was it the page load time or the actual data extraction step?

Glad you found that tip helpful. Targeting a specific element can definitely cut down wait time, but it does introduce a new point of failure if that selector changes. You'll want to pair it with a timeout and maybe a broader fallback selector.

On your question about the token bill, in most setups I've seen, the lion's share of cost isn't from the page load time itself but from sending the full rendered HTML to the LLM for processing. That's why the earlier suggestion about trying a cheap static fetch first is so crucial for cost, not just speed. Have you been able to isolate which part of your flow is generating the most tokens?

You're spot on about the token bill coming from sending full HTML. That's where the real cost hemorrhage happens.

But isolating the exact step is often a red herring in these frameworks. They bundle wait time and processing together into a single, opaque "agent run." You can guess it's the extraction step, but you're usually just looking at total tokens per run, not a clean breakdown.

So you end up optimizing blind. Cutting page load time might not even touch your main cost driver if the LLM is still chewing through a 50k token DOM.

The latency and cost you're seeing are absolutely normal for a naive headless browser implementation. The "Wait for page load" step is likely the culprit for the speed, as others have noted, but I'd focus on the cost structure first.

You mentioned the bill was higher than budgeted. That's because every second of agent runtime, including browser overhead, consumes tokens. The most expensive token usage isn't the navigation step, it's when the entire rendered HTML is passed to the LLM for the "Extract specified elements" task. Your 30-45 seconds includes that costly processing time.

Instead of tweaking wait conditions, consider a pre-processing layer. Before invoking the agent, use a simple HTTP request to fetch the raw HTML. If you can locate the pricing data with a basic parser like BeautifulSoup, you've just avoided the entire agent cost for that page. Only route the failures where the data isn't present in the static HTML to your expensive Relevance AI flow. This decouples cost from latency and gives you predictable per-URL expenses.

For your 50 URLs, profile them first. How many serve the data statically? That percentage dictates your potential savings.

That pre-processing layer idea is solid. It's basically the circuit breaker pattern for scraping costs, and it gives you a clear path to forecasting.

But you've hit on the hidden challenge: maintaining that static parser. If you're checking 50 URLs, you might need 50 different selectors or logic rules. The moment you have to write custom logic per site, the maintenance debt piles up.

A neat trick I've used is to run both paths in parallel for a while and compare results. The static fetch either matches the full agent's output or it doesn't. That builds a reliability score for each site, so you know exactly which ones you can safely offload. Saved me from writing brittle parsers for sites that were just going to change next month anyway.

That's a really helpful clarification about the bottleneck. So even if you cut costs with a hybrid approach, the latency for the fallback cases is still stuck at those 30-45 seconds because of the full browser load.

You mentioned profiling to see where the time is spent. Is there a straightforward way to actually do that in these agent frameworks? Most of the logs I've seen just show total run time, not a breakdown between network wait, script execution, and LLM processing.

I'm trying to imagine how to instrument those specific readiness checks you mentioned. For a pricing page, would you typically wait for a specific table class, or maybe for an element that has a "price" text node inside it?

You're right that most agent frameworks give you garbage logs. They're built for demo dashboards, not production debugging. You need to inject your own timers.

Here's a practical snippet I wrap around critical sections. It's crude but shows you where the seconds go.

```python
import time
start = time.time()
# ... your step to navigate/wait ...
nav_duration = time.time() - start
print(f"NAVIGATION_BLOCKED: {nav_duration:.2f}s")
```

For readiness checks on a pricing page, waiting for a specific table class is too brittle. I wait for *any* element containing a currency symbol and a number pattern. Something like `//*[contains(text(), '$') and matches(., 'd+(.d{2})?')]` in XPath. It's not perfect, but it signals the dynamic pricing widget has probably loaded, which is what you actually care about.

That timing wrapper is exactly how we debug our Zapier tasks when we suspect delays. It's simple but shockingly effective for breaking down the black box.

One thing I've noticed with the currency selector approach is you'll sometimes catch false positives in nav bars or footers that have copyright dates or other numbers. I've had better luck combining it with a parent container check, something like `//div[contains(@class, 'pricing') or contains(@class, 'plan')]` to narrow the scope first.

Might be worth logging both the selector match time and the total HTML size you eventually send to the LLM. You often find the biggest time sink isn't the wait, it's serializing that massive DOM.

Great call on pairing the currency symbol check with a parent container. That's our standard pattern now for exactly the false positive reason you mentioned.

Logging the HTML size is a perfect next step. The serialization time can be wild. We saw a 2MB DOM take 8 seconds just to stringify before any processing even started. That's often the silent killer in the total run time.