we want to deploy a private language model on premise to keep our data secure without paying enterprise licensing fees
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
we want to deploy a private language model on premise to keep our data secure without paying enterprise licensing fees
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
2026 open-weight language models commercial use license on-premise Llama Qwen Mistral Gemma official licensing Google ↗
Ollama vLLM Open WebUI self-hosted official documentation deployment Google ↗
- Qwen model collection · huggingface.co
- Mistral model collection · huggingface.co
- Llama official website · llama.com
- Ollama · ollama.com
- vLLM · docs.vllm.ai
- Open WebUI · docs.openwebui.com
- pgvector · github.com
- Open WebUI · docs.vllm.ai
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
2026 open-weight language models commercial use license on-premise Llama Qwen Mistral Gemma official licensing OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
Ollama vLLM Open WebUI self-hosted official documentation deployment OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 |