If you’ve tried to find out what MaestroQA costs, you’ve hit the same wall as everyone else: a pricing page with no prices. No tiers, no dollar amounts, no per-seat rates — just a contact form and a promise that “one of our experts will reach out.” For a platform that talks about transparency, that’s a gap when it comes to its own commercials.
MaestroQA has built a capable AI conversation analytics platform that processes 100% of customer interactions and connects QA scores to coaching workflows, data warehouse exports, and compliance monitoring. But the platform is mid-transition — actively rebranding to “Rippit” while repositioning from a contact centre QA tool to a broader “conversation data quality platform” — and its pricing stays behind a sales conversation.
We’ve pulled together what’s publicly known about MaestroQA, set it against evaluagent’s own published pricing, and included a detailed comparison so you can judge which is the better fit before you pick up the phone to either of us.
Is MaestroQA right for you?
It’s a strong choice if
- You need data warehouse integration (Snowflake, BigQuery, Redshift) to feed conversation insights into existing BI tools
- Your team wants natural-language querying of conversation data through AskAI
- You’re in a regulated industry requiring PCI DSS 4.0 Level 1 or ISO 42001 certification
- You value screen capture of agent desktops during interactions
- Your analysts want to choose between multiple LLM providers (Claude, GPT-4o, Gemini) per workflow
It may not suit you if
- You want to see pricing before talking to sales
- You need a dedicated module for evaluating AI chatbot and virtual agent quality
- You want built-in gamification and agent engagement tools alongside QA
- You prefer a platform that publishes its pricing model
- You need fast deployment without heavy configuration overhead
Pricing at a glance
| MaestroQA | evaluagent | |
|---|---|---|
| Pricing model | Per-agent-graded, custom quotes only; no public pricing | Per-user/month, published on website |
| Entry tier | Not disclosed — contact sales for a quote | AutoQA & Improvement: from $35/user/month |
| Full platform | Not disclosed; includes Conversation Analytics + AI Platform + Coaching | AutoQA + Conversation Intelligence: from $65/user/month |
| AI agent evaluation | Included in platform (pricing not disclosed) | From $0.05/conversation (AutoQA) or $0.13/conversation (Full Bundle) |
| Free trial | No public free trial; demo-only entry | No public free plan; demo and trial available on request |
| Best for | Teams wanting conversation analytics integrated with existing data warehouses and BI tools | Contact centres wanting published pricing with full QA automation, coaching, and AI agent observability in one platform |
MaestroQA’s pricing model, in depth
MaestroQA uses a per-agent-graded pricing model, meaning you pay based on the number of agents whose interactions are evaluated. Seats for reviewers, QA managers, and coaches come at no extra cost — beyond that structural detail, everything else requires a sales conversation.
The pricing page contains a contact form but no plan names, tier breakdowns, or dollar amounts. What we can piece together from public materials: role-based access tiers (Essentials/Team vs. Professional are referenced in user management documentation), flexible contracts with no long-term commitment required, and a dedicated Customer Success Manager plus Implementation Manager for every customer.
| Aspect | Details |
|---|---|
| Pricing unit | Per agent whose interactions are graded |
| QA / reviewer seats | Included at no extra cost |
| Published rates | None |
| Minimum commitment | Not disclosed |
| Contract length | Flexible, no long-term commitment required |
| Support | Dedicated CSM and Implementation Manager included |
The per-agent-graded model has a clear advantage: you don’t pay extra for the QA analysts, team leads, and managers who review and coach. If you have 100 agents and 10 QA reviewers, you pay for 100 seats, not 110 — costs stay predictable as your QA team grows.
| Pros | Cons |
|---|---|
| QA reviewer seats included free | No way to estimate costs without a sales call |
| Flexible contracts available | Can’t compare pricing to alternatives independently |
| Dedicated CSM for every customer | Budget approval requires sales engagement |
| Implementation Manager for onboarding | No self-serve trial to test before committing |
MaestroQA platform modules
Though pricing is hidden, MaestroQA’s platform is organised into distinct modules. Understanding what’s included helps frame what you’d be paying for.
| Analysis modules | |
|---|---|
| Conversation Analytics | Ingest and analyse 100% of conversations across channels; join with operational data from data warehouses |
| AI Platform (formerly AutoQA) | Create unlimited custom AI metrics using LLM, phrase match, or process-based classifiers; runs on 100% of interactions |
| AskAI | Natural-language querying of conversation data; worksheet-based analysis with multi-LLM selection |
| Reporting | Dashboards with line, bar, donut charts, and heat maps from QA, helpdesk, and WFM data |
| Action modules | |
|---|---|
| Quality Assurance | Rubric-based scoring with randomised and targeted grading assignments, calibration sessions, appeals |
| Coaching | AI-surfaced coaching opportunities, structured sessions with embedded conversation evidence, to-do tracking |
| Export | Hourly data warehouse sync to Snowflake, Redshift, BigQuery, Databricks, S3, and PostgreSQL |
| Screen Capture | Desktop recording during agent interactions with auto-highlighted critical moments |
Which modules are included at which price point, or whether any carry separate charges, is not disclosed.
| Pros | Cons |
|---|---|
| Full conversation analytics with warehouse integration | Feature gating between tiers not disclosed |
| Multi-LLM selection (Claude, GPT-4o, Gemini) | Screen Capture may carry separate costs |
| AskAI for ad-hoc natural-language queries | No clarity on what’s included vs. add-on |
| Bidirectional data warehouse sync | Configuration effort per AI metric can be significant |
evaluagent’s pricing
evaluagent publishes pricing on its website: $35/user/month for AutoQA & Improvement, $65/user/month for the full bundle with Conversation Intelligence, and $0.05–$0.13 per conversation for AI agent evaluation. Volume discounts are available for larger teams, and both tiers include a dedicated CSM and onboarding.
| Plan | Price | What’s included |
|---|---|---|
| AutoQA & Improvement | From $35/user/month |
|
| AutoQA + Conversation Intelligence (most popular) | From $65/user/month |
Everything above, plus:
|
| AI Agent Observability — from $0.05/conversation | |
|---|---|
| AutoQA for AI agents | From $0.05/conversation |
| Full bundle for AI agents | From $0.13/conversation |
| Prerequisite | Requires an active seat tier |
| Handover coverage | Bot-to-human conversations covered under seat licence |
| Bot platforms | Cognigy, Sierra, Decagon, and proprietary bots |
This is where evaluagent separates from MaestroQA. AI Agent Observability is a dedicated governance module that evaluates every AI agent conversation against the same quality standard applied to human agents — it detects hallucinations by grading bot responses against your organisation’s knowledge base, tracks containment quality, and identifies unrecoverable handovers. It operates above the agent layer, not inside it, so scores stay independent of whichever bot platform you use.
Feature-by-feature comparison
Pricing transparency
MaestroQA’s approach
Requires a sales conversation for any pricing information. The per-agent-graded model includes QA reviewer seats at no extra cost — a real structural advantage — but the base rate remains unknown until you engage sales.
evaluagent’s approach
Publishes pricing on its website: $35/user/month for AutoQA & Improvement, $65/user/month for the full bundle, and $0.05–$0.13 per conversation for AI agent evaluation. Volume discounts available for large teams.
AI-powered QA and scoring
MaestroQA’s approach
The AI Platform lets users create unlimited custom metrics using LLM classifiers, phrase match, or process-based logic. Users choose which LLM powers each workflow, with a build-test-refine loop that shows AI results alongside human grading before any metric goes live.
evaluagent’s approach
AutoQA scores 100% of conversations using the Context Engine, which grounds AI scoring in uploaded company policies, knowledge bases, and compliance rules. SmartScore provides AI-generated reasoning for every mark, and Blended Scorecards let some criteria be scored by AI while others go to human evaluators on the same scorecard.
Agent development and engagement
MaestroQA’s approach
Connects AI-surfaced coaching opportunities to structured sessions with embedded conversation evidence, customisable templates, and to-do tracking. Performance metrics can be embedded in coaching sessions — but the platform focuses on the manager-coach workflow and does not include gamification or agent-facing engagement mechanics.
evaluagent’s approach
Covers the same coaching ground with structured 1-to-1s tied to conversation evidence and performance improvement plans, but adds a full gamification layer — points, badges, leaderboards, and an auction-style reward system where agents bid on prizes using points earned from QA performance. A built-in LMS with auto-enrolment adds structured learning paths.
AI agent and chatbot governance
MaestroQA’s approach
Offers AI chatbot monitoring as a use case within its broader platform. It evaluates generative bot responses for hallucination and compliance violations, but it’s not structured as a separate module with its own pricing — it runs through the same AI Platform and QA workflows used for human agent evaluation.
evaluagent’s approach
Built AI Agent Observability as a dedicated, separately priced module. It operates above the agent layer, independent of the bot vendor, and includes fabrication detection grounded in the organisation’s knowledge base, containment analysis, intent-level performance reporting, and cross-vendor scoring.
Data and analytics integration
MaestroQA’s approach
Has invested heavily in data infrastructure. Bidirectional connections to Snowflake, BigQuery, Databricks, S3, and PostgreSQL let conversation data flow into existing BI tools while operational data flows back in for enriched analysis. AskAI lets analysts query conversation data in natural language, and a published Looker schema reference supports immediate BI setup — this is MaestroQA’s clearest competitive strength.
evaluagent’s approach
Supports data export to Power BI, Tableau, Looker, and Metabase through a reports exporter, and provides an open REST API with regional endpoints for programmatic access. The Spotlight tool provides on-demand AI investigation of conversation sets. It does not yet offer the same bidirectional warehouse integration or natural-language querying that MaestroQA provides.
Final verdict: MaestroQA vs evaluagent
The choice between MaestroQA and evaluagent depends on what matters most to your contact centre: data warehouse depth, or pricing transparency and agent development.
MaestroQA is a conversation data analytics platform built for enterprises that want to treat customer interactions as a data asset. With custom-quoted pricing based on agents graded and bidirectional data warehouse integrations, it lets analytics teams feed conversation insights into Snowflake, BigQuery, and existing BI tools while using natural-language querying to surface patterns across millions of interactions. This approach works best for organisations with mature data infrastructure and stakeholders beyond the QA team who need access to conversation intelligence.
evaluagent is a QA and performance management platform built around two principles: complete visibility across every agent — human and AI — and pricing you can evaluate before picking up the phone. With published pricing from $35/user/month, 100% conversation coverage, a dedicated AI Agent Observability module, and a closed-loop system connecting scoring to coaching, gamification, and agent development, it gives contact centres the QA infrastructure they need without requiring a sales conversation to understand what it costs.
The difference is philosophy. MaestroQA asks “how can we turn conversation data into enterprise intelligence?” evaluagent asks “how can we give contact centres complete quality visibility with pricing they can see before they buy?”