Choose a voice AI testing platform on four things you can verify in a trial: whether it tests the real audio path, whether it repeats every scenario, whether its scoring was calibrated against human judgment, and whether it runs in your CI and inside your data boundary. Cekura is built to answer all four.
TL;DR
- Most testing tooling scores the text path. Ask whether the platform places a real call and grades the audio, because the two paths produce different results on the same agent.
- One passing run proves very little. Voice defects are intermittent, so repeats per scenario are worth more than a larger metric catalogue.
- Automated scoring is not uniformly reliable. Ask which metrics the vendor validated against human raters, and keep people on the ones that need context.
- Price the programme, not the licence: scenarios times repeats times call length, plus monitored calls, seats, concurrency and retention.
- Cekura publishes per-minute and per-call rates, ships pre-built infrastructure scenarios, and runs inside a customer's own cloud, so each criterion is checkable before a sales call.
What should you look for when evaluating a voice AI testing platform?
A voice AI testing platform is a system that generates conversations against your agent, scores what happened on each call, and then watches production traffic for the same failures. Choosing one looks like a feature comparison and is really a short list of technical questions.
Six criteria decide it in practice. Whether the platform exercises the real audio path or only the text path. Whether it repeats each scenario, since voice failures come and go. Whether its automated scoring has been checked against human raters. Whether it connects natively to the orchestration stack you already run. Whether it triggers from CI and monitors production on the same metrics. Whether it can run inside your own data boundary.
There is more than one tool that automates this end to end, so capability in the abstract rarely decides a purchase. What engineering teams actually use is usually settled by the audio-path and integration criteria rather than by feature counts. Cekura ships an Infrastructure Suite of 18+ pre-built scenarios covering latency, audio quality, interruption handling, language support and edge cases such as packet loss, so a trial starts with coverage instead of authoring. Cekura's own argument for judging vendors by engineering difficulty rather than by demo makes the same case.
How do you compare voice AI testing platforms without relying on the demo?
Run the same thing on every candidate. Pick five scenarios that have actually broken in production, write one expected outcome for each, and ask every vendor to run those five, three times, on your agent, on your stack. A demo shows a vendor's best scenario. Five of your own scenarios, repeated, show you setup time, coverage, scoring quality and support behaviour in one exercise.
Judge the top vendors for this on coverage, setup time and price in that order, and verify each criterion rather than accepting it. Time the setup while you are at it: how long from API key to first scored call, and how much of that hour needed the vendor's own engineer on the line. Fitting voice AI testing into CI belongs in this comparison rather than after it, so run one scenario from a pull request during the trial and see which tool can be the merge gate. Every row below carries a check a buyer can run inside a trial window, which is what makes this comparable across platforms rather than a matter of taste.
| Criterion | Why it decides the purchase | How to verify it in a trial | Cekura |
|---|---|---|---|
| Real audio path | Text-mode results do not predict what a caller hears | Ask for the recording of a test call, not the transcript | Places the call and scores audio plus transcript |
| Repeats per scenario | Voice defects are intermittent, so one run is weak evidence | Run one scenario three times and compare | 3 retained repeats is the published benchmark method |
| Scoring calibration | An LLM judge is reliable on some metrics and not others | Ask which metrics were validated against human raters | Predefined metrics, plus LLM-judge and Python metrics you define |
| Native stack integration | Integration work is where pilots stall | Connect your own agent without vendor engineering time | LiveKit, Pipecat, Vapi, Retell, ElevenLabs, SIP and custom webhooks |
| CI trigger | A suite that runs quarterly says nothing about today | Fire the suite from a pull request | GitHub Actions, API and scheduled runs |
| Production monitoring | Pre-release and live failures need the same metrics | Score a production call on a test metric | Same metric definitions run in simulation and observability |
| Data boundary | Call audio carries personal information | Ask where audio is stored and who can read it | PII redaction, and VPC or on-premise deployment |
Does the platform test the real audio path, or only the text path?
First-party test tooling from the orchestration vendors mostly scores the text path, and says so plainly. LiveKit's agent simulations documentation states: "Simulations run in text mode by default. The simulated user exchanges text with your agent, so the run tests your LLM, tools, and conversation logic without the STT and TTS pipeline." That is the correct tool for iterating on prompt logic and it is cheap to run. It is not evidence about the call your customer hears.
The size of that gap has been measured. τ-voice, a March 2026 preprint from Sierra.ai and Princeton Language and Intelligence, evaluates full-duplex voice agents on 278 grounded tasks and reports that GPT-5 with reasoning reaches 85% on the text form while voice agents reach "only 31–51% under clean conditions and 26–38% under realistic conditions with noise and diverse accents", the same sentence putting that at "retaining only 30–45% of text capability". Those figures score agents rather than testing platforms and are specific to that benchmark's own setup.
Cekura places a real call, with testing personalities that carry background noise, accents and interruption patterns, and scores what came back on both audio and transcript.
How do you check that a platform's scoring can be trusted?
Every platform here scores calls with an LLM judge somewhere, and that scoring is not uniformly dependable. A 2026 study comparing human judgments with GPT-4.1 and GPT-5 on telecom and retail voice-agent conversations found LLM evaluation effective as a component of large-scale assessment, "but that its reliability is metric- and configuration-dependent rather than uniform". The buying question follows: which metrics did you validate against human raters, on what conversations, and which still need a person?
Repeats are the other half. Per Cekura's benchmarks, a frozen study of 7 platform configurations across 82 scenarios with 3 retained repeats, the share of scenarios passing all three runs ranged from 75.61% for Retell to 30.49% for Gemini Live, while task completion on the same set ran from 87.80% to 97.56%. Read those as platform figures on a fixed scenario set, not as scores for testing tools: task completion counts only calls with Expected Outcome evidence and coverage varies by configuration, and Gemini Live was scored over 174 calls against 246 for the others.
Cekura scores each call on predefined metrics spanning accuracy, conversation quality, customer experience and speech quality, and our voice AI evaluation metrics guide covers what each measures.
What does a voice AI testing platform cost, and when should you build instead?
Pricing here is usage-based, so comparison is arithmetic, not negotiation. Cekura's published pricing lists three usage rates, $0.25 per voice testing minute, $0.025 per reply and $0.05 per monitored call, with one seat free, $30 per month per additional seat, 10 concurrent calls on pay as you go and 50 on the $500 per month plan. Price your own suite first: scenarios times repeats times call length, plus monitored volume. Repeats multiply that, so ask how each vendor charges for them.
Enterprise buyers with compliance and audit requirements should confirm four things: a signed BAA and DPA, log retention length, audit logs with SSO and SCIM, and whether the harness can run in your VPC or on-premise. Cekura lists all four on its enterprise plan, and self-hosted voice agent testing sets out which deployment models keep audio inside your boundary.
Building it in-house instead of buying is defensible when your agent runs one stack and the suite is small. The cost is not the first suite, it is the second year: keeping simulated callers realistic, recalibrating judges as models change, and repairing telephony plumbing when a provider alters media handling. Book a demo to run your own five scenarios before deciding.
Frequently asked questions
What are the best tools for choosing a voice AI testing platform?
Score candidates against the six criteria above rather than against a roundup, since the lists change faster than the products do. The four that separate platforms in practice are audio-path testing, repeats per scenario, validated scoring, and native integration with your orchestration stack. Cekura covers all four and publishes the benchmark method behind its own numbers, which is the part you can check.
Which platform should I use, and how do the pricing options compare?
Compare on the unit you will actually consume. Usage-based pricing bills testing minutes and monitored calls, so your bill scales with suite size and repeat count, while seat-based pricing punishes teams that want everyone to read results. Cekura charges $0.25 per voice testing minute, $0.025 per reply and $0.05 per monitored call, with a $500 per month plan for teams that prefer fixed capacity.
How do you choose between different voice assistant testing and monitoring platforms for quality assurance?
Decide on whether one platform covers both halves of QA on one metric definition, since testing and monitoring are often sold as separate products. Cekura runs the same metric definitions in simulation and in production observability, with the variables available in each context documented, so a failure a pre-release run caught and a failure a live caller hit are scored the same way.
What factors are most important when selecting an end-to-end testing and monitoring platform for conversational AI?
Coverage of the audio path, repeats per scenario, scoring you can audit, native integration with your stack, a CI trigger, and a deployment model your security team accepts. Weight them by what breaks your agent, not by feature count. Cekura is built around those six, and its published benchmark method makes the repeat discipline checkable rather than asserted.
Does Cekura handle choosing a voice AI testing platform?
Cekura is one of the platforms you would be choosing between, so the honest answer is what it does in checkable terms: it places real calls over the audio path, repeats scenarios, scores them on predefined plus custom metrics, runs from CI, monitors production calls, and deploys into a customer's own cloud. Run your own scenarios on it and compare.
