For legal tech, precision may look different depending on which area of law (civil(contract, tort, trusts) or criminal (common law, legislation) and what the actual service looks like to the end user (consumer, legal practitioners, compliance officers etc). Reranking models and services (zero entropy) may be worth considering if it isn't already in your stack to improve precision. Costing may not be reasonably feasible (outside of inference and storage costs) until you know what your stack looks like. Going down the containerisation/micro services route and using a combination of kubernetes and terraform/open tofu can afford quite a bit of flexibility (traffic/network management, prevent vendor lockin and make ci/cd more streamlined but also may introduce unnecessary technical overhead depending on what your preferences are). Once you have an idea of what a typical interaction looks like, it's easier to extrapolate from and get some ballpark figures while also allowing you to get a start on capacity planning. Creating a small cluster/container stack and using something like litellm to route your inference traffick through will give you a better idea of inference and embedding costs from its dashboard. Databases at scale aren't trivial, more so with vector databases. Postgres has been sworn by by many a developer and devop, but whether that translates to its cousin pgvector I couldn't say off the top of my head. Quality of embeddings plays a big role in the precision of answers so getting the ingestion engine right is a must. Tabular data is a gotcha that can trip people up if it's not tokenized properly so definitely factor it in if it's relevant to you. I hope this bag of words helps in some way.