Sinapsis AI vs Langfuse vs Helicone
Langfuse and Helicone track what your models do. Sinapsis AI tracks what your models do AND what your users do, and tells you what to change.
LLM observability tools like Langfuse and Helicone have become essential for teams deploying AI. They answer critical questions: How much am I spending? Which models are slow? Where are errors happening?
But they only tell half the story.
The Observability Gap
Langfuse and Helicone show you AI-side metrics: token usage, latency percentiles, cost per request, error rates. This is valuable data. But it's disconnected from the most important question: is your AI actually helping your users?
Here's a real scenario:
Your speech-to-text model has a 3.2-second latency on Step 1 of your customer support pipeline. Langfuse shows you this metric. What it doesn't show you is that 40% of users abandon the conversation at exactly that point.
The AI metric says "3.2s latency." The user metric says "40% drop-off." The connection between them is the insight that matters, and no observability tool makes it today.
What Each Tool Offers
| Feature | Langfuse | Helicone | Sinapsis AI | |---------|----------|----------|-------------| | Token usage tracking | Yes | Yes | Yes | | Cost per request | Yes | Yes | Yes (per model, workflow, user) | | Latency monitoring | Yes | Yes | Yes | | Error tracking | Yes | Yes | Yes | | Model comparison | Limited | Limited | Yes (same task, side-by-side) | | User heatmaps | No | No | Yes | | Session replays | No | No | Yes | | Conversion funnels | No | No | Yes | | AI + user metric correlation | No | No | Yes | | Optimization recommendations | No | No | Yes (AI-powered) | | Workflow builder | No | No | Yes | | API deployment | No | No | Yes | | Self-hosted | Yes | Yes | Yes |
The Connection No One Else Makes
Sinapsis AI's Observe layer unifies both sides:
AI-side: Real-time cost tracking per model, per workflow step, per endpoint, per user. Latency percentiles, error rates, throughput. Model comparison across the same task.
User-side: Heatmaps of user interactions. Session replays showing exactly how users engage with AI features. Conversion funnels. Cohort analysis comparing how AI-powered users behave vs. non-AI users.
The link: AI performance metrics are directly correlated with user behavior data. You don't just see that a model is slow. You see the business impact of that slowness.
The Optimize Layer: From Data to Action
Langfuse and Helicone give you dashboards. Sinapsis AI gives you recommendations:
"Your
customer-supportworkflow costs $0.12/call. Step 3 (GPT-4 escalation) triggers on 67% of requests but only improves response quality by 3%. Recommendation: raise the sentiment threshold from -0.3 to -0.6. Projected savings: $4,200/month with no measurable quality loss."
"Users who interact with the
product-searchworkflow convert at 2.4x the rate of non-AI users, BUT 31% abandon after the first slow result (>2s). Recommendation: add streaming to Step 2."
Not dashboards you stare at. Intelligence that tells you what to do.
When to Use Each
Use Langfuse/Helicone when:
- You only need LLM-specific metrics
- You already have separate product analytics (PostHog, Amplitude)
- You're comfortable manually correlating AI and user data
Use Sinapsis AI when:
- You want AI and user metrics in one place
- You want the platform to tell you what to optimize
- You need the full stack (build, deploy, observe, optimize)
- You want team collaboration with workspaces
The Bottom Line
Langfuse and Helicone are excellent LLM observability tools. If you only need to track model performance, they work well. But if you're building a product where AI directly impacts user experience, and you want a single platform that builds, deploys, observes, AND optimizes, Sinapsis AI eliminates the gap between "what your models do" and "what your users do."