Monitor AI Workloads Across LLM
Layer and Infrastructure with
Correlated Logs, Metrics, and Traces

Track token usage, latency, and costs alongside your microservices, databases, and GPU clusters.
Handle high-cardinality data at scale with usage-based pricing and span-level alerting for traces.

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Capabilities That Make SigNoz the
Default Choice for AI Companies

Capabilities that AI Companies Need Most

Features that help you debug non-deterministic LLM outputs, control inference costs, track business outcomes, and meet compliance requirements

Span-Level Alerting

Set alerts on specific spans within a trace to isolate internal latency from third-party provider slowness. Configure thresholds on individual service spans rather than entire trace duration, so you only get notified when your code is slow, not when external APIs degrade.

Trace Funnels

Track multi-step workflows like "calls dialed" to "leads qualified" for voice agents, or visualize drop-off rates across your AI agent pipelines to identify where users abandon flows.Read Documentation

MCP Server for Agentic SRE

Enable AI agents to query your telemetry via Model Context Protocol. Agents can debug themselves, create dashboards, or perform root-cause analysis by importing telemetry data directly.Read Documentation

Self-Hosted / BYOC Compliance

Deploy on your infrastructure to meet HIPAA/GDPR compliance requirements. Keep sensitive prompt data on-premise for healthcare, banking, and government contracts.

Monitoring Model Token Usage

Track token usage and costs per model, operation, and user. View cost breakdowns, prompt efficiency scores, and configure budget alerts to optimize spending without sacrificing quality.

Full-Stack Platform Capabilities

Unlike LLM-only observability tools, we correlate AI layer performance with your entire infrastructure - databases, microservices, and application logs.

ClickHouse Architecture

Handle high-cardinality tagging without performance degradation or out-of-memory crashes using columnar storage optimized for analytical queries.

Query Any Field Without Re-Instrumentation

Unlike Langfuse's fixed observation schema, you can track custom reasoning steps, tool calls, or model parameters without code changes.

OpenTelemetry Native

Avoid vendor lock-in with industry standard instrumentation. Switch observability providers without rewriting instrumentation or removing proprietary agents.

Configuration as Code

Manage dashboards and alerts with Terraform to maintain stability during rapid product updates. Version control your observability configuration.

Alert Segmentation for On-Call Health

Define granular alert severity levels instead of blanket alerts that cause on-call burnout. Route notifications dynamically by service, environment, or customer.

Out-of-the-Box Dashboards

Start monitoring immediately with pre-built dashboards for OpenAI, Anthropic, LangChain, database queries, Kubernetes pods, and API latency. Get visibility into your LLM applications, infrastructure, and application performance on day 1.

How SigNoz Compares to
LLM-Only Tools

Feature	SigNoz	Langfuse	LangSmith	Braintrust
LLM Tracing	Full traces with OpenTelemetry	OpenTelemetry-based	Async distributed tracing	Request-level tracing
Production Alerts	Any metric	No alerting	LLM metrics only	LLM metrics only
Prompt Management	Via integrations	Version control with caching	A/B testing built-in	Side-by-side comparison
Evaluation/Scoring	Via integrations	LLM-as-judge, custom evals	Built-in evaluators	Dataset/task/scorer framework
Infra Correlation	Metrics, logs, traces together	LLM-only	LLM-only	LLM-only
Kubernetes/Docker Monitoring	Native support
Database Query Tracking	Built-in
Application Correlation	Cross-service tracing
Dashboards	Advanced query builder	Limited presets	Limited presets	Basic charts

Cost
Comparison

95% Saved

SigNoz vs Langfuse for 1 Billion Spans/Month

We charge based on data size while Langfuse charges per unit count. AI applications generate more spans due to complex agent workflows and tool calls, making unit-based pricing expensive at scale.

Langfuse Core

$60,331/month

Base $29 + $60,302 usage (graduated pricing from 100k to 1B units). Langfuse counts traces, observations, and scores as billable units.

SigNoz Cloud

$300 - $3,000/month

Span sizes typically range from 1 KB to 10 KB depending on your instrumentation and payload complexity. It also includes traces, logs, and metrics with no separate instrumentation.

How SigNoz Compares to
Traditional Tools

Feature	SigNoz	Datadog	Honeycomb	Grafana LGTM
GPU Cluster Economics	Usage-based pricing	Host-based pricing	Usage-based pricing	Usage + user seats
High-Cardinality Tags	No cardinality limits	Warns against "unbounded attributes"	Built for high-cardinality	4 backends, mixed handling
Span-Level Alerting	Native trace alerts	Trace analytics on span attributes	Triggers on any span attribute	Requires metrics-generator
LLM Cost Tracking	25+ providers via LiteLLM	LLM Observability add-on	Anthropic integration only	Cloud only
Trace Funnels	Direct/Indirect Descendant operators	RUM funnel, not APM funnels	Relational queries, not dedicated	TraceQL structural operators
Async Workflows (Span Links)	Supported	Supported	Supported	Tempo native
Self-Hosting	Built on open-standards	SaaS-only	Private Cloud (Enterprise)	Built on open-standards
Unified Backend	Single, ClickHouse	Single backend	Single backend	4 separate backends

4 Pillars of AI-Focused
Observability Architecture

ELASTIC INGESTION AT SCALE

Handle Unpredictable Telemetry Spikes Without Data Loss

Workloads generate extreme ingestion bursts, jumping from hundreds to tens of thousands of records per second in minutes. Traditional observability systems drop data or lag during these spikes. We buffer telemetry with queued retry processors and memory limiters to prevent data loss during traffic bursts.

COST PREDICTABILITY

Pay for Data Volume Ingested, Not Host Count

Companies running large GPU clusters make host-based pricing financially unsustainable. Legacy vendors charge per host and double-charge for ingestion and indexing, leading to surprise bills. We charge based on data volume ingested, not the number of nodes or containers. Set daily rate limits and ingestion quotas to prevent cost overruns.

HIGH-CARDINALITY DATA HANDLING

Tag Every Request Without Crashing Your Metrics Store

Applications tag telemetry with user IDs, model versions, and customer IDs across thousands of nodes. Prometheus and Loki crash with out-of-memory errors under this load. We use ClickHouse's columnar storage to handle high-cardinality data natively, letting you tag aggressively without performance degradation.

FULL-STACK CORRELATION

Correlate LLM Performance with Infrastructure and Application Logs

AI debugging requires connecting LLM latency spikes to database queries, infrastructure metrics, and application logs. Fragmented stacks force manual correlation across multiple tools. We unify logs, metrics, and traces with automatic trace_id correlation. Span links connect async workflows that parent-child tracing cannot represent.

Start Monitoring Your AI Apps in Minutes

Get started in three steps:

ISign up for free SigNoz Cloud account

IIInstall your framework's instrumentation package

IIIAdd two lines to initialize tracing

Your existing application code remains completely untouched while traces start flowing to SigNoz in real-time, giving you instant visibility into every aspect of your LLM operations.

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Simple
usage-based
pricing

Pricing you can trust

Tired of Datadog's unpredictable bills or New Relic's user-based pricing? We're here for you.

Pricing per unit

Retention

Scale of ingestion (per month)

Estimated usage

Subtotal

Traces

$0.3/GB

0GB

100TB

Logs

$0.3/GB

0GB

100TB

Metrics

$0.1/mn samples

100B

Monthly estimate

$49

Calculate your exact monthly billCheck Pricing

Developers
Love
SigNoz

Your data stays where you want

Use SigNoz cloud with your data staying in the US, EU, or India, or self-host.

Cloud

Fully managed, SOC 2-compliant, ideal for teams who want to start quickly without managing infrastructure.

Self-Host

For tighter security & data residency requirements. It is Apache 2.0 open source, built on open standards.

10 million+

OSS Downloads

25k+

GitHub Stars

Join the community GitHub Repository

Every single time we have an issue, SigNoz is always the first place to check. It was super straightforward to migrate - just updating the exporter configuration, basically three lines of code.Karl Lyons
Senior SRE, Shaped

Charlie Shen

Lead DevOps Engineer, Brainfish

I've studied more than 10 observability tools in the market. We eventually landed on SigNoz, which says a lot. Compared to Elastic Cloud, it's a breeze with SigNoz.

Niranjan Ravichandra

Co-founder & CTO, Cedana

Getting started with SigNoz was incredibly easy. We were able to set up the OpenTelemetry collector quickly and start monitoring our systems almost immediately.

Poonkuyilan V

IT Infrastructure Lead, The Hindu

Recently, we configured alerts for pod restarts and were able to quickly identify and resolve the root cause before it escalated. Additionally, SigNoz's tracing capabilities helped us spot unwanted calls to third-party systems, allowing us to optimize our applications.

Avneesh Kumar

VP of Engineering, Mailmodo

We have started saving almost six hours on a daily basis, which we can now invest in other tech debts and backlogs. The best thing about SigNoz is that it's open source. I can go into the source code and look at what's happening. That's a great confidence booster for long-term usage.

Khushhal Reddy

Senior Backend Engineer, Kiwi

SigNoz is something we use daily. If I have ten tabs open, six of them are SigNoz. We used traces and it helped us take 30 seconds down to 3 seconds.