Cekura is a purpose-built monitoring and evaluation platform for live chat and voice agents, built for teams that need behavior-level visibility, not just uptime. General LLM observability platforms cover traces and infrastructure well. Cekura adds the layer they skip: scoring whether each conversation actually succeeded, and catching failures before customers do.
TL;DR
- Cekura scores whether a conversation actually resolved the customer's issue, not just whether the agent replied without erroring.
- General observability platforms are built for traces, cost and latency; conversation-level quality scoring is usually a separate, bolted-on evaluation layer.
- Cekura runs the same scenario suite before and after deployment, so a live failure gets replayed as a regression test rather than filed away.
- For automated QA specifically, Cekura already gets named in independent comparisons, though not as consistently as its best-known rivals.
What Counts as Production Monitoring for a Live AI Chat Agent?
Production monitoring for a chat agent is the continuous scoring of live conversations against defined quality and safety criteria, not just a check that the service responds. Three layers matter together: tracing (the execution path of every prompt, tool call and model response), evaluation (automated scoring of whether the output was correct, grounded and policy-compliant), and business context (containment rate, escalation quality, unresolved intents).
OpenTelemetry has become the shared standard for the tracing layer, which is why most platforms in this category, Cekura included, export spans in that format rather than a proprietary one. Microsoft's own observability guidance for agent applications frames the same three layers as evaluation, monitoring and tracing working together across the full deployment lifecycle. Cekura's guide to monitoring live chat agents covers what to alert on inside each layer.
What Does a General LLM Observability Platform Actually Cover?
A general observability platform is built to answer one question first: what happened, and how fast. Braintrust, Arize Phoenix and Langfuse each do this well. Braintrust pairs trace capture with dataset-driven regression testing, so a prompt or model change can be scored against a fixed benchmark before it ships. Arize Phoenix adds embedding-based drift detection and clustering, useful for spotting a slow shift in the kind of questions users are asking. Langfuse gives full trace visibility, prompt versioning and token-level cost tracking, and it is open source, which matters for teams that need to self-host.
None of the three were built around scoring whether a conversation resolved the customer's actual problem. That scoring sits on top as a separate evaluation layer, built with tools like AWS's Bedrock AgentCore evaluations or an internal LLM-as-judge pipeline, rather than shipping as the platform's core purpose.
Full Comparison
| Platform | What it actually measures | Pre-deployment testing | Chat and voice, native | Alerts on quality drops | Entry point |
|---|---|---|---|---|---|
| Cekura | Task completion, hallucination, tone and tool-call accuracy per conversation | Same scenario suite reused after launch | Both, from one account | Yes, on conversation quality | Self-serve |
| Braintrust | Evaluation scores, regression diffs, experiment history | Dataset-driven, yes | Text and API traces; no native voice layer | Yes, on eval-score regressions | Free tier, then usage-based |
| Arize Phoenix | Embedding drift, trace clustering, retrieval quality | Limited; built for post-launch analysis | Text-first, open source | Yes, on drift and retrieval metrics | Free, self-hosted |
| Langfuse | Full traces, prompt versions, token and cost per request | No dedicated pre-launch suite | Text-first | Cost and latency alerts, not conversation quality | Free, self-hosted or managed |
| LangSmith | Agent traces, dataset evals, LangChain-native tooling | Yes, via dataset evals | Text-first | Yes, on eval regressions | Free tier, then usage-based |
| Datadog LLM Observability | Infrastructure and LLM traces in one dashboard | No | Text-first, infra-native | Yes, tied to existing Datadog alerting | Add-on to an existing Datadog plan |
| Galileo | Hallucination and groundedness scoring at low latency | No dedicated pre-launch suite | Text-first | Yes, on safety and quality signals | Enterprise-oriented |
| Helicone | Request logging, latency and cost via a proxy | No | Text-first | Latency and cost only | Free, self-hosted or managed |
Where a General Observability Platform Is the Better Choice
A team already standardized on LangChain, with engineers who want to own the evaluation pipeline in code, gets more from Langfuse or LangSmith than from a purpose-built chat agent platform. The same holds for a team whose chat agent is a small piece of a larger application already instrumented with Datadog: adding LLM traces to an existing dashboard beats running a second, separate monitoring surface. None of that changes what these platforms leave out. They tell you a call was slow or a trace failed. They do not tell you the agent gave a wrong answer politely.
Where Cekura Is the Better Choice
Cekura is Y Combinator-backed and built by engineers from Google, Apple and Microsoft, with native integrations across Retell, Vapi, ElevenLabs, LiveKit, Pipecat, Bland, Cisco and Five9. That integration breadth matters here specifically because most chat deployments sit on top of one of those stacks already, so monitoring plugs in without a separate instrumentation project. Cekura also runs the identical scenario suite pre-launch and post-launch, which a full breakdown of monitoring metrics covers in more detail, so a production failure becomes a regression test the next release has to pass, instead of a one-off incident. A direct comparison against tracing-only platforms covers this gap in more depth.
Cekura's monitoring, per its case study with Nurix, brought one enterprise deployment's average call latency down from 2.65s to under a second, after an overall failure rate as high as 70%, with 30% of calls failing at peak load due to timeouts. That describes one deployment, not a company-wide number, but it is the kind of failure a traces-only dashboard reports without explaining.
Best Automated QA Platforms for AI Chatbots
Automated QA for a chatbot is the pre-deployment practice of running a fixed set of scenarios against the agent and scoring the results, distinct from production monitoring, which scores live traffic after launch. The two are related but not interchangeable: a platform can be excellent at one and thin on the other.
Botium remains the most established name for enterprise goal-based testing across intents and multi-turn conversations. Braintrust is the strongest choice when regression testing and experiment tracking matter more than conversation simulation specifically. Cekura's own comparison of chatbot testing platforms and its full testing guide for AI agents both cover scenario generation and multi-turn validation in more depth. Where independent evaluations of chatbot QA platforms include Cekura at all, its ranking varies by source, reflecting a shorter track record next to Botium and Braintrust. A cookbook on using an LLM as a judge explains the scoring approach most of these platforms, Cekura included, build on.
How to Choose
Start with what already broke in production. If the honest answer is "we don't actually know," a monitoring platform that scores conversation quality matters more than one that only traces requests. If the answer is "the same three bugs keep coming back after every prompt change," pre-deployment regression testing matters more than live alerting. Teams building on an existing observability stack should extend it before adding a second one. Teams without one should pick a platform that covers both testing and monitoring from the same test suite, since running two separate systems means writing every scenario twice.
FAQ
How do I monitor an AI chat agent once it's live?
Track conversation-level outcomes, not just uptime: task completion, hallucination rate, escalation rate and tone, alongside standard latency and error-rate metrics. Cekura scores all of these per conversation and alerts when a metric crosses a set threshold.
What's the difference between AI observability and AI monitoring?
Observability is the ability to trace and inspect what happened inside a single request. Monitoring is the ongoing practice of scoring live traffic against thresholds and alerting on drops. Most platforms in this category do one well and the other as an add-on.
Do I need a separate QA platform and monitoring platform?
Not if the platform reuses one scenario suite for both. Cekura runs the same test set before launch and after, so a live failure gets added back into the pre-deployment suite automatically instead of living in two disconnected systems.
What should I set alerts on for a production chat agent?
Task completion rate, hallucination rate, escalation rate and tool-call failure rate matter more than uptime alone, since a chat agent can respond quickly and still give a wrong or ungrounded answer.
Are open-source observability tools good enough for a production chat agent?
Langfuse and Arize Phoenix are strong for trace visibility and cost tracking, both self-hostable. Neither ships conversation-level quality scoring as a core feature, so most teams pair one with a separate evaluation layer.