Cekura Review (2026): Pricing, Features & Honest Verdict
TLDR
Cekura is an automated QA and monitoring platform for voice and chat AI agents. Simulates conversations, evaluates full sessions, monitors production calls. YC-backed with 75+ customers. Best for: teams building conversational AI. Price: from $30/mo (7-day free trial). Rating: 6.8/10
What is Cekura?
We found Cekura on Product Hunt where it earned 523 upvotes as a platform for testing voice and chat AI agents. This is not another AI agent builder. It is the quality assurance layer that sits on top of them.
If you are building a voice AI agent on Bland.ai, Vapi, or Retell, how do you know it works correctly before deploying to production? How do you catch when it starts giving wrong answers after a prompt change? How do you monitor live calls for compliance? Cekura answers all three questions.
Founded in 2024 by Tarush Agarwal (former quant finance), Shashij Gupta (NLP researcher at Google Research), and Sidhant Kabra (Fortune 500 consulting), Cekura went through Y Combinator Fall 2024 and raised $2.4M in seed funding. They serve 75+ customers including Jotform and OpenPhone.
| Plan | Price | Plan Features | Best For |
|---|---|---|---|
| Free Trial | $0 for 7 days | 300 credits, 1 user, 1 project | Evaluating the platform |
| Developer | $30/mo | 750 credits/mo, 10 concurrent calls, unlimited agents | Individual developers and small teams |
| Enterprise | Custom | Custom credits, self-hosting, SOC 2/HIPAA, Slack support | Large-scale voice AI deployments |
What Cekura Gets Right
Full-Session Evaluation
Most LLM evaluation tools (Langfuse, LangSmith) analyze individual API calls or turns. Cekura evaluates entire conversations end-to-end. A voice agent might handle turn 1-5 perfectly but fail on turn 6 when the conversation takes an unexpected direction. Session-level evaluation catches these failures that turn-level analysis misses.
Voice-Native Architecture
Cekura is built specifically for voice. It measures silence duration between responses, detects interruptions, tracks latency at millisecond precision, and simulates IVR navigation, DTMF tones, voicemail, and hold music. These are real-world voice conditions that generic testing tools ignore completely.
Deterministic Test Agents
When you test an AI agent with another AI, you introduce hallucination risk in the test itself. Cekura uses conditional action trees rather than free-form prompts for test agents. The test follows a structured, deterministic path, ensuring that test failures reflect problems in your agent, not in the testing tool.
Production Monitoring With Smart Alerts
After deployment, Cekura monitors live calls and sends semantic alerts based on business logic. Not “error rate exceeded 5%” but “agent promised a discount it was not authorized to give.” Alerts go to Slack, PagerDuty, or email. This catches real business risks that traditional monitoring misses.
Where Cekura Falls Short
Extremely Niche
If you are not building voice or chat AI agents, Cekura has zero relevance. The total addressable market is companies deploying conversational AI, which is growing fast but still relatively small. Most MakerStack readers will not need this tool.
No Free Plan
A 7-day trial with 300 credits is limited for evaluating a testing platform. You need time to integrate, build test suites, and run meaningful evaluations. Seven days might not be enough for teams with complex agents. A free tier with limited monthly credits would improve adoption.
Credit Opacity
750 credits per month on Developer. But how many credits does a single test call consume? How long can each simulation run? Without clear documentation on credit consumption rates, budgeting for testing workloads is guesswork.
Cekura Pricing Breakdown
Seven-day free trial with 300 credits. Developer plan at $30/month includes 750 credits, 1 user (additional users extra), 1 project, 10 concurrent calls, unlimited agents, and full API access. Enterprise is custom pricing with self-hosting, SOC 2 and HIPAA compliance, dedicated Slack support, and volume credits.
Who Should Use Cekura
Any team deploying voice or chat AI agents in production. Healthcare companies with patient-facing agents need compliance monitoring. Banks with customer service bots need accuracy testing. Any company where a bad AI conversation has real business consequences should be testing and monitoring with a dedicated tool.
Alternatives
Hamming
Voice AI simulation platform and the closest direct competitor. Also focused on testing voice agents with simulated conversations. Evaluate both platforms based on your specific voice platform integrations and testing requirements.
Langfuse
Open-source LLM observability platform. Turn-by-turn tracing and evaluation rather than full-session analysis. Better for general LLM applications. Lacks voice-specific metrics. Free self-hosted option. Best for teams that need LLM observability beyond just conversational AI.
LangSmith
LangChain evaluation and tracing platform. Similar to Langfuse with turn-level analysis. Tighter integration with LangChain ecosystem. Does not handle voice-specific testing. Best for teams already building with LangChain.
The Bottom Line
Cekura fills a genuine gap in the AI infrastructure stack. As more companies deploy voice agents, the need for reliable testing and monitoring grows. The full-session evaluation, voice-native metrics, and deterministic test agents are technically sound. The niche audience and credit-based pricing limit broader appeal. If you are building conversational AI, Cekura is worth the 7-day trial.
Cekura Pros & Cons
What We Like
- Full-session conversation evaluation rather than turn-by-turn analysis
- Voice-native metrics: silence detection, interruption analysis, latency tracking
- Integrates with all major voice platforms (Bland, Vapi, Retell, ElevenLabs)
- Conditional action trees prevent hallucination in test agents themselves
What Could Be Better
- Very niche: only relevant for teams building voice or chat AI agents
- No free plan, just a 7-day trial with 300 credits
- Small team (~15 people) for a product serving enterprise use cases
- Credit-based pricing makes cost prediction difficult for variable testing loads
Cekura FAQ
Is Cekura a voice AI platform?
No. Cekura is a testing and monitoring platform for voice and chat AI agents. It integrates with voice platforms like Bland.ai, Vapi, Retell, and others. You build your agent on those platforms and use Cekura to test, evaluate, and monitor it.
How is Cekura different from Langfuse or LangSmith?
Langfuse and LangSmith evaluate individual LLM turns and trace API calls. Cekura evaluates entire conversation sessions end-to-end, including voice-specific metrics like silence, interruptions, and latency. Cekura is purpose-built for conversational AI, while Langfuse/LangSmith are general LLM observability tools.
What industries use Cekura?
Healthcare (patient intake agents), banking (customer service bots), logistics (dispatch agents), recruitment (screening calls), and retail (customer support). Any industry deploying conversational AI agents benefits from automated testing and monitoring.
What is Cekura?
QA and monitoring platform for voice and chat AI agents. Tests conversational AI flows and catches regressions before production.
How much does Cekura cost?
Cekura pricing starts at $30/mo (7-day free trial).
Is Cekura worth it?
Cekura is an automated QA and monitoring platform for voice and chat AI agents. It simulates conversations, evaluates full sessions (not just individual turns), and monitors production calls with semantic alerting. 75+ customers, YC-backed, $2.4M raised. Very niche but genuinely solves the AI agent testing gap. $30/month Developer plan is accessible. We rate it 6.8/10.
