How much do AI CPQ tools cost?

Most CPQ vendors do not publish list prices and quote by scope. Published or aggregated figures as of 2026 put Salesforce Revenue Cloud at 150 to 200 US dollars per user per month, Oracle CPQ from around 240 dollars per month, PROS Smart CPQ in the 60 to 95 dollar per user range, DealHub in a custom premium tier estimated at 500 to 1,000 dollars per month, and PandaDoc CPQ from free to around 49 dollars per seat. Enterprise CPQ also carries implementation and integration costs that often dwarf the licence.

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The Best AI Tools for CPQ and Quote-to-Cash in 2026: An Honest Buyer Comparison

19 October 202617 min read

Henri Jung

Co-founder at Superkind

Modular components configured into one assembled unit, representing Configure, Price, Quote

A quote should take minutes. In most B2B companies it takes days. A rep pulls the latest price list, checks whether a configuration is even valid, works out the discount, waits for an approval that sits in someone’s inbox, rebuilds the document after a change, and finally sends something the customer has half-forgotten they asked for. Salesforce found that building quotes is now the single largest non-customer-facing task in sales, eating 17 percent of a rep’s time - nearly double the share of two years earlier³.

That is why Configure, Price, Quote (CPQ) software exists, and why nearly every vendor has bolted AI onto it for 2026. But the market is loud, the demos all look the same, and most comparison pages are thin rankings written by people selling one of the tools. This one is written to help you choose, names concrete products, and is honest about where each one wins and where it does not - including ours.

This guide is for the revenue operations lead, sales director, or CFO who has to pick a quoting stack that fits the CRM and ERP they already run, without buying a platform their team quietly works around.

TL;DR

CPQ is one segment of quote-to-cash - the Configure, Price, Quote step inside the wider chain that runs from quote to approval, contract, order, invoice and cash.

No tool wins every row. Salesforce Revenue Cloud, Oracle CPQ and SAP CPQ suit their own ecosystems; PROS leads on AI pricing; DealHub on no-code and buyer collaboration; Conga on documents; Cincom on manufacturing configuration; PandaDoc on lightweight quoting.

The integration is the project, not the quoting screen. CPQ succeeds or fails on clean product and pricing data in your ERP and CRM.

AI inside CPQ speeds the quote but the recap, the CRM update, the approval chase and the exception handling still land on people.

The durable win is a Company Brain that keeps your pricing and approval logic, plus an AI employee that runs the routine quoting work across the systems you already use.

What CPQ and Quote-to-Cash Actually Cover

Before comparing tools, it helps to be precise about what they do. “CPQ” and “quote-to-cash” get used interchangeably, but they are not the same scope, and buying the wrong scope is how companies end up with three overlapping systems.

Configure - the buyer or rep assembles a valid product or bundle from options, with rules that stop impossible or non-sellable combinations.
Price - the system applies the right list price, volume breaks, regional pricing and discount logic, ideally with margin guardrails.
Quote - a branded, accurate document goes out, with the right terms, line items and totals, ready for signature.
Quote-to-cash (Q2C) - the wider chain that continues past the quote into approvals, contract, order, fulfilment, invoice and cash collection.
CLM and billing - contract lifecycle management and billing often sit at the back of Q2C and are where revenue leakage hides.

Where the money is

CPQ is a slice of a bigger pie. Analysts put the CPQ segment at roughly 44.5 percent of the quote-to-cash software market, which sat at about 2.85 billion US dollars in 2025². The CPQ market itself is estimated near 3.5 billion dollars in 2025, rising to almost 3.9 billion in 2026 and projected toward 10.8 billion by the mid-2030s at a mid-teens growth rate¹.

Stage	What it does	Who owns it	Where AI helps most
Configure	Build a valid product or bundle	Sales, sales engineering	Guided selling, valid-combo checks
Price	Apply list price, discounts, margin rules	Pricing, deal desk	Price recommendations, optimisation
Quote	Generate the document and terms	Sales, operations	Drafting, document assembly
Approve	Route exceptions and discounts for sign-off	Deal desk, finance, legal	Exception flagging, routing
Contract and bill	Turn the signed quote into revenue	Finance, legal, RevOps	Leakage detection, reconciliation

Keep this map in mind as you read the tools below. Some are configurators first, some are document engines, some are full Q2C suites, and the AI they add lands in different stages.

Why AI Changed the CPQ Decision in 2026

CPQ is not new. What changed is that AI moved from a demo feature to a real reason to re-evaluate your stack. A few shifts made 2026 the year buyers reopened the question.

Guided selling went mainstream - Gartner predicted that by 2025, 75 percent of B2B sales organisations would augment traditional sales playbooks with AI-guided selling solutions⁷. That prediction is now the baseline expectation, not the edge case.
Agents entered the quote - Oracle introduced an AI Quote Assistance Agent in its 2025 CPQ release that answers questions in natural language and helps reps understand pricing concepts¹¹, and PROS moved AI agents into pilot for its platform¹³. The quote screen itself is becoming conversational.
Pricing science matured - machine-learning price recommendation, once a separate product, is now embedded. PROS pairs CPQ with an optimisation engine that suggests prices from historical win and margin data¹³.
Revenue leakage got attention - MGI Research reports that 42 percent of companies experience revenue leakage, costing them between 3 and 7 percent of top-line revenue each year⁸. Tighter, AI-governed discounting is a direct answer.
The buying experience became the differentiator - tools like DealHub moved the quote into a shared, buyer-facing space rather than a PDF attachment¹⁴.
Selling time stayed scarce - with quote creation alone consuming 17 percent of a rep’s week³, any AI that gives selling time back has a clear business case.

“Sellers can no longer exclusively rely on intuition-based selling to push a deal over the finishing line.”

- Steve Rietberg, Senior Director Analyst in the Gartner Sales Practice⁵

The honest caveat: AI does not fix a broken process. If your pricing lives in a spreadsheet nobody trusts and your approvals happen on Slack, adding an AI quote assistant just automates the mess faster.

How to Read This Comparison

Rather than rank tools on a single score, judge each against the job you actually need done and the stack you already run. Five questions sort the field faster than any star rating.

What is your core stack? - If you are deep in Salesforce, Oracle or SAP, the native CPQ keeps the quote close to your CRM and ERP and shortens integration.
How complex is your configuration? - A few SKUs with simple options need a quote builder; highly configurable machinery needs a rules engine and visual configuration.
Where is your pain? - Slow documents, weak discount governance, pricing left on the table, or a clunky buyer experience each point to a different tool.
Who maintains it? - No-code platforms let RevOps own the rules; heavyweight suites usually need admins or a partner.
What happens after the quote? - If approvals, CRM updates and follow-ups are the real bottleneck, a configurator alone will not fix it.

A note on pricing figures

Most CPQ vendors do not publish list prices and quote by scope. The numbers in this article come from vendor pricing pages where they exist and reputable aggregators otherwise, and they are directional. Always get a written quote for your seat count, editions and implementation scope.

The Best AI CPQ and Quote-to-Cash Tools in 2026

These are real, established tools, grouped loosely by who they fit best. Each entry is honest about strengths and limits. None of them wins every row, and the right answer for you depends on the five questions above.

1. Salesforce Revenue Cloud

The native path for companies already running Salesforce CRM. Revenue Cloud (Salesforce’s Revenue Lifecycle Management product) is the go-forward platform that new customers are steered toward, with Agentforce available as a paid AI add-on for revenue tasks.

Best for - Salesforce-centric revenue teams that want quote, contract and billing in one data model.
AI focus - data-driven suggestions and automation of routine quoting tasks, with Agentforce Revenue Management as a separate add-on.
Pricing - Growth edition around 150 US dollars per user per month, Advanced around 200, billed annually; Agentforce priced separately¹².
Honest limit - powerful but heavy and costly, and teams on legacy Salesforce CPQ face a migration to Revenue Cloud they should plan for.

2. Oracle CPQ

A named Leader in the 2026 Gartner Magic Quadrant for CPQ Applications - its ninth consecutive time in the Leaders quadrant¹⁰ - and a strong fit when the quote must stay tied to Oracle’s back office.

Best for - large enterprises with complex configuration and an Oracle ERP estate.
AI focus - the CPQ 25C release added an AI-powered Quote Assistance Agent that answers questions in natural language and explains concepts like price waterfall and deal optimisation¹¹.
Pricing - not publicly listed in full; aggregators cite entry pricing from around 240 dollars per month²¹.
Honest limit - deep and capable but weighty to implement, and best value is realised inside the Oracle estate.

3. SAP CPQ

The quoting layer for SAP-centric organisations. SAP CPQ replaced the former CallidusCloud CPQ and is built to run independently of any single CRM¹⁹.

Best for - manufacturers and enterprises that run SAP and want quotes aligned to ERP pricing and production.
AI focus - configuration and pricing automation within the SAP ecosystem.
Pricing - quote-based; expect enterprise licensing plus implementation.
Honest limit - strongest in SAP shops; confirm the product roadmap and your migration path to SAP’s newer sales and S/4HANA direction before committing.

4. PROS Smart CPQ

The pick when dynamic, data-driven pricing is the real prize. PROS pairs CPQ with a pricing-science engine that uses machine learning to recommend prices¹³.

Best for - high-volume B2B sellers who leave margin on the table with static price lists.
AI focus - AI price recommendations, cross-sell and churn signals; AI agents moved into pilot in 2025¹³.
Pricing - not published; aggregators cite a range from roughly 60 to 95 dollars per user per month plus setup¹³.
Honest limit - pricing-led strength can be overkill if your catalogue is simple and your discounting is already disciplined.

5. DealHub

A no-code, mid-market favourite that moves the quote into a shared buyer space. DealHub’s DealRoom lets sellers and buyers collaborate and see approvals in one place¹⁴.

Best for - revenue teams that want fast setup, a strong buyer experience, and no-code rule maintenance.
AI focus - guided selling, automated approvals and AI redlining on contracts¹⁴.
Pricing - custom-quoted; estimated in a premium 500 to 1,000 dollar per month range, with a strong 4.7 average rating on review sites¹⁵.
Honest limit - premium-priced for its segment, and reviewers note that very complex pricing rules can require nested logic.

Not sure a new configurator is your real problem?

Book a 30-minute call. We will look at where quoting time actually goes in your team.

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6. Conga CPQ

Strong where the quote is also a document problem. Conga pairs configuration and pricing with deep document generation through Conga Composer and document automation¹⁶.

Best for - organisations that need branded, complex proposals and contract lifecycle management alongside quoting.
AI focus - document assembly, a single price book across channels, and an API-first approach¹⁶.
Pricing - custom-quoted for enterprise.
Honest limit - the breadth adds complexity, and the Salesforce-adjacent heritage shows in setup.

7. Cincom CPQ

A manufacturing-focused configurator with cloud and on-premise deployment, 3D visual configuration, and cross-sell and up-sell recommendations¹⁷.

Best for - makers of highly configurable machinery and equipment where buyers need to see what they are configuring.
AI focus - guided configuration and recommendation; integrations stated for SAP and Microsoft Dynamics 365¹⁷.
Pricing - quote-based.
Honest limit - a specialist fit; less relevant outside complex product manufacturing.

8. PandaDoc CPQ

The lightweight, document-centric option for smaller teams. PandaDoc adds a quote builder, product catalogue and bundling to its proposals and e-signature core, with a Salesforce integration added in 2025¹⁸.

Best for - SMBs and teams whose quoting pain is really proposals, pricing tables and getting a signature.
AI focus - quote assembly and document workflow rather than deep configuration.
Pricing - CPQ is an add-on on the Business plan and included on Enterprise; aggregated pricing runs from free to around 49 dollars per seat per month¹⁸.
Honest limit - not a deep enterprise configurator; advanced rules and layout control lag dedicated CPQ suites.

9. Superkind

Not a CPQ platform, and we will not pretend otherwise. Superkind is an AI employee that sits on top of the CPQ, CRM and ERP you already run and takes over the routine quoting and approval work around the tool - the part no configurator does for you.

Best for - teams whose configurator is fine but whose people still burn hours gathering inputs, chasing approvals and updating records.
AI focus - an AI employee that drafts quotes, routes approvals, updates the CRM and keeps the reasoning behind your pricing decisions.
Pricing - per use case and outcome, not per seat; no large upfront licence.
Honest limit - if you have no quoting engine at all and need core configuration, you need a CPQ tool first; we complement it, not replace it.

Tool	Best for	AI focus	Directional pricing
Salesforce Revenue Cloud	Salesforce estates	Automation, Agentforce add-on	$150-200/user/mo¹²
Oracle CPQ	Oracle ERP enterprises	Quote Assistance Agent	From ~$240/mo²¹
SAP CPQ	SAP shops	ERP-aligned config/pricing	Quote-based¹⁹
PROS Smart CPQ	Dynamic pricing	ML price optimisation	~$60-95/user/mo¹³
DealHub	Mid-market, buyer experience	No-code, AI redlining	~$500-1,000/mo¹⁵
Conga CPQ	Document-heavy quoting	Document automation	Custom¹⁶
Cincom CPQ	Complex manufacturing	3D visual configuration	Quote-based¹⁷
PandaDoc CPQ	SMB, proposals and e-sign	Document workflow	Free-$49/seat¹⁸
Superkind	Routine work around the quote	AI employee on your stack	Per outcome

Five linked stages in a row representing the quote-to-cash pipeline

What Every CPQ Comparison Skips

Every tool above does the quote well. The gap that no comparison sheet shows is this: the quote is a small part of the work, and the rest still lands on people. A faster configurator does not route the approval, write the follow-up, update the forecast, or remember why you price a given account the way you do.

The inputs are manual - someone still gathers requirements, pulls the account history and checks stock before the quote can even start.
The approval still drags - CPQ flags the exception, but a human has to chase the deal desk, finance or legal for the sign-off.
The CRM goes stale - the quote lives in one system and the opportunity in another, and the reconciliation falls to the rep.
The follow-up is ad hoc - the recap email, the next step and the nudge depend on whoever is busy that week.
The knowledge is in heads - why this customer gets this discount, which configurations cause trouble, how a tricky deal was structured last time - that lives with your deal-desk specialist, not in the tool.

The bus-factor problem in quoting

When your most experienced pricing or deal-desk person leaves, the configurator stays but their judgement walks out the door. The new hire can run the tool and still price badly, because the reasoning was never written down. This is the knowledge that a Company Brain is built to keep.

“A sales organization with inaccurate or missing sales data will probably not see the same value in this technology as one that ensures data quality prior to launch.”

- Tad Travis, VP Analyst at Gartner⁶

This is why the integration and the knowledge, not the quoting screen, decide whether a CPQ project pays off. Which is exactly the layer Superkind works on.

How Superkind Fits

Superkind builds custom AI employees for mid-market and enterprise companies. For quoting, that means an AI employee that connects to your existing CPQ, CRM and ERP and runs the routine work around the quote, while keeping the knowledge that makes your pricing good. It is one honest option alongside the tools above, and it is the right one only when your bottleneck is the work around the quote, not the configurator itself.

Sits on top of your stack - the AI employee connects to Salesforce, Oracle, SAP, DealHub or whatever you run today. No rip-and-replace, nothing new for the team to learn.
Gathers the inputs - it pulls account history, stock and prior deals so a quote starts with the context already in place.
Drafts the quote - it assembles the configuration and quote in your existing CPQ, ready for a human to review rather than build from scratch.
Chases the approval - it routes exceptions to the right approver, follows up, and keeps the deal moving instead of waiting in an inbox.
Updates the record - it keeps the CRM opportunity and the quote in sync so the forecast is real.
Keeps the knowledge - a Company Brain holds the reasoning behind your pricing and approval decisions, so it survives when a specialist leaves.
Human in the loop - it flags low-confidence or high-value calls for a person rather than acting blindly, with an audit trail on every action.
Enterprise-grade security - company data stays inside your infrastructure, connections are encrypted, and controls are built for GDPR.

Approach	A CPQ platform	Superkind AI employee
Primary job	Configure, price and generate the quote	Run the routine work around the quote
Relationship to your stack	A new system to adopt and maintain	A layer on the systems you already run
Approvals and follow-up	Flags exceptions; a human chases	Routes, chases and follows up
Knowledge	Rules you configure and maintain	Keeps the reasoning in a Company Brain
Pricing model	Per seat plus implementation	Per use case and outcome

Pros

✓ Works with your CPQ - complements the configurator, no migration
✓ Does the work around the quote - inputs, approvals, follow-up, CRM
✓ Keeps the knowledge - pricing and approval logic survives staff turnover
✓ Outcome-based pricing - pay for results, not seats
✓ Data stays in your infrastructure - encrypted, GDPR-aligned

Cons

✗ Not a configurator - you still need a CPQ engine for core config
✗ Not self-serve - it is built with our team, not downloaded
✗ Needs process access - we map your real quoting workflow first
✗ Overkill for tiny teams - a simple quote builder may be enough

How to Choose: A Decision Framework

Match the signal in your business to the kind of tool that answers it. The table below is a starting point, not a verdict - most companies combine a core CPQ with a layer that handles the routine work.

Your situation	What it points to
All-in on Salesforce, Oracle or SAP	Start with the native CPQ to keep the quote close to CRM and ERP
Leaving margin on the table	PROS Smart CPQ for AI price optimisation
Clunky buyer experience, slow setup	DealHub for no-code and DealRoom collaboration
Complex, configurable machinery	Cincom or SAP CPQ for visual, rules-heavy configuration
Proposals and signatures are the pain	PandaDoc or Conga for document-led quoting
Configurator is fine, people are the bottleneck	An AI employee layer like Superkind on your existing stack

Single suite

✓ One data model - quote, contract and billing in one place
✓ Fewer integrations - less to break between systems
✓ One vendor - simpler support and roadmap
✗ Costly and rigid - you adapt to the suite
✗ Lock-in - hard to swap one piece later

Best-of-breed

✓ Best tool per job - specialist strength where it matters
✓ Fits your stack - works with non-standard setups
✓ Flexible - swap one piece without ripping out the rest
✗ Integration work - more connections to build and maintain
✗ More vendors - more relationships to manage

A Buyer’s Rollout Checklist

Whatever you choose, the project succeeds or fails on preparation, not on the demo. Run these steps before you sign and the tool has a real chance of sticking.

Baseline the metrics - measure quote turnaround, error and rework rate, average discount, approval cycle time and the share of rep time spent quoting. You cannot prove ROI against numbers you never took.
Audit your data - clean the product catalogue, price lists and discount rules first. Gartner’s warning holds: messy data in means weak value out⁶.
Map the approvals - write down who signs off on what, and at which thresholds. If the rules live only in people’s heads, no tool can enforce them.
Pick one deal type to start - model one product line or one quote type end to end before you boil the ocean.
Test the integration early - prove the connection to your CRM and ERP in a sandbox before you commit, because this is where projects stall.
Decide what AI is allowed to do - set the human-in-the-loop checkpoints for pricing and approvals, and the audit trail, before go-live.
Train the team on the new flow - the configurator changes the rep’s day; walk them through it with real deals, not slides.
Review after 90 days - compare the live metrics to your baseline and decide what to expand next.

CPQ Buyer Readiness Checklist

You can name your average quote turnaround time today
Your product catalogue and price lists are clean and current
Your discount and approval thresholds are written down
You know which CRM and ERP the tool must connect to
You have identified the single biggest quoting bottleneck
You have an owner for the rules after go-live
You have decided what AI may do without a human check
You have a 90-day success metric agreed with leadership

Frequently Asked Questions

There is no single best AI CPQ tool, only the best fit for your stack and your deal complexity. For Salesforce shops, Salesforce Revenue Cloud is the native path. For Oracle and SAP estates, Oracle CPQ and SAP CPQ keep the quote close to the ERP. PROS Smart CPQ leads on AI pricing science, DealHub on no-code setup and buyer collaboration, Conga on document generation, Cincom on complex manufacturing configuration, and PandaDoc on lightweight quoting for smaller teams. Superkind is the option when the configurator is fine but the routine quoting and approval work still falls on people.

CPQ stands for Configure, Price, Quote. It is the step where a seller assembles a valid product configuration, applies the right price and discount, and produces a quote. Quote-to-cash (Q2C) is the wider chain that starts at the quote and runs through approvals, contracts, order, invoice and cash collection. CPQ is one segment inside quote-to-cash, and industry analysts put it at roughly 44 percent of the quote-to-cash software market.

Most CPQ vendors do not publish list prices and quote by scope. Published or aggregated figures as of 2026 put Salesforce Revenue Cloud at 150 to 200 US dollars per user per month, Oracle CPQ from around 240 dollars per month, PROS Smart CPQ in the 60 to 95 dollar per user range per aggregators, DealHub in a custom premium tier estimated at 500 to 1,000 dollars per month, and PandaDoc CPQ from free to around 49 dollars per seat. Enterprise CPQ also carries implementation, integration and admin costs that often dwarf the licence.

No. AI inside CPQ speeds up configuration, suggests prices, flags exceptions and drafts the quote, but a person still owns the relationship, the negotiation and the judgement calls. The realistic gain is giving reps more selling time back. Salesforce research found quote creation alone consumes 17 percent of sales time, so automating the mechanical part of quoting is where AI earns its keep.

Salesforce has steered new customers toward Revenue Cloud (its Revenue Lifecycle Management product) as the go-forward platform, while existing Salesforce CPQ customers continue to be supported. If you are evaluating a new deployment on Salesforce, plan around Revenue Cloud and factor in the migration path rather than starting on the legacy product.

Yes, and it has to. A quote is only as good as the product, price and inventory data behind it, which lives in your ERP and CRM. Oracle CPQ and SAP CPQ sit close to their own ERPs, Salesforce Revenue Cloud is native to Salesforce CRM, and tools like PROS, DealHub and Conga offer connectors to Salesforce and Microsoft Dynamics. The integration is where most CPQ projects succeed or fail, not the quoting screen.

A lightweight, document-centric tool like PandaDoc can be live in days to weeks. A mid-market no-code tool like DealHub often lands in weeks. A full enterprise CPQ on Oracle, SAP or Salesforce with complex product rules, approval matrices and ERP integration is usually a multi-month project, sometimes six months or more, and the modelling of your pricing and configuration rules is the long pole.

The quoting engine is rarely the problem. Projects stall on messy product and pricing data, approval rules that live in people’s heads rather than in the system, and integration gaps between CPQ, CRM and ERP. Gartner has long warned that a sales organisation with inaccurate or missing data will not see the same value from AI-driven selling as one that fixes its data first.

Probably not a heavyweight one. If your catalogue is small and your pricing is simple, a quote builder inside PandaDoc or a CRM-native quoting feature may be enough. CPQ earns its cost when configurations are complex, discounting needs governance, or approval chains and margin control are slowing deals down. Match the tool to your real pricing complexity, not to a competitor’s stack.

A CPQ tool produces the quote; it does not do the work around the quote. An AI employee on top of your CPQ, CRM and ERP can gather the inputs, draft the configuration and quote, chase the approval, update the CRM record, send the follow-up and flag the exceptions for a human. It also keeps the reasoning behind your pricing and approval decisions so that knowledge does not leave when a deal-desk specialist does.

It depends on the deployment. Enterprise CPQ vendors run in cloud environments with standard security certifications, and connections to CRM and ERP are encrypted. For sensitive pricing logic, ask where data is processed, whether it is used to train shared models, and how access is controlled. Superkind keeps company data inside your own infrastructure with encrypted connections and GDPR-aligned controls.

Manufacturers with highly configurable products often favour Cincom CPQ for its 3D visual configuration and deep rules, or Oracle CPQ and SAP CPQ when the quote must stay tied to the ERP and production planning. PROS Smart CPQ is strong where dynamic, data-driven pricing matters. The right pick depends on whether your pain is configuration complexity, pricing optimisation or ERP integration.

Baseline the metrics before you start: average quote turnaround time, quote error and rework rate, average discount given, approval cycle time, and the share of a rep’s week spent quoting. Track the same numbers after go-live. Revenue leakage is the other lever; industry estimates put leakage at 3 to 7 percent of top-line revenue for the companies that suffer it, so tighter discount governance alone can justify the project.

A single suite from Salesforce, Oracle or SAP keeps quote, contract and billing in one place and simplifies the data model, at the cost of flexibility and price. Best-of-breed pairs a specialist CPQ with your existing CRM and document tools, which fits non-standard stacks but adds integration work. Many companies land in between: a core CPQ plus an AI layer that connects the pieces and does the routine work across them.

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