Top Voice Agent Testing Platforms 2025: Complete Comparison Guide

This comparison is for teams deploying voice agents that handle thousands of calls—latency requirements under a second, noisy audio conditions, regulatory compliance. If you're building text-based chatbots or internal prototypes with a handful of test calls, general LLM evals and manual testing will work fine. But for production voice agents, general tools miss 40% of the failures your users will experience.

Quick filter: If your testing is transcript-only, you are missing the failures customers feel.

Disclosure: Hamming is one of the platforms compared in this guide. We've aimed for objectivity, but recommend verifying our claims about competitors directly with those vendors.

Voice agent testing looked like "LLM evaluation plus audio transcripts" at first. Then I watched teams ship agents that scored 95% on text-based evals and fail catastrophically in production. Voice testing is a different discipline entirely.

The pattern—call it the "transcript trap"—is that teams test against transcripts, not audio, and miss everything that happens between the user's mouth and the agent's response. Latency, ASR errors, interruption handling, background noise robustness—none of it appears in a transcript. The conversation looks perfect. The experience was terrible.

We see this pattern constantly. A team spends months building a voice agent, runs it through their text-based eval suite, and ships it. Within a week, they're debugging production issues that their testing never caught: latency spikes causing users to talk over the agent, Automatic Speech Recognition (ASR) errors breaking intent detection, interruptions that the agent handles poorly.

These aren't edge cases. Based on Hamming's analysis of 1M+ production voice agent calls across 50+ deployments, voice-specific failures account for 42% of all production issues. Text-based testing catches response quality. It completely misses everything else.

This guide compares the top voice agent testing platforms against the criteria that actually matter. We'll be direct about what each platform does well and where it falls short.

TL;DR: Choose a voice agent testing platform using Hamming's 6-Criterion Platform Evaluation Framework:

• End-to-end call simulation (25%): Real phone calls via PSTN/SIP, not just API testing
• Audio quality testing (20%): Word Error Rate (WER) tracking, latency percentiles, noise simulation
• Conversation flow (20%): Multi-turn context, interruption handling, turn-taking
• Scale (15%): 100+ concurrent test calls for production readiness
• Integration breadth (10%): Voice platforms, CI/CD, observability tools
• Reporting (10%): Real-time dashboards, alerting, historical analysis

General LLM testing tools miss 40% of voice-specific failures. If you're running production voice agents at scale, specialized platforms are essential.

Related Guides:

• How to Evaluate Voice Agents — Hamming's VOICE Framework
• Voice Agent QA Guide — 4-Layer QA Framework
• 7 Essential Criteria for Voice Agent QA Software — 7-Criterion Evaluation Framework
• 12 Questions to Ask Voice Testing Vendors — Vendor Evaluation Checklist
• Multi-Modal Agent Testing: Voice, Chat, SMS, and Email — Unified testing across channels

Platform-Specific Testing Guides:

• How to Test Voice Agents Built with Vapi
• How to Test Voice Agents Built with LiveKit
• How to Test Voice Agents Built with Retell

Methodology Note: Platform comparisons are based on Hamming's evaluation of 1M+ voice agent interactions across 50+ enterprise and startup deployments (2025). Feature accuracy verified as of January 2025. Competitor capabilities may have changed. Contact vendors directly for current features and pricing.

What Is a Voice Agent Testing Platform?

A voice agent testing platform is specialized software that validates AI-powered voice systems through simulated calls, production monitoring, and automated evaluation. Unlike general LLM testing tools that analyze text responses, voice agent testing platforms understand the complete audio pipeline.

The core question: Which failures do you actually care about catching?

Voice agents fail in ways text-based systems never do. Here's what we see when we analyze production calls:

When we reviewed 1,000 production voice agent failures at Hamming, 42% were voice-specific issues. Transcript-only analysis would miss them entirely.

Why You Need Specialized Voice Agent Testing

Most teams start with what they know: text-based LLM evals. That works for chatbots. For voice agents, it creates a blind spot that grows with scale.

The Problem

Consider a team processing 10,000 calls per day. Their text-based evaluation shows 95% response accuracy. Looks great.

But when we instrument the actual voice pipeline, we find:

• P95 latency: 2.3 seconds (users start talking over the agent)
• ASR Word Error Rate: 18% in noisy conditions (intents get misclassified)
• Interruption recovery: 62% (agent keeps talking when users cut in)

None of this appears in transcript analysis.

The Reality

At 10,000 calls/day with 5% voice-specific failures, that's 500 poor experiences daily.

At $50 average customer value, that's $25,000 in daily risk exposure. Over 90 days, $2.25M in potential customer impact.

ROI Calculation (apply to your numbers):

Daily Risk = Call Volume × Voice-Specific Failure Rate × Avg Customer Value
           = 10,000 × 5% × $50
           = $25,000/day

90-Day Risk Exposure = $25,000 × 90 = $2,250,000

Compare this to dedicated testing platform costs (~$2-5K/month for most teams) to quantify ROI.

Teams that rely on text-based testing typically discover these issues through customer complaints, not proactive monitoring.

The Fix

Voice-native testing platforms evaluate the complete audio interaction. They catch latency, ASR accuracy, interruption handling, and TTS quality issues before they reach production.

The question isn't whether you need voice-specific testing. It's whether you catch these failures before or after your users do.

What General LLM Testing Tools Miss

Based on Hamming's analysis of 50+ deployments, here are the voice-specific failure modes that general tools overlook:

Failure Mode 1: Latency Invisible in Transcripts

The Problem: Your transcript shows a perfect conversation. What it doesn't show is the 2.5-second pause before each agent response.

The Reality: According to Hamming's latency benchmarks, P95 response time above 800ms triggers measurable user frustration. At 1.2 seconds, users start talking over the agent. At 2+ seconds, call abandonment spikes.

Worked Example:

System A: Average latency 400ms, P95 at 600ms
System B: Average latency 400ms, P95 at 2400ms

Both systems report the same average. But System B delivers terrible experiences to 5% of users. At 10,000 calls/day, that's 500 frustrated users daily.

The Fix: Track P50, P95, and P99 latency at the audio level. Set alerts on percentiles, not averages.

Failure Mode 2: ASR Errors Compounding Through the Pipeline

The Problem: The user says "reschedule my Tuesday appointment." ASR transcribes it as "schedule my appointment Tuesday." The LLM books a new appointment instead of rescheduling.

The Reality: A single word substitution ("reschedule" → "schedule") completely changes the user's intent. Transcript-based evaluation might score the agent's response as correct. The user's problem went unsolved.

Worked Example:

• Substitutions: 1 (reschedule → schedule)
• Deletions: 1 (for)
• Insertions: 0
• Total words: 8

WER = (1 + 1 + 0) / 8 × 100 = 25%

According to Hamming's ASR benchmarks, WER above 10% in clean audio indicates a significant accuracy problem.

The Fix: Measure WER with actual audio, not just final transcripts. Test across acoustic conditions: clean audio, office noise (45dB), street noise (70dB).

Failure Mode 3: Barge-In Behavior Breaking Conversations

The Problem: The user interrupts mid-sentence. The agent keeps talking, ignoring the interruption completely. Or worse, it stops but loses context about what the user was saying.

The Reality: Based on Hamming's conversation flow analysis, interruption recovery rate below 90% causes noticeable user frustration. Many voice agents score below 70%.

The Fix: Test interruption handling explicitly. Verify the agent stops speaking, acknowledges the interruption, and addresses the new topic while retaining context.

Failure Mode 4: Background Noise Sensitivity

The Problem: Your agent works perfectly in the quiet of your office. Users call from cars, coffee shops, and busy streets.

The Reality:

According to Hamming's testing data, agents not validated against realistic noise conditions see 2-3x higher failure rates in production.

The Fix: Test with background noise injection at 20dB, 10dB, and 5dB Signal-to-Noise Ratio (SNR) levels. For a complete testing methodology, see our background noise testing KPIs guide.

Hamming's 6-Criterion Platform Evaluation Framework

When evaluating voice agent testing platforms, assess them across six weighted criteria. We developed this framework based on what we've seen matter most across 50+ deployments.

Let's examine each criterion in detail.

Criterion 1: End-to-End Call Simulation (25%)

The most critical capability is making real phone calls to your voice agent. API simulation misses telephony-specific issues.

What API simulation misses:

• Session Initiation Protocol (SIP) reliability and handshake timing
• Audio codec handling and quality degradation
• Public Switched Telephone Network (PSTN) network latency variability
• Dual-Tone Multi-Frequency (DTMF) recognition reliability
• Inbound vs. outbound call behavior differences

What to evaluate:

• Does the platform make actual phone calls (PSTN/SIP)?
• Can you test both inbound and outbound call flows?
• Does it simulate different network conditions and carriers?
• Can you test with realistic personas, accents, and speech patterns?

Why it matters: Based on Hamming's experience with 50+ deployments, teams that skip real phone testing face 2-3x more production incidents. API simulation gives you confidence that often doesn't survive first contact with real telephony infrastructure.

Criterion 2: Audio Quality Testing (20%)

Voice agents depend on accurate speech recognition and natural-sounding synthesis. Testing must evaluate the audio layer directly.

What to evaluate:

• Word Error Rate (WER) measurement with reference transcripts
• Latency tracking at P50, P95, P99 percentiles
• Background noise simulation at configurable SNR levels
• TTS quality assessment (naturalness, pronunciation)
• Accent and dialect handling across user demographics

Benchmark targets (from Hamming's analysis):

Criterion 3: Conversation Flow Analysis (20%)

Voice agents must handle the dynamics of natural conversation. Most evaluation focuses on individual responses. That misses how the conversation works as a system.

What to evaluate:

• Multi-turn scenario testing (5+ turns minimum)
• Interruption handling and barge-in behavior
• Context retention across turns
• Turn-taking efficiency and timing
• Conversation state validation

Key metrics (from Hamming's conversation flow analysis):

Criterion 4: Scale and Concurrency (15%)

Production-ready testing requires running many calls simultaneously. Load testing catches issues that don't appear in sequential testing.

What to evaluate:

• Maximum concurrent calls supported
• Call throughput (calls per hour)
• Performance degradation under load
• Geographic distribution of test calls

Scaling guidance (from Hamming deployments):

Criterion 5: Integration Breadth (10%)

Your testing platform must connect with your voice infrastructure and development workflow. Native integrations reduce friction. Webhook-only connections require custom development.

Priority integrations:

1. Your voice platform (native integration required): Vapi, Retell, LiveKit, Bland, Twilio, Daily
2. Your CI/CD pipeline (for automated regression testing): GitHub Actions, GitLab CI, Jenkins
3. Your alerting system (for production monitoring): PagerDuty, Slack, email

What to evaluate:

• Native vs. webhook-based integrations
• API access for custom automation
• Single Sign-On (SSO) and enterprise authentication
• Data export and reporting APIs

Criterion 6: Reporting and Analytics (10%)

Effective testing produces actionable insights, not just raw data. The question is whether you can quickly identify what broke and why.

What to evaluate:

• Real-time dashboards with key metrics
• Historical trend analysis
• Custom alerting with configurable thresholds
• Drill-down from metrics to specific calls
• Exportable reports for stakeholders

Top Voice Agent Testing Platforms 2025

Let's examine the leading platforms across our six evaluation criteria. We'll be direct about strengths and limitations.

⚠️ Accuracy Note: Platform capabilities change frequently. We've verified features as of January 2025, but you should confirm directly with vendors before making decisions. Vapi and Retell in particular ship updates regularly.

1. Hamming

Overview: Purpose-built voice agent testing and monitoring platform. Runs synthetic voice calls at scale and monitors production calls in real-time.

Best for: Teams running production voice agents at scale who need comprehensive pre-launch testing and production monitoring.

Key differentiators:

• Audio-native evaluation (analyzes actual audio, not just transcripts)
• Background noise injection at configurable SNR levels (20dB, 10dB, 5dB)
• DTMF and IVR navigation testing
• SOC 2 Type II certified
• Scenario rerun for debugging (replay exact test conditions)
• CI/CD native with deployment blocking on regression

What's hard: Hamming is built for voice-specific testing. If you're evaluating text-based LLM responses, it's not the right tool.

2. Braintrust

Overview: General-purpose LLM evaluation platform with a strong experimentation framework. Primarily designed for text-based AI evaluation.

Best for: Teams evaluating text-based LLM responses, not voice agents.

Strengths:

• Excellent for A/B testing prompt variations
• Good dataset management
• Strong LLM provider ecosystem

Limitations for voice:

• No actual phone call testing
• Cannot measure latency at the audio level
• Misses ASR/TTS quality issues entirely
• No interruption or barge-in testing
• Transcript-only analysis misses 40% of voice-specific failures

3. Langfuse

Overview: Open-source LLM observability platform with tracing, prompt management, and evaluation capabilities.

Best for: Teams wanting open-source LLM observability, not voice-specific testing.

Strengths:

• Open-source with self-hosting option
• Good tracing and debugging for LLM calls
• Active community and development

Limitations for voice:

• No synthetic voice call generation
• No production voice call monitoring
• Cannot test voice-specific failure modes
• Transcript-only analysis

4. Observe.AI

Overview: Contact center analytics platform focused on human agent coaching and compliance. Designed for traditional call centers with human agents.

Best for: Traditional contact centers with human agents who need coaching and compliance monitoring.

Strengths:

• Excellent for human agent QA
• Strong compliance and coaching features
• Enterprise contact center integrations

Limitations for AI voice agents:

• Designed for human QA, not AI agent testing
• No pre-launch synthetic testing
• No regression detection for prompt or model changes
• No CI/CD integration
• Cannot block deployments on quality regression

5. Speechmatics

Overview: Speech-to-text provider with transcription accuracy testing capabilities.

Best for: Teams specifically evaluating and benchmarking ASR/STT accuracy.

Strengths:

• Excellent ASR accuracy benchmarking
• Multi-language support
• Enterprise-grade transcription

Limitations for voice agents:

• Only tests the transcription layer
• No end-to-end voice agent evaluation
• No latency or conversation flow testing
• No TTS or response quality assessment
• Cannot evaluate the complete voice agent pipeline

6. Vapi Test Suites (Built-in)

Overview: Vapi's native testing feature released in early 2025. Simulates AI-to-AI conversations and evaluates transcripts using LLM-as-judge.

Best for: Vapi users who need basic functional testing of their agents before deployment.

Strengths:

• Native to Vapi platform (no additional setup)
• Convert production failures to test cases directly from dashboard
• LLM-as-judge evaluation with custom rubrics
• Free with Vapi subscription (tests consume regular call minutes)

Limitations (why teams outgrow it):

• No real phone testing: Simulates AI-to-AI chat, not actual PSTN/SIP calls. Misses telephony-specific issues like codec handling, network latency, and carrier variability.
• Transcript-only evaluation: Cannot measure audio quality, WER, TTS naturalness, or latency percentiles. As Vapi's own documentation notes, "they evaluate mainly what was said, not how it sounded."
• Limited scale: 50 test cases maximum per suite. At 5 attempts each, that's 250 test runs maximum. Production voice agents need thousands of concurrent scenarios.
• No background noise simulation: Tests run in clean audio conditions only. Real users call from cars, coffee shops, and busy streets.
• No interruption testing: Cannot validate barge-in behavior or turn-taking dynamics.
• Chat mode recommended: Vapi's docs recommend chat over voice mode because voice is slower. This defeats the purpose of voice-specific testing.

The reality: Vapi Test Suites are useful for basic sanity checks during development. They're not a replacement for production-grade voice agent testing. Teams running 10K+ calls/month typically need dedicated testing infrastructure.

Deep dive: How to Test Voice Agents Built with Vapi — Complete testing guide for Vapi agents.

7. LiveKit Agents Testing Framework (Built-in)

Overview: LiveKit's native testing framework for behavioral testing of AI agents using pytest. Operates in text-only mode.

Best for: LiveKit developers who need to test agent logic and tool calling during development.

Strengths:

• Comprehensive behavioral testing API (fluent assertions, tool mocking)
• LLM-as-judge for qualitative evaluation
• Multi-turn conversation testing with history
• Works with any pytest workflow
• No LiveKit API keys needed for tests

Limitations (why teams outgrow it):

• Text-only mode by design: LiveKit's docs explicitly state their testing helpers work "with text input and output" and are "the most cost-effective way to write tests." This means zero audio pipeline testing.
• No phone/PSTN testing: LiveKit is WebRTC-based. Their testing framework doesn't simulate phone calls, SIP, or carrier-level issues.
• Python-only: If your team isn't Python-native, the testing framework isn't accessible.
• No production monitoring: Testing is pre-deployment only. No real-time call analysis.
• LiveKit acknowledges the gap: Their own docs recommend third-party tools (including Hamming) "to perform end-to-end testing of deployed agents, including the audio pipeline."

The reality: LiveKit's testing framework is solid for what it does: behavioral testing of agent logic. But it explicitly doesn't test voice. For audio pipeline testing, LiveKit points teams to dedicated platforms.

Deep dive: How to Test Voice Agents Built with LiveKit — Complete testing guide for LiveKit agents.

8. Retell Simulation Testing (Built-in)

Overview: Retell's batch testing and simulation feature for testing agents through simulated conversations.

Best for: Retell users who need quick validation during development.

Strengths:

• Native to Retell platform
• LLM Playground for rapid iteration
• Batch testing for running multiple scenarios
• Import/export test cases as JSON

Limitations (why teams outgrow it):

• No real phone testing: Simulates conversations, doesn't make actual calls. Misses all telephony-layer issues.
• No audio metrics: Cannot track WER, latency percentiles, or TTS quality. Transcript-level analysis only.
• No built-in sandbox or version history: As noted in reviews, "prompt flows must be built manually, and debugging complex fallback chains requires engineering time."
• No real-time testing console: Teams must "simulate calls manually or create their own test infrastructure for quality assurance."
• Platform limits: 1-hour max call length, 8192 token limit for Retell LLM.
• No CI/CD native integration: Requires custom development to integrate with deployment pipelines.
• Developer-focused: Non-technical stakeholders need engineering support. No visual builder or RBAC controls.

The reality: Retell's simulation testing helps during initial development but lacks the depth for production QA. Teams building customer-facing voice agents typically supplement with dedicated testing platforms.

Deep dive: How to Test Voice Agents Built with Retell — Complete testing guide for Retell agents.

Why Platform-Native Testing Isn't Enough

Vapi, Retell, and LiveKit all built testing features to help developers iterate faster. That's valuable. But there are fundamental problems with this approach.

The Fox Guarding the Hen House

Here's the core issue: orchestration and evaluation should be independent.

When your voice platform also runs your tests, you have the fox guarding the hen house. The same system that might have bugs is the system evaluating whether bugs exist. This creates structural blind spots:

• Platform bugs affect both execution and evaluation. If Vapi's audio pipeline has a latency issue, Vapi's test suite might have the same issue and not flag it as abnormal.
• No external validation. You're trusting the platform to honestly report its own failures. There's an inherent conflict of interest.
• Tightly coupled, brittle tests. Platform-native tests are coupled to internal implementation details. When the platform changes, tests break in unpredictable ways.
• Can't catch platform-level issues. If Retell's infrastructure has regional latency problems, Retell's simulation testing won't detect it because it's running in the same infrastructure.
• Text-only testing misses voice entirely. LiveKit's testing framework explicitly operates in text-only mode. It tests agent logic, not the audio pipeline that makes voice agents work.

This isn't about trust or bad intentions. It's about architecture. Separation of concerns is a fundamental engineering principle. Your CI/CD pipeline doesn't run inside your production application. Your monitoring doesn't run on the same servers it monitors. Your voice agent evaluation shouldn't run inside your voice agent platform.

What Platform-Native Tools Actually Test vs. What They Miss

The Data

Based on Hamming's analysis of teams transitioning from platform-native to dedicated testing: teams relying solely on Vapi Test Suites or Retell Simulation Testing discover 3-4x more issues in their first week of production compared to teams using dedicated voice testing platforms.

The platforms themselves acknowledge this architectural limitation. That's why Hamming partners with Vapi, Retell, and LiveKit to provide independent, enterprise-grade testing infrastructure.

9. Custom/In-House Solutions

Overview: Many teams build internal testing tools using Twilio, scripted calls, and custom evaluation logic.

Best for: Teams with highly unique requirements not served by existing platforms.

Challenges we've seen:

• 2-4 months to build minimum viable solution
• Ongoing maintenance burden (10-20% of initial investment annually)
• Missing advanced features (noise simulation, interruption testing, latency percentiles)
• No benchmarking against industry standards
• Engineering focus diverted from core product

The real cost: One team we talked to spent 4 engineering months building a custom solution. It handled basic call testing but couldn't simulate background noise or measure barge-in behavior. They eventually migrated to a purpose-built platform.

Feature Comparison Matrix

This matrix compares all platforms across 15 key capabilities, including platform-native testing tools:

Legend: ✅ Full support | ⚠️ Partial/Limited | ❌ Not supported | *Inherited from parent platform

Key insight: Platform-native testing tools (Vapi Test Suites, LiveKit Testing, Retell Simulation) are useful for development-time validation but lack the infrastructure for production-grade QA. LiveKit explicitly operates in text-only mode. Vapi and Retell test transcripts but miss the audio layer entirely.

Case Study: NextDimensionAI (Healthcare)

Here's what platform selection looks like in practice for a team building HIPAA-compliant voice agents.

NextDimensionAI builds voice agents for healthcare providers, handling scheduling, prescription refills, and medical record lookups. Their agents integrate directly with Electronic Health Record (EHR) systems and operate autonomously.

The Challenge:

• Engineers could only make ~20 manual test calls per day
• Full-team "testing sessions" to validate releases weren't sustainable
• HIPAA compliance required testing edge cases around Protected Health Information (PHI)
• Qualitative issues (pauses, hesitations, accent handling) weren't captured reliably

Platform Evaluation: They considered building custom tooling with Twilio but estimated 3-4 months of engineering time. They also evaluated general LLM testing tools but found they couldn't test actual phone calls or measure audio-level metrics.

The Implementation:

1. Created scenario-based tests mirroring real patient behavior (pauses, accents, interrupted speech)
2. Ran controlled tests across carriers, compute regions, and LLM configurations
3. Converted every production failure into a reproducible test case
4. Built a growing library of real-world edge cases for regression testing

The Results:

"For us, unit tests are Hamming tests. Every time we talk about a new agent, everyone already knows: step two is Hamming." — Simran Khara, Co-founder, NextDimensionAI

Key Insight: Their QA loop blends automated evaluation with human review. When a production call fails, it becomes a permanent test case. The agent must pass all historical tests before any future release.

Read the full NextDimensionAI case study →

How to Choose: Decision Framework

Use this framework to match your needs with the right platform type.

By Monthly Call Volume

By Use Case Priority

Decision Flowchart

Answer these questions in order:

1. Are you testing voice agents or text-based LLMs?

• Text-based → Braintrust or Langfuse
• Voice agents → Continue to question 2

2. Do you have human agents or AI agents?

• Human agents → Observe.AI
• AI agents → Continue to question 3

3. Are you in development or production?

• Development/iteration → Platform-native tools (Vapi Test Suites, Retell Simulation) for quick feedback
• Pre-production/production → Voice-native platform (Hamming) for real phone testing

4. Do you need real phone call testing?

• Yes → Voice-native platform (Hamming)
• No → Reconsider. Transcript-only testing misses 40% of voice-specific failures.

5. What's your monthly call volume?

• under 1,000 → Start with manual + consider platform for growth
• 1,000 - 10,000 → Voice-native platform justified
• over 100,000 → Enterprise voice-native platform required

Pricing Comparison

Voice agent testing platform pricing varies significantly based on model and scale.

Pricing Models

Price Ranges (Estimated)

Note: Verify current pricing directly with vendors. Prices change and may vary by feature tier.

Note on platform-native pricing: While Vapi Test Suites, LiveKit Testing, and Retell Simulation appear "free," they're limited in scope. LiveKit is text-only (no audio testing). Vapi and Retell test transcripts but miss audio metrics. Teams needing production-grade QA (real phone testing, audio metrics, scale) typically add a dedicated testing platform.

Total Cost of Ownership

When comparing costs, factor in:

1. Direct costs: Platform subscription and usage fees
2. Engineering time: Integration, maintenance, custom development
3. Incident costs: Production issues missed by inadequate testing
4. Opportunity cost: Time spent on testing infrastructure vs. product development

Teams building custom solutions typically spend 2-4 engineering months upfront plus 10-20% ongoing maintenance. For most teams, a purpose-built platform is more cost-effective at 10K+ calls/month.

What to Ask in Vendor Evaluations

For a complete evaluation guide, see 12 Questions to Ask Before Choosing a Voice Agent Testing Platform.

Use these questions to evaluate voice agent testing platforms effectively. The follow-up questions reveal whether the capability is truly production-ready.

Technical Questions

1. Can you make real phone calls to test my voice agent?

   • Follow-up: PSTN, SIP, or WebRTC? Inbound, outbound, or both?
   • Red flag: "We test at the API level only"

2. How do you measure latency?

   • Follow-up: Component-level or end-to-end? Do you track P50, P95, and P99?
   • Red flag: "We measure average response time"

3. Can you simulate background noise?

   • Follow-up: At what SNR levels? What noise types (office, street, restaurant)?
   • Red flag: "We test in clean audio conditions only"

4. How do you handle interruptions and barge-in?

   • Follow-up: Is testing deterministic and repeatable?
   • Red flag: "We don't specifically test interruption handling"

5. What's your maximum concurrent call capacity?

   • Follow-up: Any performance degradation at scale?
   • Red flag: Capacity significantly below your production volume

Operational Questions

1. How does CI/CD integration work?

   • Follow-up: Can you block deployments on regression?
   • Red flag: "You'll need to build custom integration"

2. What alerting options are available?

   • Follow-up: Custom thresholds per metric? Escalation policies?
   • Red flag: No real-time alerting capability

3. What integrations exist for my voice platform?

   • Follow-up: Native integration or webhook-based?
   • Red flag: No integration with your specific platform

Commercial Questions

1. What's the pricing model?

   • Follow-up: How does pricing scale with volume?
   • Red flag: Pricing that scales poorly with your growth

2. Is there a free trial or POC option?

   • Follow-up: What's included? Can I test my actual scenarios?
   • Red flag: No trial without long sales cycle

3. What compliance certifications do you have?

   • Follow-up: SOC 2 Type I or Type II? HIPAA BAA available?
   • Red flag: No compliance certifications for enterprise requirements

Platform Selection Checklist

Use this checklist when evaluating voice agent testing platforms:

Must-Have Capabilities:

• Real phone call testing (PSTN or SIP)
• Latency measurement at P50, P95, P99 percentiles
• Word Error Rate (WER) tracking
• Multi-turn conversation testing (5+ turns)
• Integration with your voice platform
• CI/CD pipeline integration

Important Capabilities:

• Background noise simulation (configurable SNR)
• Interruption/barge-in testing
• Production call monitoring
• Automated alerting with custom thresholds
• Regression detection and deployment blocking
• DTMF and IVR navigation testing

Nice-to-Have Capabilities:

• Custom persona and accent simulation
• Multilingual testing support
• Historical trend analysis
• Custom evaluation metrics
• Full API access for automation
• SSO and enterprise authentication

Compliance and Security:

• SOC 2 Type II certified
• HIPAA BAA available (if healthcare)
• Data residency options
• Encryption at rest and in transit
• Audit logging

Evaluation Process:

• Request demo with your actual voice agent
• Run proof-of-concept with real scenarios (not just happy path)
• Validate scale requirements with concurrent test
• Check integration with your specific stack
• Review pricing at projected 12-month volume
• Confirm implementation timeline and support included

Flaws but Not Dealbreakers

Platform comparisons have inherent limitations:

Our analysis is biased. Hamming is one of the platforms in this comparison. We've tried to be direct about where each platform excels and where it falls short, but you should verify our claims about competitors directly with those vendors.

Capabilities change fast. Vapi, Retell, and LiveKit ship updates frequently. A limitation we documented in January 2025 may be fixed by the time you read this. Always verify current features with vendors.

There's a tension between depth and coverage. No single platform does everything perfectly. Specialized tools (Speechmatics for ASR, Observe.AI for human agents) may outperform general platforms in their specific domain. The question is whether you need one tool or many.

Custom builds aren't always wrong. For teams with highly specific requirements or existing telephony infrastructure, a custom solution may make sense. The 2-4 month investment is significant but can be justified if off-the-shelf platforms don't fit.

Start Testing Your Voice Agent

Choosing the right voice agent testing platform is critical for production success. General LLM testing tools miss 40% of voice-specific failures. Platform-native tools like Vapi Test Suites and Retell Simulation help during development but aren't enough for production QA. At scale, that gap costs you thousands of poor customer experiences every month.

The question isn't whether you need voice-specific testing. It's whether you catch these failures before or after your users do.

Ready to evaluate voice agent testing platforms?

Learn more about Hamming → | Book a demo →