Open-source DeepResearch – Freeing our search agents
What Happened
Open-source DeepResearch – Freeing our search agents
Our Take
Honestly, this is just the open-source treadmill spinning again. We're spending all this time building custom RAG pipelines just to avoid paying for the giant proprietary APIs. It's freeing up our agents from being locked into vendor lock-in, which is the only real win here. I don't see a magic bullet, but it means we control the inference costs and the data pipeline. We'll still spend weeks debugging vector databases, but at least we aren't begging a monolithic service for a price cut.
Look, the real value isn't the code itself; it's the reduction in operational friction. If we can run complex research agents locally or on cheap cloud instances instead of hitting a $100k API wall, that’s a tangible saving. It's incremental, but it's necessary for scaling our internal tooling without letting the big players dictate our budget.
What To Do
Start prototyping with open-source embedding models immediately to assess cost savings.
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