7 Best Cekura Alternatives for Voice and Chat Agent Testing in 2026

What Are the 7 Best Cekura Alternatives?

Each entry covers what the tool tests, how it compares with Cekura on the points above, who it fits, and the limitation to check before you switch.

1. Hamming

Hamming describes itself as the trust platform for voice and chat agents, and its scope lines up closely with Cekura's: test before launch, red-team what could go wrong, and monitor every production call. It imports agents from Vapi, Retell, or ElevenLabs, dials SIP numbers, and connects to LiveKit or Pipecat for direct WebRTC.

Where it pulls ahead of Cekura's self-serve plans is scale and deployment, since it runs 50K+ concurrent test calls wired into CI/CD and offers single-tenant deployments in your preferred region, with US, EU, and UK data residency.

• Scenario generation - scenarios generated from your prompt and scored by 50+ built-in metrics.
• Realistic conditions - accents, interruptions, background noise, DTMF, and IVR trees.
• Production loop - real-time monitoring and one-click conversion of a production failure into a regression test.

Best for: teams outgrowing Cekura's concurrency limits that need enterprise deployment options.

Limitation: every plan on its pricing page is marked contact us, and SOC 2 and HIPAA compliance are listed under its Enterprise plan.

2. TestMu AI Agent Testing (Formerly LambdaTest)

TestMu AI Agent Testing uses autonomous evaluators to run test conversations and phone calls against chat and voice agents, inbound and outbound phone agents, video agents, and image analyzers, which suits teams whose agent portfolio is wider than voice. Chat is a first-class surface here, scored on the same 9 quality metrics as voice: hallucination detection, bias detection, completeness, context awareness, response quality, conversation flow, tone consistency, positive user outcome, and root-cause understanding.

Each run ends in a Green, Yellow, or Red production-readiness verdict, so a release decision doesn't depend on reading every transcript, and the evidence behind each criterion stays attached to the result.

The sample view above shows how each criterion carries an expected behavior, the result, a confidence level, and timestamped evidence from the call, which is what lets a reviewer trust a pass without replaying the recording.

• Scenario generation - 60 to 100+ scenarios from uploaded docs and PRDs, or from connected Jira and Confluence.
• Production recordings - upload real call recordings for batch analysis with the same metrics.
• Scheduling and CI - preset or custom cron schedules with IANA timezones, and JUnit XML output for GitHub Actions, GitLab CI, Jenkins, and CircleCI.

Best for: teams testing chat, voice, and phone agents together who want one verdict per release.

Limitation: beyond the free start, plans are custom-quoted to your evaluation volume, so there is no public per-minute rate to compare against Cekura's, and its real-time monitoring of live calls is less central than Cekura's drift detection, since production coverage comes through uploaded recordings and scheduled runs. The Agent Testing CLI docs cover the CI setup.

Note: Score chat and voice agents on 9 quality metrics and get a readiness verdict before every release. Try TestMu AI Agent Testing

3. Coval

Coval focuses on helping teams scale voice AI agents with confidence, and its homepage centers on repeatable regression testing across prompt changes, model updates, and vendor swaps. Its documentation adds text-based simulations for chat agents alongside voice simulations, with personas that set the simulated user's voice, accent, and behavior.

In production, Coval runs evals on live conversations and routes high-stakes, failed, or low-confidence calls to human QA reviewers, which adds a human check that pure automated scoring lacks.

• Release gates - voice AI evals positioned as a quality gate for every release.
• Stack fit - built to ship on LiveKit, Pipecat, or your own stack.
• Compliance - SOC 2 Type II and HIPAA, with GDPR in progress.

Best for: voice-first teams that change models or speech vendors often and want human review on risky calls.

Limitation: private VPC deployment, data residency, SAML SSO, and SCIM are Enterprise-only, much like Cekura's plans.

4. Bluejay

Bluejay pitches itself as a way to test, monitor, and improve voice and chat AI agents, running lifelike Digital Humans across voice, chat, and text to simulate real conversations. Its pricing is public across pay-as-you-go, Growth, and Scale tiers, with Enterprise on request, and SOC 2 Type II is listed on every plan.

Load testing is where the tiers matter, since its Scale plan includes up to 200 concurrent simulations and load testing, and Enterprise makes the concurrency and load limits customizable.

• Simulation - Digital Humans across voice, chat, and text channels.
• Regression - catches regressions before launch.
• Observability - actionable observability of production behavior.

Best for: teams that want simulated users across several channels with visible pricing from day one.

Limitation: its homepage gives few technical specifics, and SSO, SAML, and SCIM are Enterprise-only, so confirm red-teaming and integration details during evaluation.

5. Roark

Roark helps teams test, monitor, and improve the voice AI agents they already run, and it treats audio as the core signal with 500+ audio-native metrics. Hundreds of simulated callers cover the angry one, the rambler, and the interrupter, and adversarial callers attempt prompt injection, jailbreaks, and social engineering.

It integrates with Vapi, Bland, Retell, LiveKit, Pipecat, ElevenLabs, Kore.ai, and Google, works for voice calls and chat threads alike, and scores every production call as it lands.

• CI gating - every prompt or model change runs the suite before it merges.
• Load and health - peak-volume concurrency and always-on health checks.
• Compliance on every plan - SOC 2 Type II and a HIPAA BAA are available on every plan, including pay-as-you-go.

Best for: voice-first teams that want deep audio metrics and compliance without an enterprise contract.

Limitation: its pricing page doesn't mention VPC or self-hosted deployment, and SSO with SAML is Enterprise-only.

6. LangWatch Scenario

LangWatch Scenario is an open-source agent testing framework under the Apache License 2.0, available in Python, TypeScript, and Go, and it lets you write simulated-user tests as code that run inside pytest or vitest. Its README treats voice as a first-class citizen, using the same entrypoint, script format, and judge for voice as for chat.

It ships a red-team agent that drops in as a replacement for the user simulator and runs multi-turn adversarial attacks through the same CI pipeline, which gives open-source teams the security testing that Cekura reserves for higher plans.

• Shipped voice adapters - ElevenLabs, OpenAI Realtime, Twilio Media Streams, Pipecat WebSocket, and Gemini Live.
• Same loop for attacks - red-team runs use the same scenario runner and CI setup as functional tests.
• Data control - tests run in your own environment rather than a hosted service.

Best for: engineering teams that want agent tests versioned with their code and no SaaS dependency.

Limitation: it is code-first, and LiveKit, Vapi, and generic WebRTC adapters are still listed as follow-up work.

7. Native Platform Testing (Vapi, Retell, and ElevenLabs)

If your agent lives on one voice platform, its built-in testing is the cheapest baseline to try before adding a vendor. Vapi Evals check responses with exact match, regex, or AI-powered judging and verify function calls with specific arguments, Retell's simulation testing has an AI play the caller and grades each run with a written explanation, and ElevenLabs Agent Testing runs simulation, next-reply, and tool-call tests from the dashboard, CLI, or API.

The trade-off is depth on voice itself, since Vapi says its Evals run at the text and model layer and don't test speech recognition, audio quality, or turn-taking, and Retell's simulation runs as a text conversation.

• Vapi - Evals and Simulations for assistants and squads built on Vapi, covered in the guide to testing a Vapi voice agent.
• Retell - text simulation with batch runs over the API so you can gate a deploy from CI, though agents using a custom LLM aren't supported.
• ElevenLabs - tests created and run per agent, with examples that target a specific agent ID.

Best for: single-platform teams that want a first layer of scenario and tool-call tests at no extra vendor cost.

Limitation: each one only tests agents on its own platform, so a multi-platform stack or a migration between platforms needs an independent tool.