Compare
ContextIQ vs. building it yourself.
Most teams reach for a vector database and a pile of prompt glue. Here's how ContextIQ compares to the usual ways of answering questions over your documents.
| Capability | ContextIQ | Roll-your-own RAG | General LLM API | Legacy doc search |
|---|---|---|---|---|
| Citation-aware answers | Yes | Partial | No | No |
| Multi-model switching | Yes | Partial | No | No |
| Sub-200ms p99 latency | Yes | Partial | Partial | Yes |
| Built-in eval suite | Yes | No | No | No |
| Structured output (typed) | Yes | Partial | Partial | No |
| Webhooks + SDK | Yes | Partial | Partial | Partial |
| Setup time | Minutes | Weeks | Days | Months |
| Infra to maintain | None | Vector DB + pipeline | Prompt glue | Search cluster |
Integrations
Connects to where your documents live.
Point ContextIQ at your sources, call one endpoint, and pipe answers into the tools your team already uses.
- S3
- Google Drive
- Notion
- Confluence
- Slack
- Zapier
- Webhooks
- REST API
- Python SDK
- Node SDK
- LangChain
- Postgres
- Snowflake
- GitHub
- Zendesk
- SharePoint
Skip the infra. Ship the feature.
Start free, no credit card. Your documents, answered in milliseconds.