Sigma
The AI Apps platform: analytics, apps and agents on governed warehouse data
By Sigma Computing · HQ San Francisco, US · 4.6/5 Value-Position score (estimate)
Positioning guardrails
Best for
- Teams already running a cloud data warehouse that want governed self-service analytics without copying data
- Finance and FP&A groups building planning, forecasting and variance apps on live data
- Operations teams that want AI agents to trigger workflows and write results back to the warehouse
- SaaS companies adding white-label embedded analytics for their own customers
- Enterprises consolidating dashboards, spreadsheets and ad-hoc tools into one governed workspace
Ideal size: 100–5,000+ people · Data-mature company with a governed cloud warehouse and an analytics team
Not for
- Companies with no cloud data warehouse for Sigma to query
- Small teams shopping for a cheap standalone BI tool
- Organizations requiring on-premise or desktop-only deployment
- Buyers who need published, self-serve list pricing before a demo
Value metrics scorecard
Time-to-Value
2–4 weeks
~21 days to first production value
Total Cost of Ownership
On request
Quote-based; no public price list. Vendor routes buyers to sales, with a free trial and help desk available.
Implementation Friction
2/5
Engineering + admin effort required
Value-Position score
out of 5 · model estimate
Full cost breakdown
Mandatory implementation fee
None
Seat tiers
Not published on vendor site
Add-on costs
- None
Company & support
Who is behind Sigma, and how your team gets help once it is live.
Company
- Founded
- Not recorded
- Headquarters
- San Francisco, US
How you get support
- PhoneNot listed
- EmailNot listed
- Live chatPlan not stated
- Support portal / ticketsPlan not stated
- Community forumPlan not stated
- Help centre / docsPlan not stated
- Dedicated account managerNot listed
- In person / on-siteNot listed
- Hours
- Not recorded
- Response time
- Not stated
Vendor docs list live chat, office hours, community and a help desk. Plan-level support tiers, support hours and SLAs are not published.
“Not listed” means the vendor’s public pages don’t mention that channel, not that it is unavailable. Ask about it during evaluation.
Market position
Where Sigma sits against its closest alternatives. Pick any two of cost, speed, friction and buyer score, and up to 9 companies to compare.
Quadrant view
Typical annual cost × Time-to-value
The lines cross at the median of the solutions shown, so about half sit on each side of each line. A dashed ring marks an outlier pinned to the edge; hover for its value.
Companies on the chart 6 / 10
- Sigma
- CaptivateIQ
- Jitterbit
- Coralogix
- TeamViewer
- SPS Commerce
Add or change companies
Up to 10 companies including Sigma. Listed closest first.
Stack fit signal
Compatibility with standard B2B ecosystems.
No supported MCP path today, so it cannot be driven from an AI client.
AI & MCP readiness
What Sigma ships in AI, and what it asks of your ecosystem.
AI features shipped
Sigma Assistant answers natural-language questions with visible logic and turns chat into workbooks; Sigma Agents monitor thresholds, trigger workflows and write back with an audit trail. AI runs on the customer's own cloud warehouse compute and inherits its permissions.
Your data & models
- Trains on your data
- Not recorded — ask the vendor
- Runs on
- Not recorded
- AI pricing
- Not recorded
In your ecosystem
- AI connection
- Not supported
- Model key
- Not recorded
- AI usage audit
- Full audit trail
Compliance attestations
* IAPP AIGP certifies individuals, not products. It means named staff hold the credential — not that the platform does.
Bottom line
Sigma is a warehouse-native analytics and AI app platform from Sigma Computing. It queries your cloud data warehouse directly so permissions, lineage and governance stay at the source, and lets business users build dashboards, reports, embedded analytics and AI apps with chat, a spreadsheet UI, SQL or Python. Sigma Agents run automated workflows and write back with an audit trail, and Sigma acts as a bidirectional MCP client and server. More than 2,000 enterprises use it; pricing is quote-based.
Frequently asked questions
How is Sigma priced, and what should we budget?
Sigma does not publish a price list. Its pricing page routes buyers to the sales team, and a free trial, help desk and community are available. Expect quote-based annual pricing, probably tied to users or usage. Budget separately for warehouse compute, because Sigma runs queries on your own Snowflake, Databricks or BigQuery account rather than a bundled data store.
Do we need a cloud data warehouse to use Sigma?
Effectively yes. Sigma is warehouse-native: it queries your cloud data warehouse directly and inherits its security, permissions and governance. Customers run it on warehouses such as Snowflake and Databricks. There is no proprietary Sigma data store to load your data into, so a cloud warehouse is a prerequisite.
How is Sigma's AI governed and auditable?
Sigma states that AI processing runs on your warehouse compute and respects existing roles, row-level security and masking. Agent actions write back with an immutable audit trail, and Sigma Assistant shows the logic, formulas and filters behind each answer so users can verify results before acting on them.
Does Sigma support MCP for connecting to other AI tools?
Yes. Sigma states it acts as a bidirectional MCP (Model Context Protocol) client and server, described as turning the warehouse into a governed agentic hub: external agents can pull on Sigma context and Sigma can reach external systems, while access stays permissioned and auditable.
How long does implementation take?
Most work is configuration rather than a long deployment: connect a warehouse, model the data, then build. Sigma's documentation says an account and first workbook can be set up in minutes, and one customer replaced nine years of spreadsheets with a governed scorecard in two weeks. Enterprise rollouts still need data modelling, governance and user enablement time.
Can Sigma agents act outside of Sigma?
Yes. Sigma Agents can monitor data thresholds, run on a schedule or interactively, and trigger cross-platform workflows in systems such as Salesforce, Slack or custom APIs, then write results back to the warehouse. Every action inherits warehouse permissions and is recorded in the audit trail.