Top 6 Cekura Alternatives in 2026

Cekura is a solid tool for testing and monitoring Voice AI agents across the agent lifecycle, including simulations and production replay.

But since you’re here looking for alternatives, it could be for any of these common reasons that apply to most such platforms:

The QA scores can feel noisy. You get false alarms, or the tool misses real issues, so you stop trusting it.
Your test cases look good on paper, but real calls are messy. And the tool doesn’t always catch what breaks in production.
When something goes wrong, you want faster answers on what broke. Was it STT, latency, the model, or a tool/API call?

This guide is built for both buyers:

Operators deploying voice agents (support, sales, scheduling, inbound/outbound).
Builders shipping voice stacks (Voice AI platforms, agencies).

Top 6 Cekura Alternatives in 2026 (At A Glance)

Tool	Best fit for	What you’ll like	Watch-outs
ReachAll	If you want QA + control layer with an option to go fully managed, not just simple monitoring	Closed-loop reliability: evals + observability + root-cause tracing, plus a path to tested fixes and continuous improvement	You’re choosing more than a “QA add-on.” It’s a bigger platform decision.
Roark	If you care about audio-native realism and production replay	Deep focus on testing + monitoring voice calls, including real-world call replay	More “QA platform” than “ops + managed execution”
Hamming	If you need scenario generation, safety/compliance checks, and repeatable evals	End-to-end testing for voice and chat agents with scenario generation, production call replay, and a metrics-heavy QA workflow	You still own ops and fix workflow
Bluejay	If your pain is coverage and you want lots of scenarios/variables fast	Scenario generation from agent + customer data, lots of variables, A/B testing + red teaming, and team notifications	Can feel like a testing lab if you want “operational control”
Coval	If you want a broader agent evaluation stack beyond voice	Simulate + monitor voice and chat agents at scale, with an evaluation layer your whole team can use	If you want deep voice pipeline tracing, confirm what you get out of the box vs what you need to configure
SuperBryn	If you want a lighter-weight eval/monitoring workflow	Voice AI evals + observability with a strong “production-first” angle (including tracing across STT → LLM → TTS)	Less public clarity on voice-specific depth vs others

Top 6 Cekura Alternatives in 2026 (Deep Dives)

Find out the right alternative for you below:

1) ReachAll: Best for teams who want a QA Stack + control layer, not just monitoring

If you’re leaving Cekura because detection isn’t the bottleneck, remediation is. ReachAll is the cleanest switch.

ReachAll gives you a QA stack for deployed voice agents. It monitors and flags issues, traces failures across STT, LLM, and TTS, and supports a control layer to help apply fixes.

Key features that matter for voice AI monitoring

Deployed-agent QA + observability: evals on production calls, monitoring across STT/LLM/TTS, and alerts when quality dips.
Control-loop approach: detect, diagnose root cause, propose fixes, and validate changes against tests before rollout (useful when your biggest pain is slow iteration)
All-in-one stack: Get ReachAll voice agent + QA,or integrate with your existing voice stack as a QA + governance layer.

Why ReachAll stands out vs Cekura?

You get root cause, not just a score.
ReachAll offers QA + Governance stack around tracing failures across the full pipeline (STT, LLM, TTS) and pinpointing where the breakdown happened, so you’re not guessing what to change.
It’s designed to help you fix issues safely, not just find them.
The promise is detect → diagnose → propose fixes → validate against test suites → deploy without regressions. That “tested fixes” loop is the main difference versus tools that stop at evaluation and monitoring.
It treats QA as an operating system for production, not a periodic audit.
It’s conducts continuous evaluation on real traffic, with observability, alerting, and governance controls running all the time, so reliability becomes part of daily ops.
You can run it across your stack, not only inside one voice platform.
ReachAll’s QA + Governance layer can sit across STT/LLM/TTS components and work even if you run another production voice stack. That matters if you don’t want to swap your whole stack just to get better monitoring.
If your bottleneck is ops bandwidth, ReachAll is built to be hands-off.
They “build, test, deploy, evaluate, fix, scale” with no engineering overhead. That’s a real differentiator if you’re tired of stitching together tools and staffing QA manually.

If Cekura is helping you measure agent quality, ReachAll is trying to help you operate agent quality in production, with a loop that can actually push improvements without breaking working flows.

Best for

Teams who want to stop treating QA as a separate project and make reliability part of daily operations
Businesses that cannot afford missed calls, broken booking flows, or silent failures
Builders/partners who want to offer “reliability as a feature” to their own customers

Pros and Cons

PROS	CONS
Strong fit when you care about production reliability and continuous improvement loops	It’s a bigger platform decision as it is more than a “QA add-on.”
Reduces tool sprawl because monitoring and QA live with the voice system
Option to run it hands-off with a managed approach (useful when you don’t want to staff Voice AI QA)

2) Roark: Best for replay-based debugging and turning failed calls into test cases

Roark is a strong alternative when your biggest frustration is “our evals don’t reflect real calls.” It leans into voice-specific testing and monitoring patterns, including replaying real production conversations and tracking outcomes that correlate with user experience.

Key features that matter for voice AI monitoring

Voice-agent testing + monitoring workflows designed around real calls (not just toy prompts)
Replay-driven debugging for catching regressions in real conversations
Focus on metrics that matter for voice, not generic chat-only evaluation

Best for

Voice teams that ship fast and need high-confidence regression coverage
Products where tone, barge-in, interruptions, and pacing materially affect conversion/support outcomes

Pros and Cons

PROS	CONS
Good fit when you care about voice realism and production replay as the source of truth	Less compelling if you want a fully managed ops layer
Helps teams move beyond “text-only evals” that miss voice failure modes	May require more setup work than “business user” teams want
Strong match for engineering teams building voice at speed

3) Hamming: Best for repeatable testing, scenario coverage, and compliance-minded QA

Hamming is a practical alternative when your pain is coverage: too many edge cases, too many changes, too many regressions. It’s built around running scenarios, replaying calls, measuring outcomes, and keeping a consistent QA discipline as you iterate.

Key features that matter for voice AI monitoring

Scenario-based testing and repeatable eval workflows
Voice-agent metrics to track quality and stability over time
Compliance and safety framing (helpful for regulated or high-risk call flows)

Best for

Teams that want structured testing discipline and clear pass/fail gates
Builders who need to ship changes without breaking production

Pros and Cons

PROS	CONS
Strong testing workflow for voice agent reliability over time	Like most eval tools, it won’t replace the engineering work of fixing prompts, tools, and flows
Helps standardize QA so you’re not manually sampling calls	If your primary pain is “fix loop speed,” you may want a platform with stronger control loops
Good for teams that want compliance-oriented checks

4) Bluejay: Best for high-volume scenario generation and stress-test across languages, accents, noise, and user behaviors

Bluejay is a good alternative when your team keeps saying: “We don’t have enough tests.” It leans into high-volume scenario creation and lots of variables so you can stress your agent across languages, call contexts, and user types without writing everything by hand.

Key features that matter for voice AI monitoring

Auto-generated scenarios and extensive variable coverage (helpful for scale)
A/B testing and red teaming workflows
Observability + notifications so issues don’t sit in dashboards

Best for

Voice teams that want broader test coverage quickly
Teams running multilingual or multi-market voice deployments

Pros and Cons

PROS	CONS
Great when your main bottleneck is coverage and edge cases	Can feel like a testing lab if your pain is operational ownership and rapid remediation
Strong for systematic scenario generation, not just ad-hoc testing	Still requires a clear internal workflow to turn failures into changes
Helpful for teams juggling many languages/flows	If you want voice agent + QA in one system, you may prefer an all-in-one platform

5) Coval: Best for teams that want a broader agent evaluation stack (voice + chat)

Coval makes sense if you’re not only evaluating voice calls. If you have multiple agents, workflows, or multimodal inputs, Coval’s evaluation and monitoring approach can unify how you measure agent quality across the org.

Key features that matter for voice AI monitoring

Agent evaluation workflows and monitoring primitives that can extend to voice setups
Useful when you want consistency across many AI systems, not a single voice agent

Best for

Teams running multiple AI agents across functions
Organizations that want a centralized “evaluation layer” across agent types

Pros and Cons

PROS	CONS
Strong fit for teams treating evaluation as a shared platform capability	May feel heavyweight if you only need voice monitoring
Helps unify QA language across agents, not only voice	Less direct if your need is immediate production voice ops
Useful for experimentation and governance across AI apps

6) SuperBryn: Best for teams that want a simpler evaluation loop without heavy infra

SuperBryn is the “keep it simple” alternative. It can be attractive when the biggest barrier is adoption: teams want monitoring and evaluation, but don’t want to build an internal QA machine to get value.

Key features that matter for voice AI monitoring

Lightweight evaluation/monitoring workflow that reduces time-to-first-signal
Useful when you want to start with a practical QA loop and expand later

Best for

Smaller teams, agencies, or internal voice projects that need a fast start
Teams that want a simpler operational workflow before adding complexity

Pros and Cons

PROS	CONS
Lower operational burden to get started	May not satisfy advanced enterprise governance requirements
Good “first QA loop” for teams new to voice monitoring	You may outgrow it if you need deep pipeline-level root cause tooling
Useful for teams that value simplicity over maximal feature depth

Conclusion

If you’re evaluating Cekura alternatives, don’t make it a feature checklist exercise. Make it a bottleneck exercise.

If your bottleneck is operational reliability and fast fix loops, ReachAll is the #1 pick because it’s built around deployed voice reliability, not just evaluation.
If your bottleneck is test coverage and regression discipline, Hamming and Bluejay are strong picks.
If your bottleneck is voice realism and production replay, Roark is a serious contender.
If your bottleneck is cross-agent evaluation at scale, Coval can fit better.

And if you want an all-in-one setup so you’re not jumping between tools, ReachAll can help you deploy the voice agent, then monitor and control quality after you go live, so every call stays on-brand and reliable.

Frequently Asked Questions (FAQs)

1) What’s the difference between voice agent testing and voice agent monitoring?
Testing is what you do before you ship changes. You run scenarios and regression checks to catch breakage early. Monitoring is what you do after you go live, so you can see what is happening on real calls and spot issues fast.

2) What metrics should you track for a voice agent?
Track both technical quality and business outcomes. A solid baseline is goal completion, tool-call success, fallback rate, latency, hang-ups, escalation rate, and ASR accuracy. If you only track “QA scores,” you will miss why users are failing.

3) What is WER, and why should you care?
WER (word error rate) tells you how often speech-to-text gets words wrong. If WER is high, everything downstream gets harder because the model is reasoning over bad inputs. You do not need perfect WER, but you want to know when ASR noise is the real reason your agent looks “dumb.”

4) What is “barge-in,” and why does it matter in monitoring?
Barge-in is when a caller interrupts while your agent is speaking. If your system handles it badly, you get talk-over, missed intent, and broken turn-taking, which hurts conversion and CSAT fast. Make sure your monitoring can flag interruptions and talk-over patterns, not just transcript quality.

5) Can these tools pinpoint what failed in the voice pipeline (STT vs LLM vs TTS vs tool calls)?
Some can, some cannot. In demos, ask to see step-level traces: ASR output quality, tool-call success, TTS playback, and latency breakdown. If a vendor can’t show this clearly, you will spend time guessing.
ReachAll can trace failures across STT, LLM, and TTS in its QA/Governance materials.

6) Are LLM-based “auto evaluators” reliable, or do you still need human QA?
LLM judges can scale your QA, but they can also be inconsistent or biased depending on the prompt and the judge model. For high-stakes flows, keep a human review lane for disputed or high-impact failures, and treat automated scores as decision support, not absolute truth.

7) What security and enterprise controls should you ask for in any voice QA platform?
Ask for SSO (SAML or OIDC), role-based access control, audit logs, data retention controls, and deletion workflows. You want tight access control because audio and transcripts can contain sensitive info.
ReachAll has RBAC + SSO/OAuth, and also describes encryption, logging/audit trails, and retention/deletion controls in its policy docs.

8) SOC 2 Type I vs SOC 2 Type II: which one should you care about?
Type I checks whether controls are designed correctly at a point in time. Type II checks whether those controls actually operate effectively over a period of time. If you are buying for enterprise, Type II is usually the stronger signal.
In case of ReachAll, customers can request its SOC 2 report under NDA.

9) What does ISO 27001 actually mean?
ISO/IEC 27001 is a standard for running an information security management system (ISMS). If a vendor is certified, it means they went through an audit against that standard. Still ask what parts of the org and product the certification covers. ReachAll has ISO compliance/certification.

10) When do you need a HIPAA BAA from a vendor?
If your calls include PHI and you are a HIPAA covered entity (or working with one), you typically want a vendor willing to sign a business associate agreement (BAA). Ask for their BAA template early, not after procurement starts.
ReachAll will process PHI only after both parties execute a BAA.

11) What should you ask about GDPR and data retention for call audio and transcripts?
First, confirm what the vendor counts as “personal data” and what they store (audio, transcripts, metadata). Then ask about retention defaults, deletion SLAs, and how you can minimize stored data. GDPR applies when you process personal data, so retention controls matter.
ReachAll publishes a DPA, and its privacy policy describes retention defaults and deletion/export options.

12) How do you choose the right tool without getting stuck in “another dashboard”?
Pick based on your bottleneck: coverage (more scenarios), root-cause speed (pipeline traces), or fix-loop speed (alerts plus a path to remediation). If your bigger pain is operational reliability after go-live, it can be simpler to choose an all-in-one setup that can monitor and drive fixes, not just score calls. ReachAll is about post-launch monitoring plus a “control layer” that can suggest and apply fixes, which fits this workflow if you want fewer moving parts.