Coval vs Cekura: Voice AI Testing Compared (2026)

Information in this comparison reflects publicly available data as of March 2026. Features and capabilities may have changed since publication.

Key Takeaways

• Coval vs Cekura is a choice between evaluation depth and self-serve accessibility. Coval provides stateful workflow testing, human review queues, and full compliance on every plan. Cekura offers a $30/month developer tier with self-serve signup.

• Coval's compliance credentials (SOC 2, HIPAA, GDPR) are available on all plans. Cekura gates compliance certifications to its Enterprise tier, with limited public documentation on certification status.

• Cekura's credit-based pricing appears affordable at $30/month, but voice testing consumes 5 credits per minute -- a rate not prominently displayed on the pricing page. The Developer plan covers approximately 150 minutes of voice testing monthly.

• Both platforms support voice and chat agents. Coval adds stateful testing and human review. Cekura adds Conditional Actions (rule-based testing to reduce LLM flakiness) and an MCP server for IDE integration.

• An independent academic study, "Testing the Testers" (arxiv, 2026), evaluated multiple voice AI evaluation platforms and scored Coval at 48.9 and Cekura at 43.0 on their evaluation accuracy benchmark. Coval's evaluation methodology comes from Waymo autonomous vehicle testing. Cekura's founders bring quantitative finance (HFT) and Google NLP research backgrounds.

Table of Contents

1. What Is Coval?

2. What Is Cekura?

3. Feature Comparison

4. Stateful Testing vs Conditional Actions

5. Human Review and Metric Calibration

6. Pricing and Cost Transparency

7. Compliance and Certification Status

8. Developer Experience

9. Who Should Choose Coval?

10. Who Should Choose Cekura?

11. FAQ

12. Conclusion

What Is Coval?

Coval is a voice AI evaluation platform that provides pre-production simulation, stateful workflow testing, human review queues, and agent-native tooling for teams deploying conversational agents in production. Built on autonomous vehicle safety testing methodology from Waymo, Coval ships SOC 2 Type II, HIPAA, and GDPR compliance on all plans.

Customers span household names in enterprise contact centers, fintech and financial services, healthcare, and AI-native companies. A leading ticketing provider went from manually auditing 50-100 calls per release to running automated voice agent simulations across their full scenario library.

What Is Cekura?

Cekura (formerly Vocera, legally Tatva Labs Inc.) is a YC F24-backed automated QA and observability platform for voice and chat AI agents. Founded in 2024 by three IIT Bombay co-founders with backgrounds in quantitative trading, Google NLP research, and enterprise consulting. As of Q1 2026, Cekura has raised $2.4M in seed funding according to publicly available funding records.

Cekura positions itself as the "reliability layer for conversational AI," with a credit-based self-serve pricing model starting at $30/month, a Conditional Actions rule-based testing engine, 2,000+ concurrent simulations, and an MCP server for IDE integration. Named customers include Twin Health, Confido Health, Lindy, and Mindtickle.

Feature Comparison Table

Stateful Testing vs Conditional Actions

Stateful workflow testing: An evaluation method that sets external pre-conditions before a simulation, runs the conversation, then verifies post-call outcomes via API calls to confirm the agent completed its task.

Conditional Actions: A rule-based testing engine that dynamically adapts test agent behavior based on the target agent's responses, reducing non-determinism in LLM-as-judge evaluation scoring.

Coval and Cekura solve different evaluation problems, and understanding the distinction matters for choosing the right platform.

Coval's stateful workflow testing evaluates whether an agent follows expected behaviors AND accomplishes its task. You can set external states before a simulation (create a user account, configure a setting) and run against configurable personas with distinct voices, accents, behaviors, and background noise, run the conversation, then verify the outcome with follow-up API calls. Did the agent book the appointment? Did the account status change? Did the payment process? This is workflow-level evaluation that catches the failures customers actually experience.

Teams evaluating both platforms consistently identify workflow adherence as the capability that sets Coval apart. A CTO at a scaling AI company noted after head-to-head evaluation: "Workflow adherence was pretty unique -- we had not seen it in other players."

Cekura's Conditional Actions solves a different problem: LLM evaluation flakiness. When you use an LLM to judge another LLM's output, results vary between runs. Conditional Actions adds a rule-based layer that dynamically adapts test agent behavior based on the target agent's responses, reducing non-determinism in simulations.

Both features are valuable. Stateful testing answers "did my agent do the right thing?" Conditional Actions answers "can I trust the simulation to follow instructions?" The question is which problem is more urgent for your team.

Human Review and Metric Calibration

LLM-as-judge: An evaluation approach where a large language model scores another AI agent's outputs, enabling automated quality assessment at scale but introducing scoring variance between runs.

Automated evaluation metrics only matter if they reflect reality. The challenge with LLM-as-judge scoring at scale is knowing when to trust the scores and when they diverge from human judgment.

Coval's human review queues let your team label conversations, compare those labels against automated scores, and build agreement rates that quantify metric reliability. Auto-add rules automatically route edge cases and anomalies to human reviewers without manual triage. For compliance teams, safety teams, and QA organizations, this produces documented evidence that automated evaluation is calibrated and trustworthy.

Cekura does not offer human review functionality. Their evaluation stack uses LLM-as-judge augmented by Conditional Actions for determinism. This approach works for teams comfortable relying on automated scoring. It creates a gap for organizations that need to demonstrate evaluation accuracy to external auditors, regulated-industry procurement teams, or institutional partners.

A Voice Agent Lead at a leading ticketing company described the impact of Coval's evaluation infrastructure: "I don't know how I did things before this. That was like the 10x improvement."

Pricing and Cost Transparency

Pricing structure matters because it determines how teams plan budgets and build business cases for evaluation tooling.

Cekura leads with a published $30/month Developer plan as of Q1 2026. The headline number is transparent, but the credit consumption model introduces complexity. Voice testing costs 5 credits per minute, chat messages cost 0.5 credits each, and metric evaluation costs 0.2 credits per run. At the Developer tier's 750 credits, you get approximately 150 minutes of voice testing per month with no metric evaluation. More realistically, at an average 4 min / call and a handful of metrics, this pricing allows for 20-30 calls / month. The 5-credits-per-minute rate is not prominently displayed on the main pricing page, which creates friction for accurate cost estimation.

Additional seats cost $30/user/month. The Enterprise tier (custom pricing) is required for SOC 2, HIPAA, GDPR compliance, self-hosting, custom concurrent calls, white-label reports, and load testing. Cekura also offers a 7-day trial with 300 credits and no credit card required.

Coval does not currently publish self-serve pricing and requires a sales conversation for cost estimates. However, the CLI and API are fully self-serve for creating test sets, personas, metrics, and running evaluations once onboarded. Self-serve pricing is launching in Q2 2026. All Coval plans include unlimited user seats because cross-functional collaboration is the cornerstone of voice AI implementations.

The tradeoff: Cekura lets you start today without talking to anyone. Coval requires a conversation but delivers pricing and access aligned to effective evaluation volume and team collaboration requirements.

Compliance and Certification Status

For teams deploying voice agents in healthcare, financial services, or global enterprise contexts, compliance certifications are not optional. According to the AICPA SOC 2 framework, these certifications determine whether a vendor passes procurement review.

Coval ships SOC 2 Type II, HIPAA, and GDPR across all plans. Documentation is available and current. There is no tier-gating on compliance -- every Coval customer gets the same certification coverage.

As of Q1 2026, Cekura lists SOC 2, HIPAA, and GDPR on its Enterprise tier. However, certification dates are not publicly visible. It is unclear from Cekura's public materials whether these certifications are completed or in progress. The Developer plan does not include compliance certifications. For regulated-industry procurement, the distinction between "we have it" and "it's on the Enterprise tier but we can't show you a cert date" is material.

If your procurement team needs to verify a vendor's compliance status with specific certification dates and documentation, this is a practical consideration when comparing the two platforms.

Developer Experience

Both platforms target engineering teams, but the integration surfaces differ.

Coval provides an agent-native development environment with CLI for terminal-based evaluation scripting, a REST API, MCP integration and skills for AI coding assistants. In addition, there are CI/CD integration via Git Actions and a GUI dashboard. Engineers can script evals from the terminal, have Cursor or Claude Code launch runs without leaving context, and gate deployments on pass rates. The CLI is a meaningful differentiator for teams with terminal-first workflows. Scheduled recurring evaluations enable automated regression testing on a cadence -- daily, weekly, or per-release -- without manual triggers.

Cekura provides a REST API, an MCP server (Claude Code, Cursor integration), GitHub Actions for CI/CD, and a web dashboard. Cekura was the first voice eval platform to ship an MCP server, which is a forward-looking integration for IDE-native workflows. However, Cekura does not offer a CLI or skills, which means terminal-native scripting requires going through the API directly.

Both platforms offer GitHub Actions for deployment gating. Coval adds the CLI and skills layer. For most engineering teams, the practical difference comes down to whether your developers prefer terminal scripting (Coval's CLI advantage) or IDE-native integration (both support MCP).

Who Should Choose Coval?

Choose Coval if your team needs:

• Stateful workflow testing to verify that agents actually accomplish their tasks, not just respond correctly

• Human review queues for metric calibration, compliance documentation, and discovering failure modes automation misses

• Compliance on every plan -- SOC 2 Type II, HIPAA, and GDPR without tier-gating

• agent-native evaluation with self-serve test creation via CLI and API, plus white-glove onboarding for teams that want customized setup

• White-glove test set creation with customized onboarding rather than auto-generated scenarios

Coval's customers operate in contexts where evaluation results need to survive external scrutiny: an enterprise telecommunications QA team runs daily regression tests across enterprise customer agents. A customer success titan runs simulation load tests before releases. A household name financial services provider verifies backend state changes after every simulated call.

Who Should Choose Cekura?

Choose Cekura if your team needs:

• Self-serve access today without a sales conversation -- the $30/month Developer plan lets you start testing immediately

• Conditional Actions for reducing LLM evaluation flakiness with rule-based testing

• SMS testing alongside voice and chat evaluation

• Budget-constrained exploration where seeing a public price before engaging with sales is a requirement

Cekura serves developer-first teams and voice AI startups building on Vapi, Retell, and LiveKit. Confido Health used Cekura's simulation to stress-test 30+ service workflows during a calling infrastructure migration, scanning every node and edge to save days of manual QA.

Frequently Asked Questions

Does Cekura support stateful workflow testing?

No. As of March 2026, Cekura's testing is state-blind. It simulates calls and scores responses but cannot set pre-conditions or verify post-call state changes. Coval supports setting states before simulation and checking outcomes after, enabling end-to-end workflow validation.

What does Cekura's $30/month actually cover?

The Developer plan includes 750 credits and one seat. Voice testing costs 5 credits per minute, providing approximately 150 minutes of monthly voice testing. Chat messages cost 0.5 credits each, and metric evaluations cost 0.2 credits per run. The plan also includes 10 concurrent calls and one project. Practically, with average 4 minute calls and 4-6 metrics per call, that's ~30 calls or $1/call. Credits above 750 incur pay-as-you-go overages.

Does Cekura have SOC 2 Type II certification?

Cekura lists SOC 2 on its Enterprise tier but has not published a certification date or completion status. Coval's SOC 2 Type II certification is confirmed and available across all plans. For procurement teams that need verifiable certification, this is worth confirming directly with Cekura.

Can I migrate from Cekura to Coval?

Yes. Both platforms use API-based agent integration. Your test scenarios and evaluation criteria would need to be re-created, but there is no vendor lock-in at the agent connection level. Coval's onboarding includes white-glove test set creation, so the migration path includes building evaluation suites customized to your workflows.

Which platform has better concurrent simulation scale?

Cekura markets 2,000+ concurrent simulations. Coval runs thousands of simulations per run. Both platforms support high-concurrency testing. The more relevant comparison is what those simulations test -- isolated call scoring versus end-to-end workflow verification.

Conclusion

Coval and Cekura serve different buying contexts. Coval provides deeper evaluation capabilities -- stateful workflow testing, human review for metric calibration, and compliance certifications on every plan -- for teams where evaluation results need to survive external scrutiny. Cekura provides a lower-friction entry point with self-serve pricing, Conditional Actions for evaluation determinism, and MCP-native IDE integration for developer-first teams.

If your primary need is getting started quickly with voice agent testing at minimal cost, Cekura's Developer plan removes the sales conversation barrier. If your evaluation needs extend to workflow verification, compliance documentation, and human-calibrated metrics, Coval is built for that depth.

Also read:

• Coval vs Hamming voice AI evaluation comparison

• Hamming vs Cekura voice AI QA comparison

• Coval vs Bluejay voice AI eval comparison

• Hamming vs Bluejay voice AI testing comparison

• Cekura vs Bluejay voice AI QA comparison