AI Batch Processing: Cut Costs in Half

Why Batch Processing Belongs in Every Dev Toolbelt

Here's the thing about AI providers that nobody talks about: they're running massive server farms with tons of idle capacity during off-peak hours. When you send requests one at a time during business hours, you're essentially paying premium prices for guaranteed immediate service—like calling an Uber during surge pricing.

Batch processing flips this around. Instead of demanding instant gratification, you're saying "hey, process these 1,000 requests whenever you have spare cycles, and I'll take the bulk discount." The providers love this because they can optimize resource usage, and you love it because your bill shrinks dramatically.

The dirty secret? Most of what we're building doesn't actually need sub-second responses. We've just gotten into the habit of treating every AI request like it's powering a chatbot, when really we're doing background data processing that could easily wait a few minutes.

ROI in One Table

Let's talk real numbers, because this isn't theoretical. Companies are saving serious money right now:

*50% discount shown; Gemini batch rates can be even better vs OpenAI real-time.

That last row isn't a typo. Companies processing at scale are literally saving the equivalent of 2-3 engineer salaries per year just by changing how they send requests. And we're being conservative with 50% savings—Google's Gemini batch pricing can hit 90% off compared to OpenAI's real-time rates.

Those savings aren't just cost reduction—they're innovation budget. That's money you can spend on better models, more experiments, or actually hiring those engineers instead of paying inflated token prices.

Perfect Fits for Batch Jobs

Let's be honest—most AI work doesn't need to happen instantly. Here are the biggest wins:

E-commerce product descriptions are the perfect example. Say you've got 5,000 new products that need descriptions. You don't need them written one-by-one as customers browse—you need them all done by Monday morning. Batch that job Sunday night and wake up to a completed catalog.

Sentiment analysis is another goldmine. Got 10,000 customer reviews to analyze? Instead of processing them one at a time during business hours, queue them all up and let them run overnight. Same result, half the cost.

Catalog organization makes perfect sense for batching. Whether you're categorizing 50,000 products or tagging inventory by season, this work happens on your schedule, not customer demand.

Other great candidates include blog content generation, customer data analysis, code documentation, and any kind of bulk data processing. Basically, if you're already doing the work in large chunks, you should be batching it.

Simple test: If your business can wait 15 minutes for the results, you should be using batch processing. Most teams discover that 60-80% of their AI spend could be batched once they audit their usage.

"Sounds Great—But My Team Has Real Work to Do"

Okay, here's where most developers mentally check out. Because traditional batch processing is a pain in the ass, and everyone knows it.

The typical flow goes something like this: First, you spend half a day figuring out how to format your data into JSONL without breaking the encoding. Then you're wrestling with cloud storage buckets, IAM permissions, and upload timeouts. Next comes the polling logic—you need to check job status without hammering their API, implement exponential backoff, handle edge cases where jobs just... disappear.

Oh, and don't forget error handling. Jobs fail, files get corrupted, results come back in a different order than you sent them. You need monitoring, alerting, retry logic, and a way to map results back to your original data.

By the time you're done, you've built a whole infrastructure layer that has nothing to do with your product. No wonder teams just eat the cost and stick with real-time APIs.

That's exactly the problem AjaxAI was built to solve.

Meet AjaxAI – Batch Processing as an SDK

Instead of building all that infrastructure yourself, what if batch processing looked like this:

from ajaxai import AjaxAI, BatchJob

client = AjaxAI(api_key="...")
job = BatchJob(model="gemini-2.0-flash")

for product in products:
    job.add_request(
        prompt=f"Write an engaging description for {product.name} at ${product.price}",
        request_id=product.id,
        image_url=product.image_url
    )

job.submit()  # Returns immediately
job.on_complete(lambda results: process_results(results))

That's it. No JSONL files, no storage buckets, no polling loops. You write code that looks like normal application logic, and AjaxAI handles all the infrastructure complexity behind the scenes.

The difference is dramatic. Teams go from spending days building batch infrastructure to shipping production batch jobs in under an hour. That's time you can spend on features that actually matter to your users.

15-Minute Pilot

Don't take our word for it. Here's how to prove the value in one afternoon:

Step 1: Pick your target. Look for any high-volume, low-urgency AI task you're currently doing with real-time calls. Product descriptions, content analysis, data enrichment—anything that processes hundreds or thousands of items and doesn't need instant results.

Step 2: Set up the comparison. Take a representative sample (say, 100-500 requests) and run them through both your current real-time setup and AjaxAI's batch processing. Track the costs, timing, and output quality.

Step 3: Do the math. Calculate what your monthly and annual costs would look like if you switched this workload to batch processing. Most developers are shocked by the numbers.

Step 4: Show the team. Present the side-by-side comparison to your team or stakeholders. The business case usually sells itself.

One e-commerce client did exactly this with their product description pipeline. They were spending $3,200/month on real-time OpenAI calls to process their catalog updates. The AjaxAI batch version cost $280/month for the same work, with better consistency and no rate limiting headaches. That's $35,040 in annual savings from a 15-minute pilot.

The Bottom Line

Batch processing isn't just a nice-to-have optimization—it's table stakes for any serious AI application. The providers have built the infrastructure to handle massive parallel workloads efficiently, and they're willing to share those cost savings with you. The question is whether you're going to take advantage of it.

Every month you delay switching suitable workloads to batch processing is money left on the table. And it's not just your money—your competitors who are already using batch processing can offer the same AI features at 90% lower cost, giving them pricing advantages or much higher margins.

The math is simple: batch processing can cut your AI costs by 50-90% while often improving quality and reliability. The only question is whether you want to build all the infrastructure yourself, or use AjaxAI to ship it this week.

Stop paying rush-hour prices for off-peak work.