Predictika · Databricks platform quotes

An AI sales agent's Databricks quote, checked before it goes out

An AI sales agent prepares a Databricks quote: cloud, tier, workloads and DBU usage, commit plan, agreement term, support and any compliance requirement, in US dollars. Before it goes out, the agent sends it to Predictika, which checks it against a declarative model of the pricing and quoting rules, compares the annual total the quote states with the computed one, and returns every finding together with the line-item quote. This is an illustrative model built from public product information with illustrative prices; it is not Databricks's internal rule set.

Scope: Databricks platform quotes (cloud, tier, workloads and DBU usage, DBCU commit plans, term, support and compliance add-on). See everything this validator covers

This validator models one slice of Databricks pricing: platform quotes for the Jobs Compute, All-Purpose Compute and SQL Serverless workloads, with the cloud, tier, DBCU commit plan, agreement term, support plan and compliance requirement around them. It handles any quote built from those parts, not only the scenarios shown on this page. It does not cover Databricks features outside platform and DBU pricing, or other vendors.

  • Covered: cloud, tier and compliance — AWS, Azure and Google Cloud availability by tier (Standard retired for new workspaces, Enterprise sold on AWS only), and compliance requirements such as HIPAA (Enterprise on AWS, the Enhanced Security and Compliance add-on on Azure).
  • Covered: workloads — DBU usage per workload line, capped at the hours in a month, with Photon and SQL warehouse checks and a flag for All-Purpose clusters that run like production jobs.
  • Covered: commit plans — 1-year and 3-year DBCU plans sized against planned usage (too small, too large, costing more than pay as you go) and matched to the agreement term.
  • Covered: commercial terms — the line-item quote, support plans, deal discounts that need a commitment and stay within the spend-based limit, the empty-quote check, and the annual total the agent states.
  • Not covered — Databricks features outside platform and DBU pricing, cloud provider charges for virtual machines, and real Databricks contract terms. Other areas can be added.
  • Labeled as assumptions — DBU rates, plan prices, support percentages and discount limits are illustrative values built from public product information, not a Databricks price list.
The AI sales agentBuilds the Databricks quote from the conversation and says it is ready.
Predictika validatorChecks every rule at once and returns the verdict, each finding, and the computed figures.

8 scenarios in 4 groups · each group runs from the agent’s first attempt to the outcome · click one to switch

What the agent proposes

The AI sales agent says

Illustrative agent statement. The validator receives the structured request below.

Proposing agent: AI sales agent (scripted)

Before sending, the agent must call Recorded run

Predictika validator


      

        
        
        
      

      
Waiting for the call.

      
    

  

  
  

    
Where rules-based CPQ gets it wrong
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Our assessment of what a typical rules-based CPQ (product rules, price rules and validation rules, as in Salesforce CPQ) does with the same cases. It is a judgment about effort and reliability, not a claim that nothing can be built.

    
What the validator caughtTypical rules-based CPQPredictika
Annual total built from workloads, DBU rates, commitment, discount and supportOnly with care
The total chains the workload DBU rates by cloud and tier, a commitment that discounts only the usage it covers, a deal discount and a support percentage with a minimum. A rule set can price each step, but every step has to be kept in step with the others.		The whole chain is computed on every call and returned as line items that add up to the annual total and the whole-agreement total.
Commit plan sized against usage, with the term matched to the agreementOnly with care
Undersized and oversized plans are two thresholds against the same usage figure, and the plan term has to match the agreement term. Each works alone; together they are easy to leave out of step.		The usage, the plan and the agreement term are compared on every call: a term mismatch blocks, and a plan that is too small or too large is flagged with the amounts.
Discount allowed only with a commitment and only up to a spend-based limitOnly with care
The permitted discount depends on the annual platform spend after commitment and on whether there is a commitment at all.		The permitted discount is computed from the platform spend on every call and compared with the one requested.
Compliance requirement that depends on cloud and tierOnly with care
HIPAA needs the Enterprise tier on AWS, but on Azure the answer is an add-on that changes the price. The same requirement leads to a block on one cloud and a price on another.		The requirement is checked against the cloud and tier, and the add-on is priced into the quote where it applies.
Annual total that the agent states must match the computed oneOnly with workarounds
A validation rule can compare a stated figure with a calculated one only if the stated figure is a field on the quote, which it rarely is.		The stated annual total travels with the quote and is compared with the computed total on every call; a mismatch is its own block.

  

  

    
What this validator catches: findings from its test cases
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Every rule is checked on every call, in any order the choices arrive; one problem never hides another. Blocking findings stop the quote; approval findings need a person to sign off; everything else is computed.

    
1The Standard tier is retired for new workspaces, so a quote on it is blocked.
2The Enterprise tier is sold on AWS only; a quote for it on Azure or Google Cloud is blocked.
3A compliance requirement such as HIPAA needs the Enterprise tier on AWS; on the Premium tier the quote is blocked.
4On Azure a compliance requirement is met by the Enhanced Security and Compliance add-on, which is priced into the quote as a warning.
5A workload cannot be planned for more hours in a month than the month has (744).
6A quote with no workload lines is blocked.
7The commit plan must run for the same number of years as the agreement; a mismatch is blocked.
8Usage above what the commit plan covers is billed at list, which is flagged as a warning with the amount outside the plan.
9A commit plan larger than the planned usage, or costing more than pay as you go, is flagged because committed units would expire unused.
10A deal discount needs a DBCU commitment, and it is limited by the annual platform spend; above the limit it is blocked.
11Large pay-as-you-go spend with no commitment is flagged, because a plan of about that size would discount it.
12Photon on a serverless SQL warehouse is flagged, because it is built in and adds nothing.
13An All-Purpose cluster running as many hours as scheduled production is flagged against Jobs Compute, which is cheaper.
14The annual total the agent states must match the computed total to within one dollar; otherwise the quote is blocked.

  

  

    
Scenarios and results
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The recorded result of the validator for each scenario on this page: the request the AI agent sent, the verdict, and every finding, in the order they appear above.

    
1. Discount authority: Five percent discount below the spend threshold

    
The agent's request. Quote for Fernbrook Analytics: workloads Jobs Compute at 100 DBU per hour for 600 hours a month; All-Purpose Compute at 20 DBU per hour for 200 hours a month; SQL Serverless at 40 DBU per hour for 300 hours a month on AWS, Premium tier, 1 year agreement, 1-year 200K DBCU plan, Enhanced support. A 5 percent deal discount is requested. The quote states an annual total of $212,587.20. It is ready to send.

    
Verdict: Blocked.

    
    
      
  • Discount above what is allowed. The requested 5 percent deal discount exceeds the 0 percent allowed for $207,200.00 of annual platform spend.
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2. Discount authority: Discount removed

    
The agent's request. Quote for Fernbrook Analytics: workloads Jobs Compute at 100 DBU per hour for 600 hours a month; All-Purpose Compute at 20 DBU per hour for 200 hours a month; SQL Serverless at 40 DBU per hour for 300 hours a month on AWS, Premium tier, 1 year agreement, 1-year 200K DBCU plan, Enhanced support. No deal discount. The quote states an annual total of $223,776.00. It is ready to send.

    
Verdict: Allowed.

    
    
      
  • Quote summary. Annual total $223,776.00: workloads $235,200.00; commitment less $28,000.00; support $16,576.00.
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3. Compliance tier: HIPAA on the Premium tier

    
The agent's request. Quote for Ashgrove Health: workloads Jobs Compute at 20 DBU per hour for 300 hours a month on AWS, Premium tier, 1 year agreement, Pay as you go plan, Business support. The workloads need HIPAA compliance. No deal discount. The quote states an annual total of $10,800.00. It is ready to send.

    
Verdict: Blocked.

    
    
      
  • Compliance needs the Enterprise tier. HIPAA workloads on AWS need the compliance security profile, which comes with the Enterprise tier. Quote Enterprise.
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4. Compliance tier: Enterprise tier quoted

    
The agent's request. Quote for Ashgrove Health: workloads Jobs Compute at 20 DBU per hour for 300 hours a month on AWS, Enterprise tier, 1 year agreement, Pay as you go plan, Business support. The workloads need HIPAA compliance. No deal discount. The quote states an annual total of $14,400.00. It is ready to send.

    
Verdict: Allowed.

    
    
      
  • Quote summary. Annual total $14,400.00: workloads $14,400.00.
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5. Commit term: Three-year plan on a one-year agreement

    
The agent's request. Quote for Marlowe Logistics: workloads Jobs Compute at 100 DBU per hour for 600 hours a month; All-Purpose Compute at 20 DBU per hour for 200 hours a month; SQL Serverless at 40 DBU per hour for 300 hours a month on AWS, Premium tier, 1 year agreement, 3-year 300K DBCU plan, Enhanced support. No deal discount. The quote states an annual total of $234,576.00. It is ready to send.

    
Verdict: Blocked.

    
    
      
  • Commit plan does not match the term. The 3-year 300K DBCU plan runs 3 years but the agreement is 1 year. The commit plan must match the agreement.
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  • Usage above the commitment. $135,200.00 a year of usage falls outside the 3-year 300K DBCU plan and is billed at list. A larger plan would discount it.
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6. Commit term: Three-year agreement

    
The agent's request. Quote for Marlowe Logistics: workloads Jobs Compute at 100 DBU per hour for 600 hours a month; All-Purpose Compute at 20 DBU per hour for 200 hours a month; SQL Serverless at 40 DBU per hour for 300 hours a month on AWS, Premium tier, 3 years agreement, 3-year 300K DBCU plan, Enhanced support. No deal discount. The quote states an annual total of $234,576.00. It is ready to send.

    
Verdict: Allowed.

    
    
      
  • Usage above the commitment. $135,200.00 a year of usage falls outside the 3-year 300K DBCU plan and is billed at list. A larger plan would discount it.
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  • Quote summary. Annual total $234,576.00: workloads $235,200.00; commitment less $18,000.00; support $17,376.00.
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7. Stated total: Stated total is off

    
The agent's request. Quote for Tidewater Energy: workloads Jobs Compute at 300 DBU per hour for 730 hours a month on AWS, Enterprise tier, 3 years agreement, 3-year 1.5M DBCU plan, Mission Critical support. A 6 percent deal discount is requested. The quote states an annual total of $485,587.20. It is ready to send.

    
Verdict: Blocked.

    
    
      
  • Stated total does not match. Stated annual total does not match: the agent stated $485,587.20 a year; the validator computes $462,464.00 a year.
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8. Stated total: Correct total stated

    
The agent's request. Quote for Tidewater Energy: workloads Jobs Compute at 300 DBU per hour for 730 hours a month on AWS, Enterprise tier, 3 years agreement, 3-year 1.5M DBCU plan, Mission Critical support. A 6 percent deal discount is requested. The quote states an annual total of $462,464.00. It is ready to send.

    
Verdict: Allowed.

    
    
      
  • Quote summary. Annual total $462,464.00: workloads $525,600.00; commitment less $140,000.00; discount less $23,136.00; support $100,000.00.
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Narration transcript
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The text of the narrated walkthrough, step by step.

    
    
      
  1. Welcome to a demo of Predictika's Validator. An AI sales agent prepares a Databricks quote: cloud, tier, workloads and DBU usage, and more. Before it goes out, the agent sends it to Predictika, which checks it against a declarative model of the pricing and quoting rules, compares the annual total the quote states with the computed one, and returns every finding together with the line-item quote. This is an illustrative model built from public product information with illustrative prices; it is not Databricks's internal rule set.
    2. 
      
  2. I will start with the first request by the agent.
    3. 
      
  3. The agent says. Quote for Fernbrook Analytics: workloads Jobs Compute at 100 DBU per hour for 600 hours a month; All-Purpose Compute at 20 DBU per hour for 200 hours a month; SQL Serverless at 40 DBU per hour for 300 hours a month on AWS. A 5 percent deal discount is requested. The rest of the request is on screen. The quote states an annual total of $212,587.20.
    4. 
      
  4. Before it goes out, the agent calls the validator.
    5. 
      
  5. Blocked, with one finding, all in one answer. The requested 5 percent deal discount exceeds the 0 percent allowed for $207,200.00 of annual platform spend.
    6. 
      
  6. The validator returned one blocking finding in one answer. A check that reads one field at a time would pass most of this; it shows only when the choices in the quote are checked against each other.
    7. 
      
  7. What this shows: for discount allowed only with a commitment and only up to a spend-based limit, a rules-based CPQ is error-prone for this.
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  8. Now the agent resubmits.
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  9. The corrected request. It changes the stated annual total to $223,776. It removes the deal discount.
    10. 
      
  10. It calls the validator again.
    11. 
      
  11. This time the validator clears it.
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  12. The validator also returns the line-item quote. It starts with Jobs Compute and SQL Serverless; the other three lines are on screen.
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  13. The earlier finding is cleared, and the validator's total, $223,776.00, matches the quote.
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  14. One problem caught in one call, fixed in one pass, and a clean $223,776 quote. That is the value of Predictika's validator: catching mistakes before they reach the customer. Now you can try the other scenarios yourself.
    15.