Free
$0
- 1 GB data with no overage, $5 one-time Signals credit, 7-day retention, 1 project, 1 seat, community support
Laminar AI: Agent Observability Pricing, Pros & Cons
Founded 2024|Free tier, flat monthly plans with data overage, Signals billed by usage, Enterprise custom
What is Laminar?
Laminar (lmnr) is an open-source observability platform for AI agents, licensed under Apache 2.0 and backed by Y Combinator. It captures LLM calls, tool calls, sub-agents, tokens, and cost from each agent run and shows them as a readable transcript, with browser session recording for browser agents.
Its main feature is Signals, which analyzes every agent run to surface failure modes you didn't define in advance and clusters similar failures into patterns. Signals run on flow-1, a model Laminar trained for trace analysis, and bill separately by the tokens used to analyze your traces. Error clusters can become eval datasets, and coding agents can query all trace data with SQL through Laminar's CLI and MCP server.
Compared with broader AI observability tools, Laminar focuses on debugging and evaluation, so prompt management and model routing aren't part of the product and prompt versions and gateway traffic stay in other tools. Self-hosting runs on Docker or Helm on AWS and GCP, and Enterprise adds on-premises deployment.
Key features
Core capabilities this platform advertises.
- Agent tracing with a readable transcript view
- Signals for automatic failure detection and clustering
- Evals and datasets built from production traces
- Full SQL access through the editor, CLI, and MCP
Strengths and tradeoffs
What this tool does well, and the limitations to keep in mind.
Pros
- Apache 2.0 license with Docker and Helm self-hosting
- Signals surface failure modes without predefined rules
- Every plan includes evals, datasets, SQL access, and the agent debugger
Cons
- No prompt management, so prompt versions live in a separate tool
- No LLM gateway, so routing, failover, and spend limits need another product
Plans & pricing
What's included in each plan, and how the tiers compare.
Starter
$30
Per month
- 3 GB data, then $2/GB, Signals billed by usage, 30-day retention, unlimited projects and seats, email support
Pro
$150
Per month
Common use cases
Teams building long-running AI agents, including browser and coding agents, who need to debug agent runs at scale
- Debugging long-running browser and coding agents
- Finding failure patterns across thousands of agent runs
- Turning recurring failures into eval datasets
Using Laminar with Respan
Respan covers the tracing and evaluation work Laminar does, and adds prompt management and the AI router on the same platform. A failure found in a trace becomes a dataset, a fix gets tested against real data, and the new prompt version ships without a redeploy, all in one product.
- Every LLM call, tool run, and agent turn in one nested trace
- Datasets built straight from production logs
- Experiments that test a prompt or model change against real cases
- Prompt versions that ship without a code deploy
- Evals on live production traffic, linked to the trace behind each score
- One API for 1,000+ models with automatic fallbacks
Go from failing trace to proven fix in one platform with Respan
10KFree traces/mo
1,000+Models
Best Laminar alternatives & competitors
Top companies in Observability, Prompts & Evals you can use instead of Laminar.
Compare Laminar
Side-by-side comparisons with other tools in this category.
Best integrations for Laminar
Companies from adjacent layers in the AI stack that work well with Laminar.
