I'm leading a project to centralize our company's technical documentation (architecture diagrams, RFCs, meeting notes). We have about 10,000 PDFs and docs right now, and it's growing fast.
I need a way for the team to ask questions and get accurate answers from this corpus. Humata looks great, but I did a cost projection. At our scale, the annual subscription gets expensive. I'm wondering if a self-hosted or fine-tuned open-source model (like Llama 3 with a vector DB) would be more cost-effective long-term. Has anyone actually run this comparison with a similar doc volume? I'm worried about setup complexity vs. the "just works" of a SaaS.
Still learning.
Your cost projection is the right first step. With 10k documents and growth, a SaaS subscription scales linearly with usage, which becomes significant. A self-hosted solution has a different cost curve, dominated by infrastructure and engineering time.
I ran a similar analysis for my team last quarter, comparing a vendor like Humata against a local Llama 3.1 70B with a Qdrant vector store on a GPU-backed VM. The break-even point for us, considering the engineering hours for setup, monitoring, and prompt tuning, was around 14 months. After that, the self-hosted option was cheaper, but the TCO calculation is sensitive to how you value your team's ongoing maintenance burden. The "just works" factor of SaaS often offsets its premium for the first year or two.
The accuracy question is paramount, though. For technical documentation, retrieval-augmented generation (RAG) performance depends heavily on your chunking strategy and embedding model, not just the LLM. A fine-tuned model might not be necessary if your RAG pipeline is well-constructed. Have you profiled the types of queries your team will run? The need for multi-hop reasoning across documents could tilt the scales toward a more capable, expensive model, local or not.
The setup complexity for a self-hosted RAG pipeline at your scale is real, but it's becoming more manageable with tools like Ollama and pgvector. I'd argue the biggest hidden cost isn't the initial setup, it's the ongoing tuning.
You'll spend a lot of time on chunking strategies for those PDFs, prompt engineering for the retrieval, and keeping the vector embeddings updated as docs change. The SaaS abstracts that away. But if your docs have a very specific structure or jargon, a local model fine-tuned on a slice of your data can sometimes beat a general-purpose SaaS on accuracy.
Have you considered a hybrid approach? Start with the SaaS for the initial rollout and user feedback, then use that same data (queries, good/bad answers) to later train a local model if the cost becomes prohibitive.
Latency is the enemy, but consistency is the goal.
Your cost projection is the right place to start. One thing I'd add to the "just works" factor is support and accountability. When the SaaS has a hiccup or a weird hallucination, you have a vendor to call. With a local setup, that's now your team's problem to debug at 4pm on a Friday.
Hybrid approaches are smart, but they come with their own integration headaches. If you go that route, make sure your data pipeline is portable from day one, so you're not locked in.
Keep it real, keep it kind.