Pecan AI

Pecan AI is a paid no-code predictive analytics platform for business teams that need churn, demand, lifetime value, fraud, lead scoring, and campaign predictions from structured data, with public annual pricing that starts at $760 per month and packaged tiers that scale by monthly prediction batches, storage, support, and SSO. The product is positioned for analysts, marketing teams, revenue teams, operations groups, e-commerce managers, and enterprise BI teams that want predictive outputs without building a full custom data science stack. Its core workflow is to define a business prediction goal, connect prepared structured data, let the platform automate model preparation and training steps, review prediction drivers and model outputs, and then schedule delivery of predictions into operational destinations such as databases, data warehouses, CRM workflows, or reporting processes.

Several verifiable details make the product profile more concrete. Pecan's published pricing page lists a Starter plan at $760 per month on an annual plan, a Team plan at $1,400 per month on an annual plan, and a Business plan with custom pricing. The same pricing page lists 2 monthly prediction batches and 500M rows of storage for Starter, 10 monthly prediction batches and 2Bn rows of storage for Team, and 5Bn rows of storage for Business. It also lists extra prediction batches at $50 per prediction batch. For authentication, Starter and Team include Google Workspace and Microsoft SSO, while Business supports SAML, OIDC, and OAuth SSO providers. Pecan's security help documentation states that the company has ISO 27001 and SOC 2 Type II certifications, and it describes GDPR and CCPA compliance, customer data isolation, and the ability to work without PII for modeling. G2 lists Pecan with an overall rating of 4.7 out of 5 from 38 reviews, giving buyers a public third-party review reference rather than relying only on vendor claims.

Pecan is best understood as a focused predictive analytics layer, not as a general-purpose BI suite, notebook environment, or developer-first machine learning platform. That focus is useful when a team already has historical tabular data and a repeatable business question, such as which customers are likely to churn, which leads are more likely to convert, which transactions carry fraud or chargeback risk, which products may see demand shifts, or which customers are likely to generate higher lifetime value. The platform's Predictive AI Agent, scheduled prediction delivery, prediction monitoring, automated feature engineering, and business-user workflows are meant to compress the time between a question and an operational prediction.

The main adoption constraint is data readiness. Pecan can reduce the need for specialized data science work, but teams still need enough clean historical data, a meaningful target outcome, and a workflow that can act on predictions. Organizations with highly custom modeling requirements, unstructured-data-heavy AI needs, or deep MLOps customization requirements may find broader platforms such as DataRobot or H2O.ai more flexible. For teams whose priority is spreadsheet-connected reporting, Coefficient may be a more direct fit, while Hex is stronger for collaborative analytics notebooks and data apps. Pecan is strongest when a business team wants a guided, repeatable path from structured business data to deployed predictive scores.

Define a prediction goal, connect structured business data, select a predictive use case, validate the model, and schedule prediction delivery.