Cekura
FreemiumAutomated QA and observability for voice and chat AI agents
By Tanmay Verma, Founder · Last verified 05 Jul 2026
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Added 4d ago
77/100Safe BetIn short
Cekura — Automated QA and observability 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 $30/mo.
Is Cekura actually worth it?
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Editorial Verdict
Best for
Voice AI startups and scale-ups building conversational agentsQA engineers testing voice and chat botsProduct teams shipping customer-facing voice agentsAI automation agencies needing reliable agent testingEnterprises with compliance requirements (healthcare, fintech)
Not ideal for
Non-technical users looking for no-code chatbot builders (Cekura is a testing tool, not a bot builder)Teams building simple FAQ chatbots (overkill for basic text bots)Users needing extensive sentiment analysis beyond voice-specific metricsOrganizations that cannot share agent logs with a third-party platform (though self-hosting is available on Enterprise)
Cekura is the most comprehensive testing and monitoring platform for voice AI agents, with unique features like auto-improve and MCP integration. Its credit-based pricing can be restrictive for heavy users, but the Developer plan's 750 monthly credits let teams try before committing. Strong choice for teams shipping production voice agents.
Skip Cekura if Skip Cekura if you are building simple FAQ chatbots or non-voice agents that don't need deep voice-specific observability – tools like LangSmith may be a lighter fit.
Last verified: July 2026
What's new in Cekura
Checked 2 days agoAcross the latest 3 updates: 3 changelog entries.
ChangelogChangelog·29 days agoNewest
Insights, OpenTelemetry Tracing, and Other Improvements
Adds Insights for automated root-cause analysis of failing metrics, and OpenTelemetry tracing for LLM, TTS, STT, and tool calls.
ChangelogChangelog·May 24
Optimize Agent, Evaluator & Metric Versioning, EU Deployment
Launches Optimize Agent for self-improving prompts, full versioning for evaluators and metrics, and EU region deployment.
ChangelogChangelog·May 11
SDK & CLI, Skills repo upgrades, MCP with OAuth
Releases unified CLI and Python SDK, MCP OAuth support, and a skills repository for AI assistants.
What independent users actually report about Cekura
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).
72% positive28% critical
Recurring strengths
- +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.
Recurring frustrations
- −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.
Patterns worth knowing
Full-session evaluation praised for catching multi-turn failures
Seen on Hacker News
Team responsiveness and customer service highlighted
Seen on Product Hunt
Pricing transparency requested by potential users
Seen on Product Hunt
Learning curve
beginnerProductive in ~A few hours
Hidden costs people mention
- • Overages for high-volume simulation runs
- • Potential per-seat costs for team accounts
Viability Score
77/100
Safe BetHow likely is Cekura to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
80
website health
90
wrapper dependency
100
Last calculated: July 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
- Auto-improve agent prompts from evaluation results
- OpenTelemetry tracing for LLM, TTS, STT, tool calls
- Support for diverse personalities (accents, tones, interruptive)
- Insights: automated root-cause analysis of failing metrics
- Cron jobs for scheduled recurring testing runs
- Selective call export and customizable call table columns
- PDF report export for sharing results
- MCP server for AI assistant integration
- CLI/SDK for programmatic access
- EU deployment region for lower latency and data residency
About Cekura
FreemiumIntermediateAPI availableWeb · API · CLI
Cekura is a testing and monitoring platform for conversational AI agents, designed to help teams ship reliable voice and chat experiences. It enables developers and QA engineers to simulate thousands of scenarios with diverse personalities (accents, emotions, behaviors), run evaluations before going live, and monitor production calls for quality signals like empathy, responsiveness, and hallucinations. The platform integrates with popular voice agent frameworks (Vapi, Retell, ElevenLabs, Synthflow, LiveKit, Pipecat, Cisco, Five9, Agora, Cartesia, Speechmatics, Moss Legal) and supports both pre-production simulation and post-deployment observability. Key capabilities include parallel scenario execution, customizable LLM judges with versioning, real-time alerts, conversation replay, and auto-improvement loops that suggest prompt fixes based on evaluation results. Recent additions include an MCP server for AI assistant integration, CLI/SDK for programmatic access, EU deployment region, Insights for automated root-cause analysis, and OpenTelemetry tracing. Cekura differentiates itself by combining testing, monitoring, and self-improvement in one workflow. It offers voice-specific metrics (interruption tracking, gibberish detection, latency, sentiment), OpenTelemetry tracing for deep debugging, and enterprise-grade compliance (SOC 2, HIPAA, GDPR). The platform recently raised $2.4M and is backed by Y Combinator. Targeted at conversational AI teams ranging from indie developers to large enterprises, Cekura aims to reduce the manual effort of QA while catching regressions and performance issues before they impact end users.
Behind the Verdict
Cekura excels in bridging testing and monitoring for voice AI agents, a niche that few tools address holistically. Its scenario library with diverse personalities allows you to simulate realistic interactions, including angry or interruptive users. The auto-improve feature that suggests prompt fixes based on evaluation results closes the feedback loop quickly. The recent addition of OpenTelemetry tracing gives you deep visibility into LLM, TTS, STT, and tool calls, making debugging much easier. However, the credit-based pricing (750 credits included in the Developer plan) can be a bottleneck for high-volume testing; additional credits incur pay-as-you-go costs. The free tier is only a 7-day trial, which may not be sufficient for thorough evaluation. While Cekura supports some chat testing, its focus is clearly voice agents. For teams building simple FAQ chatbots or non-voice flows, other tools might be more appropriate. Enterprise features like self-hosting, custom fine-tuned metrics, and dedicated support require a custom plan and likely a significant commitment. Overall, if you're shipping production voice agents, especially with Vapi, Retell, or ElevenLabs, Cekura is a solid pick. It competes with tools like LangSmith and HoneyHive but offers voice-specific metrics those lack.
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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.
QA engineer at a voice AI startup
You need to test a new voice agent update before release. You create a test profile with 50 scenarios using diverse personalities (angry, confused, non-native accent) and run them in parallel. Cekura scores each call on empathy and hallucination metrics, flags failures, and suggests prompt improvements.
Outcome: You identify 3 regression issues in 10 minutes, fix them with the suggested prompt changes, and re-run to confirm passing before deploying.
Product manager at a customer service automation company
After deploying a voice agent, you want to monitor real-time call quality. You set up dashboards for sentiment, interruption rate, and gibberish detection, with Slack alerts if hallucinations exceed 5%. You review flagged calls on replay.
Outcome: You catch a spike in sentiment drops linked to a new vendor and roll back the change within an hour, preventing customer dissatisfaction.
Indie developer building a voice assistant
You've integrated Retell AI and want to ensure the agent handles appointment cancellations correctly. You use Cekura's cron jobs to run 20 scenarios every Monday morning, exporting a PDF report to share with your co-founder.
Outcome: You get weekly automated regression tests without manual effort, and the PDF report gives your team confidence before each release.
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
- Export PDF reports of test results to share with stakeholders before a production launch
Limitations
- Cekura's free tier is limited (7-day trial) and the Developer plan caps credits at 750 per month before pay-as-you-go kicks in, which may be insufficient for high-volume testing.
- The platform is currently focused on voice agents with some chat support; non-voice conversational flows may be less mature.
- Enterprise features like self-hosting and custom fine-tuned metrics require a custom plan and likely a significant commitment.
as of 2026-07-05
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
$360
Over 12 months
Effective monthly
$30
Billed monthly
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.
Developer
$30/mo
Ideal for
Individual developers or small agencies testing a few voice agents with moderate call volume (up to 750 simulated calls per month).
What this tier adds
Starting tier with 750 credits, 1 project, 10 concurrent calls, and 30-day log retention — free trial available for 7 days.
Enterprise
Custom
Ideal for
Voice AI startups and enterprises needing custom scale, multiple projects, advanced compliance (SOC 2, HIPAA, GDPR), and features like self-hosting, load testing, and red teaming.
What this tier adds
Adds custom credits, multiple projects, self-hosting, white label reports, custom fine-tuned metrics, load testing and red teaming, dedicated support, and audit logs.
Integrations
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 $30/mo Developer plan with 750 credits fits solo developers and small agencies testing a handful of agents weekly. For high-volume testing, credits add up – cheaper than building in-house but pricier than a fixed-seat tool like LangSmith ($99/mo for 1M tokens). Enterprise pricing is custom and targets compliance-heavy teams.
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.
For a single-agent setup with a supported integration (Vapi, Retell, ElevenLabs), you can have your first test running in under 10 minutes via the web UI. CLI/SDK and MCP setup take about 30 minutes for CI pipeline or AI assistant integration. Enterprise onboarding with custom integrations may take 1-2 days.
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.
Migrating in
- →From custom scripts or manual testing: Replace ad-hoc scenario lists with Cekura's scenario library and cron jobs, then integrate their API to push test results to your existing dashboards.
- →From another monitoring tool (e.g., LangSmith): Export your test case definitions and agent prompts, recreate evaluators in Cekura, and configure the same alerting rules via webhooks.
- →From no testing tool: Import agent test scenarios as CSV via Cekura's test profile JSON format, set up the first integration (e.g., Vapi OAuth), and schedule a cron job.
Migrating out
- ↗To an in-house solution: Export all test results and logs via Cekura's API (JSON/CSV) and import into your own database; rerun scenarios using your own scripts.
- ↗To another vendor (e.g., QA.tech): Convert Cekura evaluator definitions into the target platform's metric format, and replay test scenarios using the new tool's scenario builder.
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Frequently Asked Questions
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