Iris Pricing Solutions vs Zilliant: Mid-Market Pricing Compared [2026 Guide]

Subtitle: An independent analysis for PE operating teams choosing between a pricing consultancy and an AI pricing platform Last updated: Q1 2026 (this comparison is refreshed quarterly) Category: Pricing Strategy & Optimization Tags: pricing-optimization, iris-pricing, zilliant, private-equity, mid-market, AI-pricing, B2B-pricing

1. The Portfolio Company That Knew Its Pricing Was Broken but Did Not Know Where

The operating partner had been in the role for six months. The portfolio company — a $90M B2B services and parts distribution business — was hitting revenue targets but missing EBITDA by 200 basis points. Cost reduction had already been executed in the first hundred days. Revenue growth was on track. The margin gap was hiding somewhere in the commercial engine.

A month of digging revealed the source: pricing. Not a single catastrophic pricing decision, but a thousand small ones compounding daily. New customer quotes priced at whatever the last similar deal had been — regardless of whether that deal was profitable. Renewal pricing defaulted to CPI escalation regardless of the customer's willingness to pay. Discount approvals routed through a regional VP who approved everything because rejecting deals slowed bookings and nobody tracked discount rate trends. The rate card existed but functioned as a ceiling, not a floor. Realized price on the same product varied by 35% across the customer base with no segmentation logic.

The operating partner now faces a question that PE teams encounter in mid-market portfolio companies with surprising frequency: Do we need a pricing strategy, or do we need pricing technology? The honest answer is usually both — but the entry point matters. Two providers that represent opposite ends of this spectrum are Iris Pricing Solutions (a mid-market pricing consultancy) and Zilliant (an AI-powered pricing and sales optimization platform). Choosing between them — or deciding to deploy both sequentially — depends on the portfolio company's pricing maturity, data readiness, and the operating team's appetite for technology investment.

2. TL;DR Comparison Table

3. Why This Comparison Matters

Mid-market PE portfolio companies occupy an awkward middle ground in the pricing provider landscape. They are too complex for the "raise prices 10% and hope" approach. They are too small for a $1M Simon-Kucher engagement. They often lack the data infrastructure for an enterprise AI platform. And they are under time pressure — the hold period clock started the day the deal closed, and the operating partner needs margin improvement that can be measured and attributed within the first year.

This is the landscape where Iris Pricing Solutions and Zilliant compete — not directly, because they offer fundamentally different types of capability, but in the operating partner's allocation of pricing investment dollars. The question is: given a finite budget and a 3–5 year hold period, do you invest first in pricing strategy and governance (Iris), or in pricing technology (Zilliant), or both in sequence?

Iris represents the "strategy-first" approach: understand the pricing problem, design the architecture, build governance, enable the team, and then (if the complexity warrants it) implement technology to sustain the improvement. Zilliant represents the "technology-first" approach: deploy an AI platform that identifies pricing opportunities from transaction data and delivers optimized pricing guidance at scale, with the strategy embedded in the platform's configuration.

Both approaches work. Neither is universally superior. The right choice depends on three factors: the portfolio company's pricing maturity, the quality and completeness of its transaction data, and the complexity of its pricing environment.

4. Company Profiles

4a. Iris Pricing Solutions

Positioning & Approach

Iris Pricing Solutions positions itself as a B2B pricing consultancy focused on mid-market companies — the segment where pricing expertise is most scarce and the impact per dollar invested is typically highest. The firm explicitly bridges the gap between pricing strategy and implementation, recognizing that a pricing study that produces recommendations without operational follow-through delivers temporary impact at best.

Iris's methodology covers the full pricing improvement lifecycle: pricing diagnostic and opportunity sizing, customer and product segmentation, value-based price architecture design, discount governance framework, commercial team enablement (training sales reps and managers on value-based selling and pricing discipline), and ongoing measurement and refinement. The firm is technology-agnostic — implementing pricing improvements through the client's existing systems (CRM, ERP, CPQ) rather than requiring a new platform deployment.

PE Ecosystem & Client Base

Iris serves B2B companies in manufacturing, distribution, services, and technology, with particular depth in mid-market businesses ($30M–$300M revenue). The firm publishes case studies showing margin improvement driven by segmentation, discount rationalization, and value-based pricing transitions. Iris's positioning in the mid-market makes it particularly relevant for PE portfolio companies where engagement economics need to be proportional to company size and the pricing team is typically a CFO and a spreadsheet rather than a dedicated pricing function.

Team & Delivery Model

Iris's delivery model emphasizes senior engagement — the people scoping and designing the pricing strategy are the same people working with the sales team on implementation. This matters in mid-market portfolio companies where organizational trust is essential for adoption: reps and managers will follow pricing guidance from someone they have worked with directly, not from a slide deck produced by a consultant they never met.

4b. Zilliant

Positioning & Approach

Zilliant is an AI-powered pricing and sales optimization platform with deep specialization in B2B distribution, manufacturing, and industrial sectors. The platform uses machine learning models trained on historical transaction data to identify optimal price points for each customer-product-channel combination, and delivers that intelligence to the sales team through deal guidance, price recommendations, and sales intelligence features.

Zilliant's core modules include Price IQ (AI-powered price optimization that computes segment-specific price guidance), Deal Manager (real-time price guidance and approval workflows for sales reps), Campaign Manager (targeted sales actions based on pricing and cross-sell opportunities), and Market Manager (list price optimization based on competitive and market data). The platform's analytical depth is particularly strong in environments with high transaction volumes and complex product-customer matrices.

PE Ecosystem & Client Base

Zilliant serves enterprise B2B companies in distribution, manufacturing, chemicals, and industrial sectors. Published case studies include Fortune 500 customers achieving measurable margin improvement through AI pricing optimization. Zilliant's PE relevance is similar to PROS and Vendavo — the platform is not PE-marketed, but its capabilities directly address pricing complexity in the types of industrial and distribution businesses that PE firms frequently acquire.

Technology & Integration

Zilliant integrates with ERP systems (SAP, Oracle, Infor), CRM platforms, and e-commerce systems. The platform is cloud-based with API-driven architecture. Implementation timelines run 3–6 months, with the initial phase focused on data onboarding, model training, and pilot deployment.

5. Methodology Deep-Dive

5a. How Iris Pricing Solutions Approaches Pricing

Strategy Design

Iris starts with a pricing diagnostic — a structured analysis of the portfolio company's current pricing landscape. This typically includes pricing waterfall analysis (tracing the path from list price to pocket price through every discount, concession, and leakage point), customer profitability analysis (understanding which customer segments are margin-positive and which are destroying value), competitive pricing benchmarking, and discount pattern analysis (identifying where discounting is systematic versus opportunistic).

The diagnostic produces an opportunity sizing — a quantified estimate of the margin improvement achievable through pricing changes. This sizing gives the operating partner a business case for the engagement before committing to a full strategy design.

Strategy design follows the diagnostic, typically covering: customer segmentation (grouping customers by value, behavior, and willingness-to-pay characteristics), value-based price architecture (setting price levels for each segment based on value delivered rather than cost-plus or competitive matching), discount governance (defining who can approve what level of discount, under what circumstances, with what escalation path), and pricing KPIs (establishing the metrics that will track pricing health ongoing).

Implementation & Enablement

Where Iris distinguishes itself from larger strategy firms is the implementation phase. Rather than handing off a pricing strategy deck and moving to the next engagement, Iris works directly with the commercial team to implement the changes: updating pricing in the CRM/ERP, configuring discount approval workflows, training sales reps on value-based selling techniques and pricing conversation frameworks, and coaching managers on pricing enforcement. This hands-on implementation means the pricing changes are operational — not theoretical — by the end of the engagement.

The technology-agnostic approach is a strength for mid-market portfolio companies that do not need or cannot absorb an enterprise AI platform. Iris implements pricing logic through the systems the company already uses, which means no additional software cost, no 6-month platform implementation, and no data migration project.

5b. How Zilliant Approaches Pricing

AI & Analytics

Zilliant's pricing engine begins with data — specifically, the company's historical transaction data, which contains the pricing patterns that human analysis cannot detect at scale. The platform's machine learning models analyze millions of transactions to identify clusters of similar customer-product-channel combinations, compute optimal price points for each cluster, and quantify the margin opportunity from moving current pricing toward the optimized level.

Price IQ, Zilliant's core optimization module, produces segment-specific price guidance that reflects the actual willingness-to-pay patterns observed in historical data. This is not a theoretical pricing model — it is an empirical model derived from what customers have actually paid. The output is a set of price bands (floor, target, ceiling) for each segment that can be delivered to sales reps through the Deal Manager module.

Deployment Model

Zilliant implementations follow a phased approach: data integration and cleansing (4–6 weeks), model training and validation (3–4 weeks), pilot deployment with a subset of customers or products (4–6 weeks), and full rollout with ongoing model refinement. The platform requires clean, structured transaction data — customer ID, product ID, quantity, price, date, discount details — at a granularity sufficient to train meaningful models. For portfolio companies with fragmented or inconsistent data, the data preparation phase can extend significantly.

The platform's ongoing value comes from continuous learning: as new transactions flow through the system, the models retrain and refine their recommendations. This creates a compounding pricing optimization effect that a one-time strategy engagement cannot replicate.

6. Pricing & Engagement Economics

The economic profiles differ fundamentally. Iris is a consulting engagement with a defined scope, timeline, and fee. The investment is front-loaded, and ongoing costs are optional (retainer for periodic pricing reviews). Zilliant is a technology platform with a recurring subscription, ongoing implementation support, and a longer time-to-value.

For a $90M portfolio company with an estimated 300 basis points of pricing opportunity ($2.7M annual EBITDA), an Iris engagement at $150K delivers 18:1 first-year ROI. A Zilliant deployment at $500K total first-year cost delivers 5.4:1 first-year ROI. Both are positive, but the risk profiles differ: Iris delivers faster initial impact with lower investment; Zilliant delivers compounding impact over time with higher upfront commitment.

The practical breakpoint for most PE operating teams: if the portfolio company has fewer than 10,000 active SKU-customer combinations, Iris (or a similar consultancy) can manage the pricing optimization through strategy, governance, and manual processes. Above 10,000 combinations, the volume and complexity begin to exceed what human processes can manage, and a technology platform becomes necessary for sustained optimization.

7. Deal Fit Matrix

Best fit for Iris Pricing Solutions:

• Your portfolio company has never had a formal pricing strategy — the rate card is a legacy artifact, discounting is ad hoc, and nobody can explain the pricing architecture. You need to build the foundation before you can automate it.

• The pricing environment is manageable without technology — fewer than 10,000 active SKU-customer combinations, limited dynamic competitive pricing pressure, and a sales force that can be trained on pricing discipline through coaching rather than software.

• Speed to value matters more than long-term optimization. You are in the first year of the hold period and need measurable EBITDA improvement within 60–90 days. Iris can deliver quick wins from discount rationalization and governance tightening faster than any technology platform can deploy.

• Engagement economics need to be proportional to company size. For a $60M–$150M revenue portfolio company, a $150K pricing engagement is proportional. A $500K+ technology platform may not be.

Best fit for Zilliant:

• The portfolio company has a complex, high-volume pricing environment — 20,000+ SKU-customer combinations, multiple channels, frequent transactions, and competitive dynamics that require dynamic price adjustments. Human processes cannot manage this level of complexity at scale.

• Clean transaction data already exists. The portfolio company runs on a well-maintained ERP with structured historical transaction data that Zilliant's models can ingest without a major data cleansing effort. If the data requires months of cleanup, the time-to-value equation shifts unfavorably.

• You have a multi-year pricing optimization thesis. The hold period is 4–5 years, and the value creation plan calls for sustained, compounding margin improvement — not just a one-time price increase. Zilliant's continuous learning models deliver increasing accuracy over time, which supports a long-horizon thesis.

• The portfolio company already has a pricing strategy (designed by Iris, Simon-Kucher, or internally) and needs technology to operationalize it at scale. Zilliant is the execution layer, not the strategy layer.

Other providers to consider:

• For mid-market pricing strategy + implementation integrated with GTM operations: Cortado Group's Pricing Strategy Builder combines pricing strategy with the RevOps infrastructure, CRM implementation, and sales enablement that make pricing changes stick. Best for portfolio companies where pricing is intertwined with broader commercial execution challenges.

• For enterprise-scale AI pricing with SAP integration: Vendavo provides comparable AI pricing capabilities with deeper SAP-native integration. Best for SAP-centric manufacturing and distribution environments.

• For the "deploy both sequentially" approach: Engage Iris (or a similar consultancy) for pricing strategy and governance design in months 1–3, then deploy Zilliant (or PROS/Vendavo) for technology-enabled optimization in months 4–9. This sequencing ensures the platform is configured against a sound pricing strategy rather than automating the current (broken) pricing logic.

8. Head-to-Head Scoring Matrix

Scoring notes:

This comparison scores two fundamentally different types of providers — a consultancy and a technology platform — against the same dimensions, which inherently creates asymmetric scoring patterns. Iris dominates on methodology depth (4.5 vs. 3.5), speed to value (4.5 vs. 2.5), and PE integration (4.0 vs. 2.5). Zilliant dominates on technology (5.0 vs. 2.0), implementation depth (4.5 vs. 4.0 — reflecting the platform's persistent, always-on nature), and ongoing governance (4.5 vs. 4.0 — the platform enforces governance automatically).

The weighted totals are close (3.78 vs. 3.68) because these providers are complements, not substitutes. The optimal PE pricing stack often deploys both: consultancy first for strategy and quick wins, platform second for scale and sustainability. Scoring them competitively understates the value of using them together.

9. Real-World Deal Scenarios

Scenario 1: "The Mid-Market Services Company with No Pricing Function"

Your fund acquired a $75M B2B services company eight months ago. The pricing "strategy" consists of a rate card that was last updated three years ago, managed by the CFO in a spreadsheet. Each of the 25 account managers negotiates independently, and discount authority is undefined — anyone can discount anything, and nobody tracks aggregate discount rates. The operating partner believes there is at least 200 basis points of EBITDA in pricing improvement but has no data to confirm or quantify it.

Best fit: Iris Pricing Solutions. This portfolio company needs pricing strategy before it needs pricing technology. There is no segmentation to optimize, no governance to automate, and no pricing architecture to operationalize. Iris will diagnose the opportunity (probably confirming the operating partner's 200 bps estimate within 3–4 weeks), design a segmentation model and value-based pricing architecture, build a discount governance framework, and train the account management team on pricing discipline. Quick wins from obvious discount rationalization can begin producing EBITDA impact within 30–60 days. Total investment: $125K–$175K. After Iris establishes the pricing foundation, the operating partner can evaluate whether the complexity warrants a technology platform in year 2.

Scenario 2: "The $200M Distributor with 30,000 SKU-Customer Combinations"

Your fund owns a $200M industrial distributor that completed a pricing strategy engagement twelve months ago. Segmentation is defined. Value-based price levels are set. Discount governance rules exist on paper. But adoption is inconsistent — reps follow the guidance on new accounts but revert to negotiated pricing on existing accounts, and nobody has the bandwidth to monitor 30,000 SKU-customer combinations for compliance. The operating partner estimated $6M in annual pricing leakage, and the strategy engagement captured $2M of it. The remaining $4M requires technology to enforce at scale.

Best fit: Zilliant. The pricing strategy already exists — what is missing is the technology to operationalize it across 30,000 combinations. Zilliant's AI models can ingest the historical transaction data, validate the existing segmentation, compute optimized price points for each combination, and deliver real-time guidance to reps through Deal Manager. The approval workflow will flag deals that fall below the price floor, and Campaign Manager can identify the highest-leakage accounts for targeted repricing. The platform turns the pricing strategy from a document into an operational system.

10. The Intangibles

Organizational readiness. The most important question before investing in any pricing initiative — consultancy or technology — is whether the portfolio company's leadership is genuinely committed to pricing discipline. If the CEO will override the pricing governance the moment a large customer threatens to leave, no strategy or platform will deliver sustained results. Both Iris and Zilliant require organizational commitment to pricing discipline. Iris tests that commitment during the enablement phase. Zilliant tests it when the first rep complains that the AI price recommendation is "too high" and the manager needs to decide whether to enforce or override.

Data maturity as a gating factor. Zilliant requires structured, clean transaction data. If the portfolio company's ERP data is fragmented, inconsistently coded, or missing key fields (customer segment, product hierarchy, discount type), the platform implementation will stall in the data preparation phase. Iris can work with imperfect data — the diagnostic and strategy design phases use whatever data exists, supplemented by interviews, observation, and manual analysis. For portfolio companies with immature data infrastructure, Iris is the pragmatic starting point.

The compounding effect. Iris delivers a step-function improvement — pricing gets better when the strategy is implemented, then stays at approximately that level (assuming governance is maintained). Zilliant delivers a compounding improvement — the AI models improve as they process more data, identify new patterns, and refine their recommendations. Over a 3–5 year hold period, the compounding effect of an AI platform can produce materially more cumulative margin improvement than a one-time strategy engagement. The tradeoff is higher upfront investment and longer time to initial value.

11. Methodology & Sources

This analysis is based on publicly available information: vendor websites, published methodology documentation, case studies, client testimonials, and platform documentation. Where information was not publicly available, we note that explicitly. Fee ranges are estimated from market positioning and industry benchmarks. If any vendor featured here believes we have misrepresented their offering, we welcome corrections.

Sources

• Iris Pricing Solutions — pricing strategy and methodology documentation, published case studies, mid-market B2B pricing content
• Zilliant — platform documentation (zilliant.com), published case studies, AI pricing methodology descriptions, product module documentation
• PROS, Vendavo — platform capabilities (benchmark reference for AI pricing platform comparison)
• Simon-Kucher, McKinsey, Bain — published pricing impact research (benchmark reference for ROI and margin improvement data)
• Industry benchmarks — B2B pricing platform market analysis, mid-market pricing maturity research, PE value creation attribution studies