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The short version

Pick Pecan AI if you have a predictive use case (churn, LTV, conversion classification) at large data volumes, you want custom models, and your warehouse is well-organized.
Pick clariBI if you want forecasting and driver discovery on time-series metrics, daily dashboards, conversational analytics, and goal tracking in one product without training models.
Pick both if you have a data science team running Pecan for prediction work and an operating team using clariBI for daily decisions and forecasting.

The teams who pick clariBI over Pecan most often: SaaS teams who wanted predictive insight but did not have a data science function, and e-commerce teams who needed forecasting plus dashboards in one product.

Side by side

Capability clariBI Pecan AI
Entry price (monthly) $19 manual / $99 AI tier Sales-led; typical contracts $30,000+ annual
Primary use case BI + forecasting + driver discovery Custom predictive ML (churn, LTV, conversion)
Time-series forecasting Yes (9 methods, walk-forward backtest) Yes (custom ML models, not the focus)
Classification predictions (churn yes/no) Not the focus Yes (primary feature)
Model training required No (auto-selects from 9 methods) Yes (Pecan models trained per use case)
Driver discovery FDR-controlled correlation across all metrics Feature importance from trained model
Dashboards + reports Yes Predictions feed downstream tools
Conversational analytics Yes No
Anomaly + structural-change detection Yes, built in No
Goal tracking + OKRs Yes No
Data scientist required to operate No Often yes for model tuning
Free trial 14 days, no card Sales-led pilot

Pecan AI pricing summarized from their public site. Plans change; double-check before committing.

Forecasting head to head

Pecan and clariBI both call themselves "predictive," but the products solve different problems.

Pecan trains custom ML models on your warehouse data for predictive use cases: which customers are likely to churn next month, what is the expected LTV of a new customer, which leads are most likely to convert. The models are tailored to each use case and need to be trained, validated, and maintained.

clariBI's forecasting engine projects time-series metrics (MRR, churn rate, CAC, ad spend, traffic) up to 90 days ahead, scoring nine methods on walk-forward backtests and picking the winner. Correlation discovery surfaces which other metrics are moving with the target, with Benjamini-Hochberg false-discovery-rate control. Anomalies and structural breaks in the historical series get flagged automatically.

If the question is "which specific customers are likely to churn," Pecan's classifier is built for that. If the question is "where will our churn rate be in 30 days and what is driving it," clariBI's time-series forecasting is the right shape. Many teams want both, but not until they have a data science function.

When Pecan AI is the better pick

You have a predictive-modeling use case at scale. Per-customer churn prediction, per-lead conversion scoring, per-account LTV prediction. These are classification or regression problems on customer-level data. Pecan's product is purpose-built for this.

You have a data scientist on payroll. Pecan provides a no-code interface, but model training still requires someone who understands the predictions, can validate them, and decides when to retrain. Without a data role, the product underdelivers.

Your data is in a clean warehouse. Pecan reads from Snowflake, BigQuery, Redshift. If your customer data is already modeled and clean, Pecan's value lands quickly. If your data is in scattered SaaS sources, you would build the warehouse first.

Per-customer predictions are the deliverable. Pecan outputs scored lists: this customer at 0.87 churn probability, this lead at 0.23 conversion probability. The output feeds CRMs and marketing tools. clariBI's output is metric-level: "churn rate will be 3.2% next month."

When clariBI is the better pick

You want forecasting without training models. clariBI picks the forecasting method automatically via walk-forward backtest. No model training, no retraining cycle, no validation step. Bind a forecast to a metric and it runs on the cadence you set.

You also need dashboards, reports, and goals. Pecan is predictive ML, not BI. If you need the dashboard, report, conversational query, and goal-tracking surface around your predictions, you are buying two tools. clariBI bundles them.

You do not have a data scientist. Pecan markets itself as no-code, but real value still requires someone who can interpret predictions. clariBI's conversational analytics + forecasting are usable by founders and operators directly.

Self-serve pricing matters. Pecan is sales-led with annual contracts. clariBI Starter is $99 a month, billed monthly, cancel anytime. The procurement path is different.

Your metric questions are about trends, not individual customers. "Will MRR hit our target?" "What is driving the CAC spike?" "When did the trend break?" Pecan answers "which customers," not "which trends."

Pricing, side by side

Pecan does not publish prices. Reference deals reported in $30k to $200k+ annual range depending on data volume and model count. clariBI's $99 a month Starter has no contract, no annual commit.

clariBI

  • Free: 0 AI credits, 3 sources, 1 GB storage
  • Lite, $19/mo: manual dashboards, 5 sources, no AI
  • Starter, $99/mo: 500 AI credits, 10 sources, 3 seats, MCP integrations, forecasting
  • Professional, $199/mo: 1,500 AI credits, 50 sources, 15 seats, RBAC
  • Enterprise, $999/mo: 5,000 AI credits, 100 sources, 100 seats

Flat rate at each tier. Per-month billing. Forecasting included in Starter and up.

Pecan AI (public list pricing)

  • Public pricing: Not published
  • Typical annual contract: from $30,000+
  • Pilot: Sales-led; 4 to 12 weeks typical
  • Enterprise: Custom, multi-year contracts common

Verify current pricing at www.pecan.ai.

Moving from Pecan AI to clariBI

  1. 1
    List what Pecan currently predicts for you. Per-customer churn? LTV? Conversion? These are classification/regression problems and stay in Pecan. The list might be shorter than you expect.
  2. 2
    List the metric-level questions you have. "Where will MRR be next month?" "What is moving CAC?" These are time-series questions, which is what clariBI's forecasting is built for.
  3. 3
    Pick the right tool per question. Per-customer predictions go to Pecan. Metric-level forecasts go to clariBI. If your full need is the second list, you may not need Pecan.

FAQ

Is clariBI a Pecan replacement?

If you wanted predictive insight at the metric level (forecasts, drivers, anomalies) without a data science team, yes. If you specifically need per-customer churn or LTV scoring at scale, Pecan is the right shape and clariBI is not a replacement.

Can clariBI predict per-customer churn?

Not as a primary feature. clariBI forecasts metric-level series like overall churn rate, MRR, and CAC. Per-customer classification is a different shape of ML problem that we do not optimize for.

How much training does clariBI require?

None from the user. The forecasting engine runs a walk-forward backtest against nine methods on connect and selects the winner. The user does not see model training.

Can I use both?

Yes. Many teams with data science capacity run Pecan for specific predictive use cases and clariBI for daily BI and forecasting. The two solve different problems.

What about driver discovery?

Pecan exposes feature importance from each trained model. clariBI's driver discovery uses Pearson and Spearman correlation with Benjamini-Hochberg FDR control across every metric in your workspace. Different mechanisms, similar intent.

See it for yourself

14 days free, no card, no model training. Connect a source and run your first forecast with auto-selected methods plus driver discovery.

Start the free trial See pricing

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