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Quaeris Semantic Layer
What Quaeris Semantic Layer is
Quaeris Semantic Layer is a governed analytics platform for teams that need shared metric definitions. It learns patterns from natural-language questions and lets data teams review, certify, version, and govern proposed metrics. Answers can trace metrics from dashboard values to source tables, while recording who approved changes and when. Named warehouse connections include Snowflake, BigQuery, Databricks, Redshift, and Azure Synapse. The platform also names SAP, Salesforce, Oracle, Workday, and Microsoft Dynamics 365 integrations, with availability to confirm with the account team. Customers can connect OpenAI, Anthropic, Google, or Meta models and switch providers while retaining governance definitions. Quaeris queries warehouse data in place, does not use platform data to retrain models, and deletes session data after use. Row-level security and column masking apply at query time. Web access, a REST API, and MCP tools are available. Pricing starts at $1,000/mo, with a free trial.
Who it suits
It suits data teams and cross-functional users requiring governed analytics and certified metrics. Quaeris also identifies regulated industries among its target deployments.
What it does well
- Learns and certifies shared metric definitions
- Traces metrics to source tables
- Supports major cloud data warehouses
- Offers row-level security and column masking
- Provides REST API and MCP tools
What to know first
- No free plan is listed
- Pricing starts at $1,000/mo
- Some integrations require account confirmation
- HIPAA controls are only on the roadmap
Limits
Team costs $1,000 a month and includes one warehouse connection, up to 200 certified metrics, one AI model provider slot, and a 90-day prompt-level audit log. Professional costs $2,500 a month and allows up to five warehouse connections, three model-provider slots, and one-year audit-log retention.
Plans
| Plan | Billed and limits | Price |
|---|---|---|
| Team | $1,000–$2,500 / month; billed annually or monthly, depending on number of Agents/Agent Runs, Users and Source Systems · 1 warehouse connection · up to 200 certified metrics · 1 AI model provider slot · 90-day prompt-level audit log | $1,000 a month |
| Professional | $2,500–$7,500 / month; billed annually or monthly, depending on number of Agents/Agent Runs, Users and Source Systems · Up to 5 warehouse connections · 3 AI model provider slots · 1-year prompt-level audit log | $2,500 a month |
| Enterprise | Custom pricing; contact sales for a configuration scoped to your environment · Unlimited users · unlimited warehouse connections · unlimited AI model provider slots · custom audit-log retention · custom SLA | On request |
As quaeris.ai lists them, read 30 Sept 2026. The maker's own page has the final word.
Getting started
- Open the Quaeris Semantic Layer website
- Start the free trial or contact sales
- Choose Team, Professional, or Enterprise
- Connect a supported warehouse
- Have the data team review and certify proposed metric definitions
- Connect selected AI model providers through the platform
Verdict
Quaeris focuses on governance, lineage, and controlled access across analytics interfaces. Its paid plans differ by connections, model slots, audit retention, and support.
Questions
How much does Quaeris Semantic Layer cost?
Team is $1,000 a month, with monthly or annual billing in a $1,000–$2,500 range. Professional is $2,500 a month with a $2,500–$7,500 range, and Enterprise uses custom pricing.
Is there a free trial?
Yes. Quaeris offers a free trial, but there is no free plan.
Which warehouses can it connect to?
Named connections include Snowflake, BigQuery, Databricks, Redshift, and Azure Synapse.
Which AI models are supported?
Customers can connect OpenAI, Anthropic, Google, or Meta models and switch providers while retaining governance definitions.
What security controls are available?
Quaeris describes row-level security and column masking at query time, with policies intended to carry across the UI, agent, API, and export paths.
How long are audit logs retained?
Prompt-level retention is 90 days on Team, one year on Professional, and custom on Enterprise.
Facts from Quaeris Semantic Layer's own pages
- 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. · quaeris.ai, 30 Sept 2026
- Audit and retention
- Prompt-level audit-log retention is listed as 90 days for Team, one year for Professional, and custom for Enterprise. · quaeris.ai, 30 Sept 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. · quaeris.ai, 30 Sept 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. · quaeris.ai, 30 Sept 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. · quaeris.ai, 30 Sept 2026
- Headquarters
- San Francisco, California, United States · quaeris.ai, 28 Sept 2026
- Learning and certification
- The platform observes natural-language queries in real time, while the customer’s data team reviews and certifies proposed definitions. · quaeris.ai, 30 Sept 2026
- Lineage
- Quaeris says answers can trace metrics from dashboard values to source tables and record who approved metric changes and when. · quaeris.ai, 30 Sept 2026
- Model integrations
- Quaeris says customers can connect OpenAI, Anthropic, Google, or Meta models and switch providers while retaining governance definitions. · quaeris.ai, 30 Sept 2026
- Platform access
- The company offers a REST API and MCP tools for querying governed data from applications, workflows, and compatible AI hosts. · quaeris.ai, 30 Sept 2026
- Purpose
- The Smart Semantic Layer learns metric patterns from user questions and lets a team certify, version, and govern shared metric definitions. · quaeris.ai, 30 Sept 2026
- Support
- Team includes community support and documentation, Professional adds priority support and dedicated onboarding, and Enterprise includes a named Customer Success Manager. · quaeris.ai, 30 Sept 2026
- Warehouse support
- Named warehouse connections include Snowflake, BigQuery, Databricks, Redshift, and Azure Synapse. · quaeris.ai, 30 Sept 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. · quaeris.ai, 30 Sept 2026