Cekura
Automated QA, monitoring, and self-improvement for voice and chat AI agents.
Cekura is the most end-to-end voice AI testing and monitoring platform we've seen, with unique self-improving loops, vendor benchmarking, and enterprise compliance baked in. It's ideal for teams that treat agent quality as a CI/CD concern—QA engineers and product teams shipping production voice agents. The pay-as-you-go pricing is flexible, but heavy usage can add up. Start with the free tier to validate before scaling. Compared to fragmented tools like individual stress-testers or generic LLM observability platforms, Cekura's all-in-one workflow saves time and reduces integration risk.
Verified 7d ago · liveness 83/100 · cite: rightaichoice.com/tools/cekura
- Voice AI startups and scale-ups building conversational agents
- QA engineers testing voice and chat bots
- Product teams shipping customer-facing voice agents
- AI automation agencies needing reliable agent testing
- Non-technical users looking for no-code chatbot builders
- Teams building simple FAQ chatbots
- Users needing extensive sentiment analysis beyond voice-specific metrics
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Skip Cekura if you're building a simple FAQ chatbot or need a no-code builder—it's built for technical teams running serious voice agent QA at scale.
Going past the free first seat costs $30/mo per additional seat, which adds up for larger teams.
Cekura's pay-as-you-go pricing is a good fit for early-stage teams with modest usage—the free tier offers 300 credits (~60 min) to test the waters. The $500/mo Startup plan beats comparable platforms that charge per-seat for QA tools. For heavy enterprise use, volume discounts and custom plans make it competitive, though smaller teams may find the per-minute costs add up.
In short
Cekura — Automated QA, monitoring, and self-improvement for voice and chat AI agents. Best for Voice AI startups and scale-ups building conversational agents, QA engineers testing voice and chat bots, Product teams shipping customer-facing voice agents. Free to start; paid plans from $500/mo.
What's new in Cekura
Checked 2 days agoAcross the latest 8 updates: 6 feature updates and 2 changelog entries.
Other August Improvements
Editable call metadata, custom metadata filters, BYO audio in tests, broader provider coverage, simpler setup, billing overage support.
Self-Improving Loops for Cloud Providers
Point Cekura at failing run; it diagnoses, fixes, re-tests until pass; apply with one click.
Tests as Code
Define tests in cekura.tests.json, run via curl or CI, with dry_run to validate and estimate cost.
Cekura August Week 2 Product Updates
Tests as Code, self-improving loops for cloud providers, editable call metadata, new integrations, onboarding improvements.
Other June Improvements
Per-agent webhooks, results page revamp with distinct workflow issue highlighting, performance metrics, and scoring rubric.
OpenTelemetry Tracing
Deep visibility into voice agent execution: LLM, TTS, STT, tool calls as spans with timing and token usage.
Insights
Automated clustering of failing LLM-judge metrics into root-cause themes; on-demand audits via API.
Cekura June Week 2 Product Updates
Insights, OpenTelemetry Tracing, per-agent webhooks, results page revamp.
What people actually say about Cekura — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
24 mentions across 3 sources (Hacker News, Product Hunt, Lemmy) · researched Jul 3, 2026.
- +Combines pre-production simulation and production monitoring in one platform.
- +Voice-specific metrics like empathy, hallucinations, and interruption detection provide deep insights.
- +Integrates with major voice agent frameworks (Vapi, Retell, ElevenLabs, etc.).
- +Full-session evaluations help catch regressions that single-turn tests miss.
- +Real-time alerts and conversation replay for production call debugging.
- −Lack of long-term independent reviews to validate reliability claims.
- −Pricing details are unclear beyond a vague 'freemium' label.
- −Some users question contextual reliability for domain-specific conversations.
- −No publicly available pricing tiers or usage limits documented.
- −Newer platform; may lack maturity in handling edge cases at scale.
- • Overages for high-volume simulation runs
- • Potential per-seat costs for team accounts
Viability Score
How well maintained and how widely used is Cekura? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Parallel call simulation with thousands of scenarios
- Voice-specific quality metrics: empathy, responsiveness, hallucinations
- Real-time production call monitoring and alerting
- Conversation replay for debugging regressions
- Customizable LLM judges with versioning
- Optimize Agent: suggests prompt fixes from evaluation results
- OpenTelemetry tracing for LLM, TTS, STT, and tool calls
- Insights: automated root-cause analysis of failing metrics
- Tests as Code (cekura.tests.json) for CI/CD integration
- Cron jobs for scheduled recurring testing runs
- Benchmarking across vendors (Vapi, Retell, ElevenLabs, Pipecat, LiveKit)
- Adversarial red-teaming (jailbreaks, off-script, PII leaks)
- CLI and Python SDK for programmatic access
- MCP server with OAuth for AI assistant integration
- Selective call export and customizable call table columns
About Cekura
Cekura is a unified platform for testing, monitoring, and improving conversational AI agents, specifically built for teams shipping voice and chat experiences. It lets you run thousands of simulated conversations before you go live—testing with diverse personas, accents, emotions, and interruptive behaviors to catch regressions early. In production, Cekura monitors every call for voice-specific quality signals like empathy, responsiveness, gibberish, and hallucinations, and alerts you in real time. The platform is designed to close the loop between testing and improvement. Its self-improving agent workflow detects failures in production, reproduces them in simulation, suggests prompt fixes, and re-runs the tests to verify—so you can apply the fix with confidence. You can also benchmark your agent against multiple vendors (Vapi, Retell, ElevenLabs, Pipecat, LiveKit) using the same scenarios and scoring, making it easy to pick the best-performing stack. Cekura shines for QA engineers, product teams, and AI automation agencies that need to ensure reliability at scale. It offers enterprise-grade compliance (SOC 2, HIPAA, GDPR), with BAA/DPA support, self-hosting, and VPC deployment. Pricing is usage-based with a free tier, making it accessible for early-stage teams while scaling to enterprise needs. Recent additions include Tests as Code (cekura.tests.json), OpenTelemetry tracing, automated root-cause analysis via Insights, and an Optimize Agent feature that suggests prompt fixes. These features make Cekura a comprehensive choice for teams serious about voice agent reliability.
Behind the Verdict
Cekura stands out by addressing the full lifecycle of voice agent reliability. Pre-production simulation lets you run thousands of scenarios with realistic conditions like background noise, accents, and interruptions—far more than a quick smoke test. The production monitoring adds live drift detection and alerts, so you catch regressions before customers notice. The self-improving loop is a differentiator: it doesn't just flag issues; it reproduces them in simulation, suggests prompt patches, and re-runs the gate to verify. That's a real time-saver for teams iterating on prompts. We're impressed by the depth of observability. OpenTelemetry tracing captures every LLM call, TTS, STT, and tool invocation with timing and token usage, making it easy to pinpoint bottlenecks. Insights clusters failing calls into root-cause themes, turning raw logs into a short actionable list. The vendor benchmarking is another strong point—you can run the same test suite across Vapi, Retell, ElevenLabs, Pipecat, and LiveKit to make data-driven stack decisions. Pricing is usage-based, with a free tier that gives you 300 free credits (about 60 minutes of testing). The pay-as-you-go rate of $0.25 per voice testing minute and $0.05 per monitored call is reasonable for moderate usage but can scale up with heavy QA. The $500/month Startup plan offers predictable capacity with ~2,000 minutes of testing and 10,000 monitored calls—good for teams hitting their stride. Enterprise plans are custom, with volume discounts, VPC/on-prem, and SSO. Where Cekura fits: voice-first startups and scale-ups, QA engineers who want CI/CD-style testing, and agencies managing multiple client agents. It's not for simple FAQ chatbots or non-technical teams—the platform assumes you're comfortable with APIs, CLI, and test specs. Also consider that chat testing is mentioned but voice is clearly the focus.
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Real-world workflow fit
Concrete scenarios for the personas Cekura actually fits — and what changes day-one when you adopt it.
Set up regression tests after a prompt change
Outcome: Within a day, you create a cekura.tests.json spec, run it from CI, and catch a regression in the cancellation flow before it reaches customers.
Monitor live call quality and get alerts for HIPAA violations
Outcome: Cekura monitors every call for compliance signals and sends real-time alerts when a call drifts, so your team can intervene before a violation escalates.
Benchmark vendors for a new client deployment
Outcome: You run the same test suite across Vapi and Retell, compare metrics like latency and interruption handling, and choose the best performer with data to back the decision.
Use Cases
- Test a voice agent's response to angry or interruptive customers using predefined personalities
- Replay a problematic production conversation to catch regressions after a prompt change
- Monitor real-time call quality metrics and get Slack alerts when hallucinations spike
- Automate weekly regression testing of appointment cancellation flows via cron jobs
- Optimize agent prompts by running evaluations and applying suggested improvements from Cekura
- Benchmark Vapi vs. Retell vs. ElevenLabs to pick the most reliable voice stack
- Run adversarial red-team tests to probe for jailbreaks, leaks, and off-script behavior
- Export PDF reports of test results to share with stakeholders before a production launch
Limitations
- Cekura offers pay-as-you-go pricing at $0.25 per voice testing minute and $0.05 per monitored call, with a free first user.
- The Startup plan costs $500/month and includes approximately 2,000 minutes of voice testing and 10,000 monitored calls.
- Enterprise plans are custom with volume discounts and custom concurrency, seats, and retention.
- The platform focuses on voice and chat AI agents, with chat testing mentioned in documentation.
as of 2026-08-16
Verification history
We have re-verified Cekura 4 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Cekura tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Pay as you go
$0/mo (first user free) + usage
Ideal for
Solo developers and small teams building their first AI agents, wanting to test without upfront commitment.
What this tier adds
Starting tier with free first user, pay-per-use rates, and 30-day log retention.
Startup
$500/mo
Ideal for
Voice AI teams ready to scale with predictable monthly capacity, needing BAA/DPA and dedicated support.
What this tier adds
Adds predictable volume (2,000 min, 10,000 calls), 50 concurrency, 10 seats, BAA/DPA, and Slack support.
Enterprise
Custom
Ideal for
Large organizations with compliance and scale needs, requiring custom infrastructure and hands-on support.
What this tier adds
Adds volume discounts, custom concurrency/support, VPC hosting, SSO/SCIM, and audit logs.
Where the pricing makes sense
The company stage and team size where Cekura's pricing actually pencils out — and where peers do it cheaper.
Cekura's pay-as-you-go pricing is a good fit for early-stage teams with modest usage—the free tier offers 300 credits (~60 min) to test the waters. The $500/mo Startup plan beats comparable platforms that charge per-seat for QA tools. For heavy enterprise use, volume discounts and custom plans make it competitive, though smaller teams may find the per-minute costs add up.
Setup time & first value
How long it actually takes to get something useful out of Cekura — broken out by persona, not the marketing-page minute.
Minimum time to first value is about 15 minutes: sign up, connect a voice provider (Vapi, Retell, etc.), and run a test simulation. For a full CI/CD integration with Tests as Code, expect a few hours to a day. Setting up production monitoring with OpenTelemetry tracing takes a bit longer, typically a day to integrate into your existing stack.
Switching to or from Cekura
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From fragmented tools (e.g., separate testing and observability platforms): import your existing test scenarios and map your metrics to Cekura's evaluators—most teams get parity in a few days.
- →From internal scripts: use the CLI/SDK to wrap your existing test cases and start generating richer reports.
- →From manual QA: use Cekura's simulation to replace manual scenario testing, starting with your most critical flows.
- ↗To open-source alternatives: export your test scenarios from cekura.tests.json and adapt them to a CI runner.
- ↗To other observability platforms: use OpenTelemetry tracing to export spans to any OTLP-compatible backend, like Jaeger or Prometheus.
- ↗To a custom solution: use the Python SDK to retrieve your test results and build an in-house dashboard.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Featured Head-to-Head Comparisons
Cekura vs Presto Voice
Cekura and Presto Voice are not direct competitors; Cekura is a testing/observability platform for any voice AI agent, while Presto Voice is a production drive-thru automation solution for QSR chains. Choose Cekura if you build or QA conversational AI and need simulation, monitoring, and debugging tools. Choose Presto Voice if you operate multi-location drive-thrus and want a turnkey voice AI ordering system with proven upsell lift.
Cekura vs Locus Robotics
Buyers should choose between two entirely different domains: Cekura for testing and monitoring conversational AI agents (voice/chat), and Locus Robotics for physical warehouse automation with AMRs. Cekura's recent additions like OpenTelemetry tracing and self-improving prompts make it ideal for AI QA teams; Locus Robotics' RaaS model and dynamic picking suit high-volume fulfillment centers. No direct overlap — decision hinges on whether need is software QA or physical logistics.
Cekura vs Truleo
Cekura and Truleo serve completely different markets. Choose Cekura if you're building or testing voice/chat AI agents and need robust simulation, monitoring, and LLM evaluation. Choose Truleo if you're in law enforcement and need to surface intelligence from siloed data sources. They are not direct competitors.
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