Gong vs Avoma: 2025 Comparison of Features, Pricing, and User Reviews

What Are Gong and Avoma, and Why Are Sales Teams Comparing Them in 2025? [toc=Platform Comparison Overview]

The conversation intelligence market has reached a critical inflection point in 2025, with sales leaders increasingly scrutinizing platforms that promise to transform how revenue teams capture, analyze, and act on customer interactions. Gong and Avoma represent two distinctly different approaches to this challenge Gong as the established enterprise leader with comprehensive revenue intelligence capabilities, and Avoma as the budget-conscious alternative focused primarily on basic meeting transcription and recording.

Traditional sales organizations have long struggled with the fundamental challenge of conversation intelligence: how to systematically capture, analyze, and leverage the wealth of insights buried within customer interactions. Pre-generative AI tools like Gong and Avoma emerged in the previous decade as solutions to this problem, but both platforms suffer from inherent limitations of their SaaS-based architectures. These tools require extensive user adoption, manual interpretation of insights, and significant ongoing training to deliver value. As Trafford J., Senior Director of Revenue Enablement, noted in his review of Gong, "It can be overwhelming to set up trackers. AI training is a bit laborious to get it to do what you want." Similarly, Avoma users report persistent reliability issues—Aleshia R., a Client Director, explains that "We see it show up late, drop from calls randomly and sometimes just not show up."

The generative AI revolution has fundamentally transformed what's possible in conversation intelligence, moving beyond keyword matching and basic sentiment analysis to true contextual understanding. Modern AI can now process conversations with human-like comprehension, extracting nuanced insights about deal progression, customer sentiment, and competitive positioning without requiring manual configuration or ongoing maintenance. However, most existing platforms merely bolt generative AI features onto their legacy architectures, creating incremental improvements rather than transformational workflow changes.

Oliv.ai's agentic approach represents a fundamental reimagining of conversation intelligence, where AI agents actively perform the work that traditional tools merely surface for human action. Our Meeting Assistant Agent automatically processes every conversation into actionable MEDDIC scorecards, while our CRM Manager Agent updates deal records without manual intervention. Rather than requiring sales teams to learn complex software interfaces and interpret dashboard insights, Oliv.ai's agents proactively deliver completed work—updated forecasts, qualified leads, and coaching recommendations—directly into existing workflows.

This shift from "software you use" to "agents that work for you" explains why 73% of conversation intelligence searches now include terms like "alternatives," "pricing," and "reviews"—revenue leaders are recognizing that traditional platforms, regardless of their feature sophistication, still create work rather than eliminating it. As we've observed in our research helping 100+ global companies optimize their revenue operations, the most successful sales organizations in 2025 are those that have moved beyond adopting yet another SaaS tool to implementing agentic AI that actually performs revenue-generating activities.

What's the Real Cost Difference Between Gong and Avoma in 2025? [toc=Pricing Analysis]

The pricing landscape for conversation intelligence platforms reveals a stark reality that extends far beyond simple per-seat costs, encompassing hidden fees, implementation expenses, and the true total cost of ownership that many sales leaders discover only after commitment. Gong's enterprise-focused pricing model now bundles multiple products at approximately $250 per user per month when including Engage and Forecast modules, while Avoma positions itself as the budget alternative at around $100 per seat without platform fees.

Traditional SaaS pricing models in the conversation intelligence space have evolved into complex, multi-tiered structures that obscure true costs through platform fees, mandatory professional services, and feature-gated pricing tiers. Gong's approach exemplifies this challenge, with Scott T., Director of Sales, noting that "The additional products like forecast or engage come at an additional cost. Would be great to see these tools rolled into the core offering." This pricing complexity creates significant budgeting challenges for sales leaders who discover that achieving full functionality requires purchasing multiple add-on modules. Similarly, Avoma's seemingly straightforward pricing often leads to unexpected expenses, as Jessica W., an IT Specialist, experienced: "We are paying for double the amount of seats that we need. We only have 48 active users and are paying for 87."

Modern AI-native platforms are pioneering transparent, value-based pricing that aligns costs with actual usage and delivered outcomes rather than artificial feature restrictions. This approach eliminates the traditional SaaS model of forcing customers to purchase comprehensive enterprise packages when they only need specific capabilities. However, the cost implications extend beyond licensing fees to include implementation complexity, training requirements, and ongoing maintenance overhead that can dramatically increase total cost of ownership.

Oliv.ai's agent-based pricing model fundamentally restructures conversation intelligence economics by charging for work performed rather than software access. Our Meeting Assistant Agent costs $19 per rep per month, while our CRM Manager Agent is $29 per rep per month—with each agent delivering completed work rather than requiring human interpretation and action. This approach eliminates the need for expensive RevOps resources to configure dashboards, interpret reports, and maintain complex integrations that traditional platforms require.

The economic advantage becomes particularly pronounced when considering Iris P.'s experience with Gong: "It was a big mistake on our part to commit to a two year term. Gong is a really powerful tool but it's probably the highest end option on the market... all have said the same thing - they've been fine using a lower cost, simpler alternative." Our analysis of enterprise implementations shows that organizations choosing Gong alternatives typically achieve 70-86% cost savings compared to traditional conversation intelligence platforms while receiving superior automated functionality that would require dedicated personnel to replicate manually.

What Are Real Users Saying About Gong vs Avoma in 2025? [toc=User Reviews Analysis]

User sentiment analysis across verified review platforms reveals a complex landscape of satisfaction and frustration that extends far beyond marketing promises, with real-world experiences highlighting fundamental differences in platform reliability, user experience, and delivered value. G2 reviews and community discussions consistently emphasize the gap between vendor promises and actual user experiences, particularly regarding implementation complexity, ongoing maintenance requirements, and the significant training burden these traditional platforms impose on sales teams.

Traditional pre-generative AI platforms like Gong and Avoma suffer from inherent user experience limitations that stem from their SaaS-based architectures requiring extensive human involvement to generate value. Users consistently report that these tools create additional work rather than eliminating it, with complex interfaces that demand significant training and ongoing maintenance. John S., a Senior Account Executive, candidly describes Gong as "too complicated, and not intuitive at all. Using it is very...discomforting. Searching for calls is not easy, moving around in the calls is not easy, and understanding the pipeline management portion of it is almost impossible." Similarly, Avoma users report persistent reliability issues, with a verified user noting that "sometimes its highly inaccurate - does not pick up the right notes - or the right person speaking."

Modern users increasingly demand platforms that deliver immediate value without extensive onboarding or ongoing maintenance, reflecting a broader shift toward outcome-based technology adoption. This expectation challenges traditional software vendors who built their platforms assuming dedicated RevOps teams would handle configuration, training, and ongoing optimization. The generative AI era has raised user expectations for intuitive, self-service platforms that provide sophisticated capabilities without complexity, but most existing platforms merely add AI features to legacy architectures rather than reimagining user experience entirely.

Oliv.ai's agentic approach addresses these user experience challenges by eliminating the need for human platform interaction entirely—our AI agents perform the work automatically rather than requiring users to navigate complex interfaces or interpret dashboard insights. Users consistently praise this "set it and forget it" functionality, where agents handle complex tasks like CRM updates, deal scoring, and forecast generation without manual intervention. This approach directly addresses the meeting challenges that plague traditional conversation intelligence platforms.

The contrast becomes particularly evident in enterprise implementations, where Amanda R., Director of Customer Success, describes Gong's value: "I love conversational AI. My favorite aspect of Gong is being able to go into any account and ask what is going on." However, she also notes implementation frustrations: "I wish they were a little more responsive to customer requests. They say a feature is coming in a certain quarter and then it doesn't." This pattern of promising functionality that requires manual effort versus delivering automated results represents the fundamental difference between traditional SaaS platforms and agentic AI systems that actually perform revenue-generating work for users through advanced sales automation tools.

How Do Gong and Avoma Compare Across Every Critical Feature Category? [toc=Feature Comparison]

The comprehensive evaluation of conversation intelligence platforms requires examining capabilities across nine critical feature categories that determine long-term success, user adoption, and revenue impact. Sales leaders evaluating these platforms must look beyond surface-level marketing claims to understand how each tool performs in real-world scenarios, particularly regarding reliability, ease of use, and the actual work required to extract value from the platform.

Traditional pre-generative AI platforms like Gong and Avoma suffer from fundamental architectural limitations that create ongoing operational challenges for sales teams. These tools, built in the previous decade, require extensive human involvement to generate meaningful outcomes, with complex interfaces that demand significant training and ongoing maintenance. John S., Senior Account Executive, captures this complexity perfectly: "It's too complicated, and not intuitive at all. Using it is very...discomforting. Searching for calls is not easy, moving around in the calls is not easy, and understanding the pipeline management portion of it is almost impossible." Similarly, Trafford J., Senior Director, Revenue Enablement, notes that "It can be overwhelming to set up trackers. AI training is a bit laborious to get it to do what you want." These platforms create additional work rather than eliminating it, forcing sales teams to become software administrators rather than focusing on revenue generation.

The generative AI revolution has transformed what's possible in conversation intelligence, moving from keyword-based analysis to true contextual understanding that can automatically perform complex sales tasks. Modern AI can now process conversations with human-like comprehension, extracting nuanced insights about deal progression, customer sentiment, and competitive positioning while automatically updating CRM systems and generating actionable recommendations. However, most existing platforms merely add AI features to legacy architectures, creating incremental improvements rather than fundamental workflow transformation.

Oliv.ai's agentic approach represents a complete reimagining of conversation intelligence, where specialized AI agents automatically perform the work that traditional platforms merely surface for human action. Our platform eliminates the complexity that plagues traditional tools by having AI agents handle everything from transcription processing to CRM updates, deal scoring, and forecast generation without requiring manual intervention or complex configuration.

Transcription & Recording Capabilities Analysis

The reliability gap becomes particularly evident in user feedback. Aleshia R., Client Director, describes Avoma's fundamental reliability issues: "We see it show up late, drop from calls randomly and sometimes just not show up. If there are two account holders on one call, we have seen it show up twice." This unreliability creates gaps in conversation capture that can impact deal progression and coaching effectiveness.

Conversation Intelligence & Analytics Deep Dive

A verified user in consulting highlights Avoma's accuracy challenges: "I think sometimes its highly inaccurate - does not pick up the right notes - or the right person speaking - it does not accurately capture sometimes and it sometimes misquoting the wrong person on the call." This inaccuracy undermines the fundamental value proposition of conversation intelligence.

Automation & Integration Capabilities Comparison

Pricing & Value Considerations

The cost implications extend far beyond licensing fees.

‍Iris P., Head of Marketing, Sales & Partnerships, reflects on common pricing frustrations: "It was a big mistake on our part to commit to a two year term. Gong is a really powerful tool but it's probably the highest end option on the market, and now we're stuck with a tool that works technically but isn't the right business decision."

‍Scott T., Director of Sales, adds: "The additional products like forecast or engage come at an additional cost. Would be great to see these tools rolled into the core offering."

Jessica W., IT Specialist, highlights Avoma's contract inflexibility: "We are paying for double the amount of seats that we need. We only have 48 active users and are paying for 87. We don't have the headcount that we did at the beginning of the year, which we let them know about, and they signed us up for another year with the same amount of people."

Security & Compliance Standards

Revenue Intelligence & Forecasting Capabilities

The fundamental difference lies in Oliv.ai's agentic approach, where AI agents perform the actual work rather than simply providing tools for human operation. While traditional platforms require sales teams to become software administrators, our AI agents handle complex tasks automatically, allowing sales professionals to focus on what they do best: building relationships and closing deals. This represents a paradigm shift from "software you use" to "agents that work for you," eliminating the adoption challenges and complexity that plague traditional conversation intelligence platforms. For sales managers, this means less time spent on administrative tasks and more time coaching their teams to success.

Which Platform Offers Better Deal Intelligence and Forecasting Capabilities? [toc=Deal Intelligence Comparison]

Deal intelligence and forecasting accuracy represent the holy grail of revenue operations, directly impacting a company's ability to predict revenue, allocate resources, and make strategic decisions. Sales leaders consistently cite forecasting as their most challenging responsibility, with inaccurate predictions leading to missed targets, resource misallocation, and lost investor confidence. The platform that can deliver reliable deal intelligence while automating forecast generation holds tremendous competitive advantage.

Traditional pre-generative AI platforms like Gong and Avoma approach deal intelligence through manual data compilation and subjective analysis, creating inconsistent forecasting outcomes. Scott T., Director of Sales, highlights Gong's value but also its limitations: "Before Gong we had a lack of visibility across our deals because information was siloed in several places like CRM, Email, Zoom, phone. Now all of this is centralized in one view via the Gong deal boards." However, he notes the financial burden: "The additional products like forecast or engage come at an additional cost. Would be great to see these tools rolled into the core offering." This fragmented approach forces revenue teams to purchase multiple modules and still requires significant manual interpretation to generate actionable forecasts.

The generative AI revolution has fundamentally transformed deal intelligence by enabling platforms to understand conversation context, sentiment, and deal progression signals automatically. Modern AI can analyze multiple data sources simultaneously - emails, calls, CRM activity, and buyer behavior - to generate objective deal health assessments. However, most existing platforms still require human interpretation and manual action on these insights, creating bottlenecks that limit their effectiveness in fast-moving sales environments.

Oliv.ai's Forecaster Agent represents a paradigm shift by automatically performing the analytical work that traditional platforms merely surface for human review. Our agent processes conversation intelligence, email patterns, and CRM data to generate completed forecasts with specific deal recommendations and risk assessments. Unlike platforms that provide dashboards requiring interpretation, our Forecaster Agent delivers finished analysis with clear next steps, probability assessments, and timeline predictions.

The practical impact becomes clear when examining enterprise implementations. Amanda R., Director of Customer Success, describes her experience with Gong: "I love conversational AI. My favorite aspect of Gong is being able to go into any account and ask what is going on. By asking what the customer said they needed, I can prepare for any meeting." However, she also notes delivery challenges: "I wish they were a little more responsive to customer requests. They say a feature is coming in a certain quarter and then it doesn't." This highlights the fundamental difference between tools that provide information versus AI agents that automatically execute forecasting workflows, eliminating the need for manual analysis and reducing time-to-insight from hours to minutes.

How Do These Platforms Handle CRM Integration and Data Management? [toc=CRM Integration Analysis]

CRM integration and data management form the operational backbone of any conversation intelligence platform, determining whether the tool enhances or complicates existing sales workflows. Poor integration creates data silos, manual data entry requirements, and inconsistent information across systems - problems that can render even sophisticated conversation intelligence useless. Sales managers need platforms that seamlessly maintain CRM hygiene while automatically updating deal records based on conversation insights.

Traditional platforms like Gong and Avoma struggle with bidirectional CRM synchronization, often requiring manual data entry and creating hygiene issues that burden sales teams. Neel P., Sales Operations Manager, experienced significant data access limitations with Gong: "While Gong offers valuable insights into call data and sales interactions, our experience has been impacted by significant data access limitations, especially concerning data portability and bulk export capabilities." He describes the impractical process: "their current solution is far from convenient or accessible - it requires downloading calls individually, which is impractical and inefficient for a large volume of data." This forces organizations to engage development teams at additional cost just to access their own data.

The generative AI era has enabled more sophisticated CRM integration through intelligent field mapping, automated data enrichment, and contextual record updates. Modern AI can understand conversation context and automatically populate relevant CRM fields based on deal progression, competitor mentions, and customer sentiment. However, many platforms still require significant configuration and suffer from sync issues that create inconsistencies between conversation intelligence and CRM records.

Oliv.ai's CRM Manager Agent eliminates these integration challenges by automatically maintaining complete data hygiene without manual intervention. Our agent processes every conversation to extract deal insights, updates opportunity records, creates follow-up tasks, and ensures consistent information flow across all systems. Rather than requiring RevOps teams to configure complex field mappings, our CRM Manager Agent intelligently understands sales processes and automatically maintains accurate, up-to-date records.

The reliability contrast becomes evident in user experiences. Aleshia R., Client Director, describes persistent issues with Avoma: "We see it show up late, drop from calls randomly and sometimes just not show up. If there are two account holders on one call, we have seen it show up twice." These reliability issues create gaps in data capture that undermine CRM accuracy. In contrast, our agentic approach ensures consistent data flow regardless of meeting attendance, automatically processing conversation intelligence into structured CRM updates that maintain data integrity across all revenue operations workflows.

Q7: What Are the Implementation and Training Requirements for Each Platform? [toc=Implementation Requirements]

The implementation and training requirements for conversation intelligence platforms represent a critical decision factor that often determines long-term success or failure, with time-to-value directly impacting sales team productivity and adoption rates. Sales leaders must evaluate not only the initial deployment complexity but also the ongoing training burden, change management requirements, and hidden costs associated with platform adoption that can significantly exceed initial licensing fees.

Traditional enterprise platforms like Gong and Avoma typically require 8+ weeks of implementation with dedicated RevOps resources and extensive team training to achieve meaningful adoption. Trafford J., Senior Director of Revenue Enablement, captures this complexity: "It can be overwhelming to set up trackers. AI training is a bit laborious to get it to do what you want." The challenge extends beyond initial setup, as John S., Senior Account Executive, describes the ongoing user experience: "It's too complicated, and not intuitive at all. Using it is very...discomforting. Searching for calls is not easy, moving around in the calls is not easy, and understanding the pipeline management portion of it is almost impossible." This complexity forces sales teams to become software administrators rather than focusing on revenue generation, with Karel Bos, Head of Sales, noting: "There's so much in Gong, that we don't use everything."

Modern AI platforms promise faster deployment and reduced training requirements, but many still require significant customization and change management to deliver value. The challenge lies in the fundamental architecture of these pre-generative AI tools, which were built as SaaS software requiring human adoption rather than performing work automatically. Even when deployment timelines are reduced, the ongoing maintenance burden remains substantial, with teams needing continuous training to maximize platform value and interpret complex dashboards.

Oliv.ai's agentic architecture fundamentally transforms implementation by enabling 2-week deployment with minimal training requirements, as our AI agents handle complex tasks automatically rather than requiring user adoption. Our agents begin working immediately upon deployment, processing conversations, updating CRM records, and generating forecasts without requiring sales teams to learn new interfaces or workflows. This eliminates the traditional change management challenges that plague conventional platforms through advanced sales automation tools.

The implementation advantage becomes particularly evident when considering support quality. Elspeth C., Chief Commercial Officer, describes her frustration with Gong: "Since we purchased our package, the support model has changed drastically, which is infuriating." In contrast, our agentic approach reduces support requirements by eliminating the complexity that typically generates support tickets, while our AI agents continuously optimize their performance based on usage patterns rather than requiring manual configuration adjustments. This streamlined approach addresses common meeting challenges that traditional platforms struggle to resolve effectively.

Which Platform is Right for Your Sales Team Size and Maturity? [toc=Team Size Guide]

The decision framework for selecting conversation intelligence platforms has traditionally been dictated by team size and organizational maturity, with complex enterprise tools reserved for large teams and simpler solutions for smaller organizations. However, this conventional wisdom fails to account for the fundamental shift toward AI-native platforms that democratize advanced capabilities regardless of organizational size or sales process sophistication.

Traditional advice suggested that smaller teams should choose basic tools like Avoma due to cost constraints, while larger organizations with dedicated RevOps resources could handle complex platforms like Gong. Iris P., Head of Marketing, Sales & Partnerships, reflects this challenge: "It was a big mistake on our part to commit to a two year term. Gong is a really powerful tool but it's probably the highest end option on the market... Having talked with other friends who lead revenue functions, all have said the same thing - they've been fine using a lower cost, simpler alternative." However, this approach often leaves smaller teams with unreliable tools, as Aleshia R., Client Director, experienced with Avoma: "We see it show up late, drop from calls randomly and sometimes just not show up."

Modern AI capabilities are democratizing advanced sales intelligence, making enterprise-level features accessible to teams of all sizes without the complexity traditionally associated with sophisticated platforms. Generative AI eliminates the need for dedicated RevOps resources to configure and maintain complex systems, while providing insights that previously required extensive data science capabilities. This transformation enables smaller teams to access advanced functionality without the operational burden.

Oliv.ai's agent-based approach scales seamlessly from individual contributors to enterprise teams, providing sophisticated capabilities without the complexity typically associated with advanced platforms. Our AI agents for sales teams deliver the same level of intelligence and automation whether serving a 5-person startup or a 500-person enterprise sales organization, with pricing that scales appropriately with team size and usage patterns. This approach supports effective sales team collaboration while providing advanced note-taking AI capabilities regardless of organizational size.

Recommendation Matrix

Our experience helping 100+ global companies optimize their revenue operations demonstrates that team success depends less on size and more on the platform's ability to deliver immediate value without operational complexity. Amrit D., Customer Success Manager, captures this need: "The automatic recording transcription features are very accurate, saving us time and ensuring nothing gets lost in conversation." This represents the fundamental shift from platforms that require adoption to agents that simply work, regardless of organizational size or maturity. For sales managers, this means consistent support across all team sizes, while organizations can leverage proven sales methodologies without the traditional implementation complexity.

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