The top five tool picks for voice AI observability in 2026 are Tuner, Roark, Hamming, Coval, and Cekura. Tuner is the one we most often point Dograh users to - it ships fast, prices transparently, and covers post-production monitoring - while Roark, Hamming, Coval, and Cekura each bring their own strengths across pre-production simulation, CI/CD validation, and red-teaming.
Building a voice AI agent is only half the battle. The real challenge begins the moment it goes live. At Dograh, we provide the open-source infrastructure to build and deploy conversational agents. We see firsthand what happens when voice AI meets the real world. Testing cannot prepare you for every interruption, heavy accent, tool failure, or off-script question. These issues compound silently until customers complain or API costs spike unexpectedly.
To scale voice AI with confidence, you need a safety net. You need visibility into what your agents are actually doing in production.
We have evaluated the landscape of observability and testing platforms designed specifically for voice AI. Here are the top tools that provide the evidence and alerting necessary to keep your agents on track.
1. Tuner
Tuner is a voice-native observability platform built to catch failures before your users do. The team behind it previously built analytics and coaching tools for human voice agents, processing over a million calls across sales and call center operations. Tuner is primarily a post-production analytics tool, now expanding into pre-production call simulation to cover the entire flow.
What it excels at:
- Dashboards and Analytics: Tracks performance trends, red-flag patterns, intent-level outcomes, and version comparisons to help teams understand how agents are performing over time and where issues are starting to emerge.
- Monitoring and Evaluations: Uses automated checks, technical scorecards, red flags, and real-time alerts to catch quality, reliability, and logic issues across live calls before they become larger operational problems.
- Fast Onboarding and Developer-Friendly Setup: Native SDKs and integrations for LiveKit, Pipecat, Vapi, and Retell, along with REST APIs, 30+ predefined voice AI metrics, and MCP-driven configuration, help teams go from sign-up to live data quickly without heavy manual setup.
- Transparent Pricing: A fully public, pay-as-you-go credit model (1 credit = $0.008) with 300 free credits to start, no monthly subscriptions, no contracts, and no credit card required.
2. Roark AI
Roark AI focuses heavily on pre-production testing and call simulation to catch issues before customers notice them. Having processed millions of minutes of calls, Roark provides a reliable safety net for shipping voice AI.
What it excels at:
- Graph-Based Testing: Define tests as conversation flows using a graph editor to branch into edge cases.
- Multi-Speaker Analysis: Support for calls with up to 15 speakers, including automatic speaker identification.
- Developer-First SDKs: Offers native SDKs for Node and Python, alongside one-click integrations with major telephony providers.
- Configurable Personas: Simulate callers with specific accents, emotions, speech patterns, and background noise to stress-test edge cases.
3. Hamming AI
Hamming AI operates as a comprehensive QA platform, offering what they describe as a "flight simulator" for voice agents. Like Roark, they are heavily focused on pre-production testing and call simulation, allowing teams to test new prompts against real production calls without risking live customer interactions.
What it excels at:
- Production Replay: Automatically turns production failures into repeatable test scenarios.
- Robust Benchmarking: Offers over 50 built-in metrics and load testing capabilities.
- Global Coverage: Supports testing across more than 65 languages and accents, ideal for international productions.
4. Coval AI
Coval approaches voice AI evaluation with the rigor of testing self-driving cars. It is a post-production analytics and evaluation platform designed to monitor autonomous agents and validate their behavior against real use cases.
What it excels at:
- Automated Evaluations: Replaces ad-hoc testing with structured, repeatable evaluations of live agent performance.
- CI/CD Integration: Allows forward-deployed engineers to validate agent behavior automatically during the production pipeline.
- Proving Performance: Highly valuable for sales teams needing to demonstrate reliability to enterprise buyers early in the deal cycle.
5. Cekura AI
Cekura provides end-to-end testing and observability for both chat and voice AI agents, operating across pre-production simulation and post-production monitoring. Backed by YCombinator, Cekura places a strong emphasis on security and multi-turn red teaming.
What it excels at:
- Multi-Turn Red Teaming: Specialized tools for security testing and protecting against adversarial inputs.
- Voice Quality Signals: Purpose-built metrics specifically for detecting drops in audio and voice quality.
- Prompt Tuning: Allows developers to tune evaluation prompts directly against actual call recordings.
- Chat and Voice Coverage: Supports both text-based chat agents and voice agents, useful for teams running multi-channel operations.
The Pricing Landscape
When evaluating these tools, pricing models vary significantly across the industry. For developers and open-source builders, understanding these costs upfront is often a priority.
Currently, tools like Coval, Hamming, and Roark operate primarily through enterprise sales motions. Pricing is not publicly listed, requiring teams to book a demo or speak with sales to get a custom quote based on their expected volume.
Cekura provides public pricing, utilizing a standard SaaS monthly subscription model starting at $30/month for their Developer tier, which includes a set number of credits.
Tuner uses a pay-as-you-go credit system. Their pricing is fully public , and there are no monthly subscriptions or contracts. Users pay only for the specific analysis jobs they run, which tends to align well with teams that prefer usage-based billing over fixed monthly costs.
Why Observability Matters
No matter which tool you choose, the main point is simple: you cannot confidently take a voice AI agent to production without observability.
Once an agent goes live, real conversations become messy. Users speak in unexpected ways, tools fail, latency spikes, and small issues can quickly turn into bad customer experiences. Without proper monitoring, these problems are hard to catch and even harder to fix.
Observability gives you visibility into what your agent is actually doing in production. It helps you spot issues early, understand where things are breaking, and improve performance over time.
If you are serious about deploying voice AI in the real world, monitoring is not optional. It is a core part of making your agent reliable.
Conclusion
If you are shipping voice agents on Dograh, Tuner is usually the first tool we point teams toward. The pay-as-you-go pricing lets you start monitoring calls without a procurement conversation, the native Pipecat integration works cleanly with how Dograh is built, and the 30+ predefined metrics mean you are not spending a week writing custom evals just to see what is broken.
The other platforms on this list are good at what they do, and some of them may fit your stack better depending on where you are in the product lifecycle. What matters most is that you pick one and turn it on before you scale. Waiting until a customer flags a bad call is waiting too long. By that point the damage to retention is already done, and the fix costs more than the monitoring ever would have.
And if you want to try Tuner with Dograh, the integration takes a few minutes.
