Quaeris Semantic Layer
The facts below are what the maker publishes about Quaeris Semantic Layer, checked and dated. Our editors have not written about it yet.
Plans and prices
From Quaeris Semantic Layer's own pricing page, checked 30 Sep 2026.
| Plan | Price | What it gives you |
|---|---|---|
| Team | $1,000 a month | 1 warehouse connection · up to 200 certified metrics · 1 AI model provider slot · 90-day prompt-level audit log · $1,000–$2,500 / month; billed annually or monthly, depending on number of Agents/Agent Runs, Users and Source Systems |
| Professional | $2,500 a month | Up to 5 warehouse connections · 3 AI model provider slots · 1-year prompt-level audit log · $2,500–$7,500 / month; billed annually or monthly, depending on number of Agents/Agent Runs, Users and Source Systems |
| Enterprise | Priced on request | Unlimited users · unlimited warehouse connections · unlimited AI model provider slots · custom audit-log retention · custom SLA · Custom pricing; contact sales for a configuration scoped to your environment |
The facts
Each one read from Quaeris Semantic Layer's own pages, with the date it was checked.
What it does
- Access controls
- The access-control page describes row-level security and column masking applied at query time, with policies intended to carry across UI, agent, API, and export paths.checked 30 Sep 2026
- Audit and retention
- Prompt-level audit-log retention is listed as 90 days for Team, one year for Professional, and custom for Enterprise.checked 30 Sep 2026
- Business system integrations
- The integrations page names SAP, Salesforce, Oracle, Workday, and Microsoft Dynamics 365, and notes that availability should be confirmed with the account team.checked 30 Sep 2026
- Compliance status
- The pricing page says GDPR data-processing controls are included in Enterprise, SOC 2 Type II and ISO 27001 audits are in progress, and HIPAA controls are on the roadmap.checked 30 Sep 2026
- Data handling
- Quaeris states that it queries warehouse data in place, does not use platform data to retrain models, and deletes session data after use.checked 30 Sep 2026
- Learning and certification
- The platform observes natural-language queries in real time, while the customer’s data team reviews and certifies proposed definitions.checked 30 Sep 2026
- Lineage
- Quaeris says answers can trace metrics from dashboard values to source tables and record who approved metric changes and when.checked 30 Sep 2026
- Model integrations
- Quaeris says customers can connect OpenAI, Anthropic, Google, or Meta models and switch providers while retaining governance definitions.checked 30 Sep 2026
- Purpose
- The Smart Semantic Layer learns metric patterns from user questions and lets a team certify, version, and govern shared metric definitions.checked 30 Sep 2026
- Support
- Team includes community support and documentation, Professional adds priority support and dedicated onboarding, and Enterprise includes a named Customer Success Manager.checked 30 Sep 2026
- Warehouse support
- Named warehouse connections include Snowflake, BigQuery, Databricks, Redshift, and Azure Synapse.checked 30 Sep 2026
- Who it is for
- Quaeris describes the Semantic Layer as serving data teams and cross-functional users who need governed analytics, and identifies regulated industries among its target deployments.checked 30 Sep 2026
The company
- Headquarters
- San Francisco, California, United Stateschecked 28 Sep 2026
Where it runs
- Platform access
- The company offers a REST API and MCP tools for querying governed data from applications, workflows, and compatible AI hosts.checked 30 Sep 2026
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