Laminar vs SigNoz
Both are observability & tracing tools. Here is how they actually differ on price, billing model and deployment.
Laminar
An open-source, OpenTelemetry-native tracing and eval platform for AI agents, written in Rust. The only tool here you can self-host in full - but its billing unit, "Signals" measured in tokens spent reading traces, is genuinely hard to forecast.
SigNoz
An open-source Datadog alternative that handles LLM telemetry as part of full-stack observability rather than as a separate product. Free to license, but you are running ClickHouse - the cost is infrastructure and ops, not fees.
| Laminar | SigNoz | |
|---|---|---|
| Category | Observability & Tracing | Observability & Tracing |
| Our rating | 4/5 | 4/5 |
| Starting price | $30/mo | $19/mo (startup) or $49/mo |
| Billing meter | span | gb-ingested |
| Free plan | Yes | Yes |
| Free self-hosting | Yes, free | Yes, free |
| Best for | Teams building AI agents - especially browser agents - who want OpenTelemetry-native tracing they can self-host completely and don't mind an unusual billing model | Teams that want one observability backend for their whole stack rather than a separate LLM tool, are cost-sensitive relative to Datadog, and either have ClickHouse operational capacity or will pay for cloud. |
Our verdict on Laminar
Laminar is the one platform here you can genuinely self-host end to end, and for a browser-agent or long-running-agent stack it's the most purpose-built option, with real credibility from OTel's co-creator as an angel and Browser Use documenting it as their integration. Two honest reservations. It's the youngest and smallest, and its billing - data GB plus Signals measured in tokens spent reading traces - is the hardest to forecast in the category. Self-host is the escape hatch from the billing puzzle, but the exact feature gap versus cloud isn't clearly documented, so verify it before you commit.
Full Laminar review →Our verdict on SigNoz
SigNoz is the right answer to a question many teams are actually asking - not "which LLM observability tool" but "how do I see my LLM calls alongside everything else without paying Datadog prices." It handles LLM telemetry as one workload within full-stack observability, giving token-level tracing, per-model cost attribution and prompt latency breakdown, all correlated with the databases and microservices underneath. It is genuinely open source and self-hostable with no feature gating. Two honest caveats. Self-hosted free covers the license and nothing else - you are operating ClickHouse, which is resource-hungry, and you inherit the on-call burden of your own monitoring stack going down. And there is no evaluation capability whatsoever, so if you need LLM-as-judge scoring, datasets or regression testing, SigNoz is half your stack and you will pair it with something like DeepEval or promptfoo.
Full SigNoz review →Frequently Asked Questions
What is the main difference between Laminar and SigNoz?
Laminar: Teams building AI agents - especially browser agents - who want OpenTelemetry-native tracing they can self-host completely and don't mind an unusual billing model SigNoz: Teams that want one observability backend for their whole stack rather than a separate LLM tool, are cost-sensitive relative to Datadog, and either have ClickHouse operational capacity or will pay for cloud. Both sit in Observability & Tracing, so the decision usually comes down to billing model and deployment rather than raw capability.
Which is cheaper, Laminar or SigNoz?
It depends entirely on your workload shape, because they meter differently - Laminar bills on span and SigNoz bills on gb-ingested. Published starting prices are $30/mo and $19/mo (startup) or $49/mo respectively, but those numbers are not comparable until you apply them to the same traffic. Use our cost calculator to model both against your own request volume and span count.
Can I self-host Laminar or SigNoz?
Laminar: Yes, free. SigNoz: Yes, free. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.