When Prompt Batching Made My LLM App More Expensive

Source: Dev.to

I was working on cost optimization for an LLM-based document translation At that point, the LLM translation flow was still very direct: one extracted It worked, but it was not ideal for cost. For a document with many text segments, the number of API calls grew linearly. In simpler terms: Instead of sending one API call for every text segment, we group multiple That was the plan. But in the first real benchmark, the “optimization” made the system more The test used the same input file: File: sample_10p.pdf

Language pair: zh-TW -> en

Model: gpt-4.1-nano

Before batching, the system translated one segment per API call.

Metric No batching

Segments 160

API calls 160

Input tokens 14,287

Output tokens 2,506

Estimated cost $0.0024

Duration 30.4s

This was simple and predictable: 160 segments meant 160 API calls. The problem was also obvious: if I wanted to reduce cost, reducing the number of The first implementation added prompt batching. The idea was to group up to 20 text segments into one request using keyed JSON: keyed_subset = {str(idx): text for idx, text in enumerate(masked_subset)}

kwargs = { “model”: settings.OLLAMA_MODEL_NAME, “messages”: [ {“role”: “system”, “content”: self._sys_batch}, {“role”: “user”, “content”: user_msg}, ], “temperature”: self._temperature, “response_format”: {“type”: “json_object”}, }

At first glance, the result looked better because API calls dropped from 160 to But the cost and latency got worse.

Metric No batching First batching

Segments 160 140

API calls 160 107

Input tokens 14,287 14,876

Output tokens 2,506 4,541

Estimated cost $0.0024 $0.0033

Duration 30.4s 136.2s

Fallback rate 0% 71.43%

So batching reduced API calls by 33%, but increased cost by 37%. This was the confusing part. The dashboard said we had fewer API calls. But the final bill estimate was So the question became: where did the extra cost come from? The batch size was 20. With 140 segments, the system should only need: 140 / 20 = 7 batch calls

But 5 of those 7 batch calls failed validation. When one ID was missing from the JSON response, the old fallback logic retried for i in range(len(subset)): key = str(i) if key in keyed_translations: translated_list.append(keyed_translations[key]) else: mismatch_found = True break

if mismatch_found or len(translated_list) != len(subset): return self._fallback_per_item(texts, tracker)

That means one missing translation could discard 19 successful translations and The reconstructed call count matched the dashboard: 7 batch calls 5 failed batches x 20 per-item retries = 100 retry calls

Total API calls = 7 + 100 = 107

So 100 of 107 API calls were retries. That was the real cost multiplier. The first implementation used: “response_format”: {“type”: “json_object”}

This only asked the model to return valid JSON. It did not guarantee that all required IDs would be present. The prompt said “do not skip any IDs”, but prompt instructions are still In the logs, the missing IDs often appeared near the end of the batch: ID 19 missing ID 18 missing ID 12 missing ID 18 missing ID 14 missing

That pattern was consistent with long structured outputs degrading near the The fix had three parts. First, for the OpenAI endpoint, the response format was changed from json_object to strict json_schema. keys = [str(i) for i in range(n_items)]

return { “type”: “json_schema”, “json_schema”: { “name”: “batch_translation”, “strict”: True, “schema”: { “type”: “object”, “properties”: { “translations”: { “type”: “object”, “properties”: { k: {“type”: “string”} for k in keys }, “required”: keys, “additionalProperties”: False, } }, “required”: [“translations”], “additionalProperties”: False, }, }, }

Now every expected ID is listed as required. For non-OpenAI endpoints, the system still uses best-effort json_object mode Second, fallback became partial. Instead of retrying the whole batch, the code keeps successful translations and missing = [i for i, v in enumerate(translated) if v is None]

if missing: tracker.record_prompt_batch_fallback()

if len(missing) > 1:
    retry_result = self._request_batch_keyed(
        [masked_subset[i] for i in missing],
        context,
        tracker,
    )

still_missing = [i for i, v in enumerate(translated) if v is None]
for i in still_missing:
    translated[i] = self.translate(subset[i], tracker)

Third, the batch request now sets max_tokens and checks truncation: if choice.finish_reason == “length” and len(items) > 1: mid = len(items) // 2 left = self._request_batch_keyed(items[:mid], context, tracker) right = self._request_batch_keyed(items[mid:], context, tracker) return left + right

So a truncated batch is split and retried as smaller batches instead of falling After the fix, the same benchmark was rerun.

Metric First batching Fixed batching No batching

API calls 107 7 160

Fallback rate 71.43% 0.00% 0%

Input tokens 14,876 6,206 14,287

Output tokens 4,541 2,640 2,506

Estimated cost $0.0033 $0.0017 $0.0024

Duration 136.2s 22.1s 30.4s

Processed segments 240 140 160

The fixed version finally achieved the original goal: API calls dropped from 160 to 7 Estimated cost dropped from $0.0024 to $0.0017 Duration dropped from 30.4s to 22.1s Fallback dropped to 0% The lesson is simple: batching is not automatically cheaper. If a batch response can fail partially, the fallback strategy matters as much For structured LLM workflows, these details are important: Use schema enforcement when the endpoint supports it. Do not rely only on prompt instructions for required fields. Keep partial successes. Retry only missing items. Check finish_reason. Measure real cost, not just API call count. In this case, the first optimization reduced requests but increased cost. The real optimization was not just batching. It was making the batch output reliable.