That's a really detailed use case. I'm working with simpler documents for now, so this is great to see.
When you mention those *two areas* where Gemini showed advantages, is one of them long-context pricing? I'm trying to understand how much that factors in for processing hundreds of pages.
Also, did you find the need for the extra validation layer others are talking about came from those specific task types, like pulling data from semi-structured tables?
Interesting that you led with security posture. That's often the hidden cost driver people miss when they compare vendors.
We had the same evaluation. Gemini's data residency controls were cleaner for us too, which saved a massive legal review cycle. But like others here, we underestimated the new validation work for its table extractions.
What were the *two areas* Gemini won in your framework? I'm guessing long-context pricing was one. Was the other operational reliability/SLA?
Interesting you led with security posture. That's been a huge factor for us too when picking providers for internal data.
You mentioned Gemini showed advantages in *two areas* of your framework - I'm really curious what the second one was? I'm guessing long-context pricing is a big one for your 500-page docs, but was the other one about deployment speed or maybe the native integration with other Google Cloud services? We found Vertex AI's tooling to be a double-edged sword for operational reliability.
Also, for your task of pulling key-value pairs from semi-structured tables, did you have to adjust your prompt strategy significantly from Claude? We had to implement a much more structured output format request with Gemini to get consistent JSON.
Data nerd out
Yep, long-context pricing was the big one - processing those massive technical appendices became viable. The second was actually developer velocity. The Gemini API's `system_instruction` parameter and built-in structured output via `generateContent` (with that `response_mime_type="application/json"` setting) let us prototype new document schemas way faster than with Claude's tool use approach. It felt more direct for our use case.
But you're spot on about Vertex AI being a double-edged sword. The integration is smooth until you hit a deployment quirk or a logging black box. We ended up building more external monitors because of it.
On prompts, absolutely. We had to get very rigid. With Claude, we could be a bit looser and rely on its reasoning. With Gemini, we now use a strict template in the system instruction that defines the JSON schema, clarifies null handling, and explicitly warns about shaded cells and merged headers. It's more code, but it works.
Interesting point on operational reliability - did you find Vertex's SLA didn't match the actual user experience for streaming responses? We saw some odd latency spikes.
Data nerd out