If you are evaluating LLM observability today, you are almost certainly comparing Laminar and Langfuse. Both are strong open-source platforms. Both cover tracing, evals, and datasets. Both have a polished UI. But for teams building production-grade AI agents, we believe Laminar is the clear, long-term foundation.
This is not a neutral comparison. It is honest and practical, and it is biased toward Laminar because our priorities are built around agent reliability, real-time debugging, and deep data access. If prompt management is your core workflow, Langfuse may be a fit. If observability and debugging are the center of your stack, Laminar wins.
The shift is structural. Early in the agent era, most systems were effectively single LLM calls with some prompt iteration. The bottleneck was prompt optimization. Today’s agents are multi-step, tool-heavy, and long-running. They spawn subagents, call external systems, and generate complex trace trees. The question is no longer “which prompt worked,” it is “what actually happened across the whole run.” Laminar is purpose-built for that world.
Quick Take
Laminar and Langfuse overlap on feature checklists. The difference shows up in what they optimize for.
- Laminar is optimized for real-time agent observability: fast ingestion, deep trace exploration, full-text search, SQL-native access, and debugging workflows that scale.
- Langfuse is optimized for the prompt lifecycle: prompt management, versioning, and evaluation workflows tightly coupled to prompt iteration.
If your agents are already in production, Laminar’s bias aligns with your day-to-day reality.
| Laminar | Langfuse | |
|---|---|---|
| Primary bias | Real-time agent observability | Prompt lifecycle management |
| Trace depth | Tree/timeline explorer for complex agents | Structured logs and observation model |
| Data access | SQL-native editor in product | API/SDK-centric access |
| Best fit | Production agents with complex traces | Prompt-iteration-heavy teams |
Observability Depth: Laminar Goes Further
Langfuse does a solid job capturing prompts, responses, token usage, and latency. It is a reliable tracing tool for earlier-stage workflows and prompt-centric iteration.
Laminar goes further because it is designed for complex, multi-step agents:
- Real-time trace viewing for long-running agents
- Full-text search over spans, making it easy to find specific errors or outputs across massive datasets
- A trace viewer built for complex agent trees, not just single prompt/response pairs
- OTel-native ingestion, which fits naturally into modern observability pipelines
If you are debugging multi-step agents that call tools, spawn subagents, and run for minutes, trace depth and real-time visibility are not optional. They are your core workflow. Laminar is built for that.
Data Access: SQL-Native and Built In
Laminar includes a built-in SQL editor that gives direct access to traces, spans, events, tags, and datasets. This changes the development loop:
- You can answer “why did this fail?” with one query.
- You can build custom metrics without waiting for product features.
- You can generate datasets directly from production traces.
Langfuse has a strong API, but the workflow still depends on programmatic access. Laminar’s SQL-first approach makes analysis immediate and interactive, which is exactly what you want when your agent fails at 2 a.m.
Signals: Ask New Questions of Old Traces
Agents are messy. The failure pattern you care about today might not even exist in your dashboards. Laminar’s Signals let you define a question once (prompt + schema), then run it across both new and historical traces.
That means you can:
- Detect specific failure modes as they happen (and alert on them)
- Backfill months of traces to validate a hypothesis
- Turn production trace history into labeled datasets without writing scripts
Langfuse is strong on prompt management, but Signals are built for agent debugging at scale: they sit directly on top of traces and make the system queryable in human terms.
UX: Trace-First vs Prompt-First
Langfuse’s UX is polished, especially around prompt workflows and evaluation.
Laminar’s UX is optimized for trace navigation and debugging at scale:
- Multi-view trace exploration (tree/timeline/reader)
- Fast traversal across large traces
- Clear cost and token visibility at the span level
- Replay workflows tied directly to trace data
In practice, this means Laminar is the faster tool when you are trying to understand what an agent actually did and why it failed.
| UX Area | Laminar | Langfuse |
|---|---|---|
| Trace navigation | Tree/timeline/reader with fast traversal | Trace detail with observation structure |
| Debug speed | Optimized for large, nested traces | Best when tied to prompt iteration |
| Replay workflow | Replay from spans inside traces | Playground from trace context |
Evaluations and Datasets: Both Strong, Laminar More Integrated
Both platforms support evaluation workflows and datasets. The difference is how they integrate with production debugging.
Laminar treats datasets, annotation, and evals as extensions of observability rather than a separate workflow. You can go from a failed trace to a curated dataset and then back into evals without switching contexts.
Langfuse has a strong eval story, but its workflow is more strongly tied to prompt iteration and versioning. That’s valuable, but it is not always the fastest path from “we saw a failure” to “we fixed the system.”
Pricing Model: Units vs Data Size
Langfuse Cloud pricing is based on billable units (their definition: Units = Traces + Observations + Scores). That means lots of small spans/observations can add up quickly even if each one is tiny.
Laminar prices by data size ingested (GB). For agent systems with many small spans, size‑based pricing tracks the actual payload rather than the count, which tends to be more predictable at scale.
Architecture: Designed for Speed
Laminar’s stack is tuned for high-throughput agent observability:
- Rust backend
- gRPC ingestion
- ClickHouse for analytics
- Postgres for transactional state
- RabbitMQ for async processing
Langfuse uses a solid web + worker architecture with Postgres, ClickHouse, Redis, and S3-compatible storage. It is a great setup for batch processing and prompt workflows.
Our bias is clear: if we care about real-time trace visibility and low-latency ingestion, Laminar’s architecture is the better fit.
The Practical Choice
Use Langfuse if:
- You are mostly tracking single LLM calls or short, prompt‑centric flows
- You need built-in prompt versioning and caching
- You are earlier in the product lifecycle and iterating on prompt quality
Choose Laminar if:
- You are operating multi-step agents in production (or getting there fast)
- You need real-time trace visibility
- You want SQL-native access to telemetry
- You care about speed and debugging depth at scale
That second list is the world we live in. It is why we built Laminar, and why we believe it is the better platform for serious production agent work.
Try Laminar
If you want to see Laminar in action, start by tracing a single agent workflow. It takes minutes to set up, and the moment you open your first trace tree, the difference becomes obvious.
If you are evaluating observability platforms right now, we would love to help you compare against your exact use case. Reach out and we will show you what Laminar looks like on real production data.
