Buyer prompt · published evidence
When should a team buy a dedicated vector database instead of adding embedding search to its existing operational database?
An exact prompt-level view of which reviewed brands surfaced and which public URLs the configured AI models cited. No answer text or tenant data is published.
- Intent
- commercial
- Model providers
- 3
- Categories
- 1
- Last observed
- Aug 4, 2026, 12:00 AM UTC
Competitive outcome
Brands surfaced
Ordered by provider breadth, then total mentions and owned-domain citations. A mention is not an endorsement or a position claim.
| # | Brand | Category | Providers | Mentions | Owned citations | Provider evidence |
|---|---|---|---|---|---|---|
| 01 | Redisredis.io ↗ | Vector DBs | 2 | 0 | 2 | Openai · Gemini |
Model comparison
Answer matrix
One row per configured provider and category snapshot. Hashes prove answer identity without publishing stored answer text.
| Provider | Model version | Category | Brands surfaced | Citations | Unique domains | Answer hash |
|---|---|---|---|---|---|---|
| Openai | openai/gpt-4o-mini | Vector DBs | Redis | 7 | 7 | e9bad510…e71ea |
| Anthropic | anthropic/claude-haiku-4-5 | Vector DBs | None observed | 0 | 0 | dbd1788b…8bb03 |
| Gemini | google/gemini-2.5-flash | Vector DBs | Redis | 6 | 6 | 3961e665…d3ef5 |
URL-derived citations
Sources cited
Hostnames are derived from the returned public URLs. Best position is the smallest citation position observed.