If you're comparing Helicone vs Langfuse, Cekura is the third tool worth knowing about. I've tested all three, and this breakdown covers the strengths and limits of each one.
Helicone vs Langfuse vs Cekura: At a Glance
The comparison below is what you need before committing to any of the three tools.
| 💻 Tool | 🎯 Best For | 💰 Starting Price | ⚡ Key Strength |
|---|---|---|---|
| Cekura | If you're building and QA-testing voice and chat AI agents | $30/month (7-day free trial, no credit card required) | End-to-end automated QA with production call simulation |
| Helicone | Developers monitoring LLM API costs and request performance | $79/month (with a limited free tier) | Lightweight proxy-based LLM gateway with a one-line integration |
| Langfuse | Engineering teams that need deep LLM tracing and prompt management | $29/month (with a limited free tier) | Open-source observability with full prompt lifecycle control |
Choose Cekura if: you're shipping voice or chat AI agents and need pre-launch scenario testing, production call monitoring, and automated QA.
Choose Helicone if: you want fast LLM request logging, caching, and cost tracking without too much strain on engineers.
Choose Langfuse if: you need open-source LLM tracing, prompt versioning, and evaluation pipelines, especially if self-hosting matters to you.
Meet the Contenders
All three tools aren't built for the same job, so the comparison only makes sense once you know what each one is doing.
Cekura: Purpose-Built QA for Voice and Chat AI Agents
Cekura is an automated testing and monitoring platform focused on voice and chat AI agents. It lets you run full conversation scenarios before launch by testing different user personalities and replaying problematic calls.
This way, compliance failures get caught before they reach production.
Native integrations work out of the box for Retell, VAPI, ElevenLabs, LiveKit, Pipecat, and Bland.
Helicone: LLM Gateway with Built-In Observability
Helicone is an open-source LLM gateway that sits between your app and your model provider, where it logs every request automatically. It gives developers fast visibility into token usage, costs, latency, and errors with near-zero setup via a one-line proxy change.
It works with OpenAI, Anthropic, and 26 other providers. Helicone was acquired by Mintlify in March 2026, and it's now in maintenance mode (it's functional, but no new features are shipping).
Langfuse: Open-Source LLM Engineering Platform
Langfuse is the leading open-source LLM engineering platform by community adoption, used by 19 of the Fortune 50 and processing over 10 billion observations per month.
It covers tracing, prompt management, evaluations, experiments, and human annotation in one connected workflow.
It's free to self-host under an MIT license, OpenTelemetry-native, and covers 80+ integrations.
Helicone vs Langfuse vs Cekura: Feature Breakdown
The categories below are where these tools diverge for teams building conversational AI. Where a tool has no answer for a feature, that tells you more than the pricing page.
Agent Testing and Simulation
Cekura: In my testing, the personality simulation was the most useful feature. I ran the same outbound scenario against three user profiles and caught a compliance gap on the second one that wouldn't have shown up in a post-call log.
Interruption handling and adversarial users are also configurable, and the setup was faster than I expected.
It also supports conditional actions during the test. If the agent skips a required disclosure, the simulation can escalate the scenario on the spot instead of waiting for the next test run.
Cekura targets voice and chat agents exclusively, so general LLM workflows are outside its scope.
Helicone: Logging happens post-request, so by the time you see the issue, it's already reached a user. There's no way to catch problems before they go live.
Langfuse: Offers an experiment feature to test prompt changes against datasets, with side-by-side comparison of results. It covers text-based LLM evaluation, so it doesn't handle live multi-turn voice agent simulation.
Winner: Cekura. The strongest option here for pre-production agent simulation.
Observability and Tracing
Cekura: Logs full conversation sessions rather than individual turns. Its LLM Judge evaluates the entire transcript at once, which means it can catch failures that span multiple turns, like an agent that skips verification but proceeds to the next step anyway.
The platform natively tracks silence and dead air, interruption handling, and exact audio latency between user input and agent response. Raw LLM token traces aren't part of what it captures.
Helicone: The one-line proxy swap took minutes in my setup, genuinely the fastest time-to-first-log of the three. It covers 26 model providers out of the box. The tradeoff you feel immediately is that you're always looking backward. By the time a problem shows up, a user has already seen it.
It has processed over 14.2 trillion tokens across more than 16,000 organizations. Tracing is turn-level only, with no support for multi-step agent flows.
Langfuse: Hierarchical traces capture every LLM call, tool invocation, and retrieval step, with filtering by user, session, cost, latency, or custom metadata. It runs on OpenTelemetry natively. Voice-specific signals like interruptions or audio latency aren't available out of the box.
Winner: Tie. Langfuse wins on general LLM tracing depth. Cekura wins for voice and chat agent production monitoring.
Evaluations
Cekura: Runs one LLM Judge per full conversation, which costs considerably less than evaluating every individual request.
It uses a DSPy-based metric compiler where you annotate 5 to 10 calls manually and the system builds the best evaluation prompt until it matches your grades.
Cekura also supports custom metrics like empathy, compliance adherence, and hallucination detection. It targets conversational AI, so teams running general LLM evaluation tasks will need a different tool.
Helicone: Basic evaluation support through third-party integrations, with no native evaluation pipeline. Evals aren't a core part of what it does.
Langfuse: Full evaluation suite with LLM-as-a-judge, heuristic functions, and human annotation workflows. Includes dataset creation, prompt experiments via UI and SDK, and golden dataset management.
The entire evaluation layer is text-native, so voice-specific QA isn't available.
Winner: Cekura for voice and chat agent teams. Langfuse is the stronger option for general LLM evaluation pipelines.
Integrations and Setup
Cekura: Covers the integrations mentioned above, plus Cisco, Five9, and Synthflow, which gives it more voice platform coverage than either of the other two. Onboarding requires a demo request, so there's no self-serve option.
Helicone: One-line proxy change to start logging. Supports OpenAI, Anthropic, Azure, LiteLLM, and 26 other model providers. There's a zero-friction setup, though there are no native integrations for voice platforms like VAPI or Retell.
Langfuse: 80+ integrations including LangChain, Vercel AI SDK, LiteLLM, Pydantic AI, Google ADK, CrewAI, LiveKit, and Pipecat. Available as cloud or self-hosted via Docker, Kubernetes, AWS, GCP, or Azure under an MIT license.
The setup takes more effort than Helicone, particularly for self-hosted deployments.
Winner: Helicone for fastest time to first log. Cekura for voice platform coverage.
What Real Users Say
I noticed a consistent pattern in the reviews comparing Helicone vs Langfuse vs Cekura. Each tool gets praised when it fits the job and criticized when someone pushes it past what it was built for.
Cekura
✅ "Second layer is automated evaluation where you stop relying on humans calling in all day. That is where platforms like Cekura helped us." — Verified user, Reddit
✅ "We ended up using Cekura because it runs full multi-turn stress tests against accents, noise, interruptions, and memory retention. The interesting part was seeing metrics like context drift and recovery instead of just WER." — Verified user, Reddit
Helicone
✅ "Helicone is a great tool for monitoring your LLM projects." — Sezer Yavuz, Product Hunt
✅ "Helicone just works right out of the box – really helpful for us to dig into user issues and understanding how much money we're burning on LLM API calls." — Brandon Chen, Product Hunt
❌ "Dropped Helicone as an option for the company I work for because the 'generate' API demands I put API keys in .env instead of supporting them inline." — Verified User, Reddit
❌ "How long Helicone takes to scan the computer while doing the upload." — Ieshia G., G2
Langfuse
✅ "Highly recommend Langfuse for anyone using complex chains or with user-facing chat applications, where latency becomes crucial." — Verified User, Product Hunt
✅ "Being able to host Langfuse on our own infrastructure while getting enterprise-grade LLM observability is exactly what we needed." — Product Hunt
❌ "On the observability side: I've used Langfuse, and it's much better than nothing, but I found I outgrew it quickly." — Verified User, Reddit
❌ "Every trace in Langfuse, still no idea what actually broke. Anyone else hit this wall?" — Verified User, Reddit
Which Tool Should You Choose?
Helicone, Langfuse, and Cekura solve different problems, which is why picking based on name recognition tends to backfire.
If you make the wrong choice, you could end up building a monitoring setup around questions your stack isn't asking.
Choose Cekura if you:
- Are shipping a voice or chat AI agent and worried about edge cases, angry callers, off-script exchanges, and compliance gaps in production.
- Need to catch broken agent behavior before it reaches production and a customer finds it for you.
- Work in a regulated vertical like healthcare, financial services, or recruitment, where a single bad call carries real legal and compliance consequences.
- Are already on VAPI, Retell, LiveKit, Pipecat, ElevenLabs, or Synthflow and need a QA layer that plugs in natively instead of one you have to wire together manually.
Choose Helicone if you:
- Are moving fast on an LLM-powered product and need cost and latency visibility running in minutes.
- Don't have the engineering bandwidth to set up complex tracing infrastructure, and just need to know what your model is doing and what it's costing you.
Choose Langfuse if you:
- Lead an engineering team running serious LLM applications and need a full audit trail (every call, every prompt version, every evaluation run) in one place.
- Self-hosting is a hard requirement, whether for compliance, data sovereignty, or cost control at scale.
- Are iterating heavily on prompts and need datasets, human annotation workflows, and evaluation pipelines that connect directly to your production traces.
My Final Verdict
Helicone vs Langfuse comes down to depth versus speed of setup, with deep LLM tracing and prompt versioning on one side and cost and latency visibility with zero overhead on the other.
Cekura covers different ground, with pre-production conversation simulations, multi-turn compliance tracking, and production alerts when a user drops mid-flow.
For teams shipping voice or chat agents, that last part is where the other two come up short.
Cekura supports SOC 2, HIPAA, and GDPR compliance, covering transcript redaction, role-based access, and audit trails.
Test Before Your Users Do
If your team runs voice or chat AI agents in production, book a demo with Cekura and see how automated simulation testing fits into your stack.
Frequently Asked Questions
What Is the Main Difference Between Helicone and Langfuse?
The main difference between Helicone vs Langfuse is how they integrate. Helicone works as a proxy, so you just change one URL, and you're logged in in 15 minutes.
Langfuse uses SDKs, so it takes longer to set up, but gives you hierarchical traces, prompt versioning, and evaluation pipelines that Helicone doesn't have.
Is Langfuse Really Free?
Yes, Langfuse is free to self-host under an MIT license with no feature gates. The cloud version has a free Hobby tier that includes up to 50,000 units per month. Paid cloud plans start at $29/month for the Core plan.
Can Helicone Test Voice AI Agents?
No, Helicone doesn't support voice AI agent testing. It logs LLM API requests post-call, so it can tell you what a model returned and what it cost.
It can't simulate conversations, test multi-turn agent behavior, or track voice-specific signals like interruptions or audio latency.
What Is Cekura Used For?
Cekura is an automated QA platform for voice and chat AI agents. It runs pre-production simulations to test how agents handle real-world scenarios before launch, and monitors production calls for quality signals like compliance gaps, sentiment, and conversation drop-offs.
What Happened to Langfuse?
Langfuse was acquired by ClickHouse in January 2026. Unlike Helicone, Langfuse is continuing active development, and ClickHouse has committed to maintaining it as an open-source project and keeping the cloud offering running as-is.
Is Langfuse Free To Self-Host?
Yes. Langfuse is fully open source under an MIT license with no feature gates. Every capability, including tracing, evaluations, prompt management, and experiments, is available when you self-host. You only pay if you use Langfuse Cloud or need enterprise support.