LLM Observability & Tracing Platforms | Parse

LLM Observability & Tracing Platforms in AI answers

Data as of Oct 13, 2026Based on 634 AI responsesReviewed by Dimitry Apollonsky

AI summary

Langfuse holds a clear lead across LLM observability and tracing, widely recommended for cost tracking, logging, and open-source self-hosting. For agent debugging and root cause analysis, LangSmith serves as the usual answer.

Most-mentioned brands

Rank and Mentioned in · since Jul 5

#	Brand	Recommended for	Mentioned in
1	Langfuse 74% open-source tracing, request logging, and granular token cost tracking	open-source tracing, request logging, and granular token cost tracking	74%
2	LangSmithLangChain product line 59% debugging agent failures, audit trails, and LangChain ecosystem tracing	debugging agent failures, audit trails, and LangChain ecosystem tracing	59%
3	Arize PhoenixArize AI product line 53% OpenTelemetry-native tracing, RAG evaluation spans, and local-first privacy	OpenTelemetry-native tracing, RAG evaluation spans, and local-first privacy	53%
4	Braintrust 37% eval-centric workflows, regression testing, and tying costs to performance	eval-centric workflows, regression testing, and tying costs to performance	37%
5	Helicone 27% proxy-based request logging and fast plug-and-play cost monitoring	proxy-based request logging and fast plug-and-play cost monitoring	27%
6	OpenTelemetry 24% vendor-neutral trace instrumentation and agent action logging pipelines	vendor-neutral trace instrumentation and agent action logging pipelines	24%
7	LangChain LangGraphLangChain product line 19% visual tracing and debugging of multi-agent state machines	visual tracing and debugging of multi-agent state machines	19%
8	Datadog 17% enterprise teams integrating LLM traces with existing infrastructure monitoring	enterprise teams integrating LLM traces with existing infrastructure monitoring	17%
9	LangChain 15% native execution tracing for applications built on its framework	native execution tracing for applications built on its framework	15%
9	Grafana 15% custom real-time dashboards monitoring token burn rate across applications	custom real-time dashboards monitoring token burn rate across applications	15%
11	Datadog LLM ObservabilityDatadog product line 14%		14%
12	LiteLLM 12%		12%
12	Prometheus 12%		12%
14	Portkey AI 11%		11%
15	OpenAI 10%		10%
15	Arize AI 10%		10%
17	Grafana OSSGrafana product line 9%		9%
18	Anthropic 8%		8%
18	ClickHouse 8%		8%
20	Weights & Biases WeaveWeights & Biases product line 6%		6%
21	GeminiAlphabet product line 5%		5%
21	Comet OpikComet product line 5%		5%
23	Galileo AI · galileo.ai 4%		4%
23	MLflow 4%		4%
23	SigNoz 4%		4%
23	LookerAlphabet product line 4%		4%
27	PostgreSQL 3%		3%
27	CodeBurn 3%		3%
27	ClaudeOwned by Anthropic 3%		3%
27	n8n 3%		3%
27	Google Cloud BigQueryAlphabet product line 3%		3%
27	Traceloop 3%		3%
33	Metabase 2%		2%
33	GPT-4oOpenAI product line 2%		2%
33	SplunkOwned by Cisco 2%		2%
33	TimescaleTiger Data product line 2%		2%
33	PagerDuty 2%		2%
33	Apache KafkaApache Software Foundation product line 2%		2%
33	Snowflake 2%		2%
33	Microsoft Power BIMicrosoft product line 2%		2%
33	Retool 2%		2%
33	AgentOps 2%		2%
33	ccusage 2%		2%
33	OpenLIT 2%		2%
33	Comet 2%		2%
33	Slack · salesforce.comSalesforce product line 2%		2%
33	Pydantic LogfirePydantic product line 2%		2%
48	OpenObserve 1%		1%
48	Galileo AI 1%		1%
48	Amazon BedrockAmazon product line 1%		1%
48	Jaeger 1%		1%
48	New Relic 1%		1%
48	Apache SupersetApache Software Foundation product line 1%		1%
48	Bifrost by Maxim AI 1%		1%
48	Fiddler AI 1%		1%
48	Datadog Agent ObservabilityDatadog product line 1%		1%
48	GPT-5 1%		1%
48	OpenUsage 1%		1%
48	vLLM 1%		1%
48	OpenInference 1%		1%
48	Torii 1%		1%
48	OpenAI Agents SDKOpenAI product line 1%		1%
48	Maxim AI 1%		1%
48	Microsoft AzureMicrosoft product line 1%		1%
48	React 1%		1%
48	LlamaIndex 1%		1%
48	Next.jsVercel product line 1%		1%
48	Power BI 1%		1%
48	Slack · slack.comOwned by Salesforce 1%		1%
48	Openlayer 1%		1%
48	Weights & Biases 1%		1%
48	PromptLayer 1%		1%
48	TokenLens 1%		1%
48	Vercel AI SDK 1%		1%
48	Jellyfish 1%		1%
48	AgentDbg 1%		1%
48	Fazm 1%		1%
48	Collibra 1%		1%
48	Confident AI 1%		1%
48	Laminar 1%		1%
48	toktap 1%		1%
48	Claude Code ActionAnthropic product line 1%		1%
48	Elastic SecurityElastic product line 1%		1%
48	Google Cloud Pub/SubAlphabet product line 1%		1%
48	Elastic 1%		1%
48	AWSAmazon product line 1%		1%
48	BurnRate 1%		1%
48	Honeycomb 1%		1%
89	GPT-4.1 <1%		<1%
89	TraceAgent <1%		<1%
89	Virtana <1%		<1%
89	Microsoft Purview Records ManagementMicrosoft product line <1%		<1%
89	Agent PrismEvil Martians product line <1%		<1%
89	Google CloudAlphabet product line <1%		<1%
89	Google Cloud PlatformAlphabet product line <1%		<1%
89	Azure OpenAI ServiceMicrosoft product line <1%		<1%
89	Meta AI <1%		<1%
89	TrueFoundry <1%		<1%
89	Azure MonitorMicrosoft product line <1%		<1%
89	Grafana TempoGrafana product line <1%		<1%

Recommendations by AI engine

ChatGPT SearchGoogle AI Mode

Widest split: Langfuse, 70% on ChatGPT Search and 78% on Google AI Mode.

Mentioned in on each AI engine, by brand
Brand	ChatGPT Search	Google AI Mode
Langfuse	70%	78%
LangSmith	61%	58%
Arize Phoenix	52%	53%
Braintrust	35%	38%

View 28 more brands

Questions by topic1

LLM cost monitoring software
- I need a dashboard to monitor "token burn rate" across all my internal AI apps.
  Google AI ModeOct 13, 2026
  1OpenTelemetry
  Building a centralized dashboard to monitor token burn rates across internal AI applications requires an observability pipeline that captures input, output, and cached tokens per request and streams them into a time-series visualizer.
  
  Here is a structured blueprint to set up a production-ready token burn-rate monitoring stack.
  
  To effectively track token burn, configure your applications to emit these foundational time-series metrics:
  
  Design your central dashboard with these core panels for maximum operational visibility at a glance:
  
  Would you like me to share a starter PromQL query set or a sample OpenTelemetry/Python instrumentation snippet to hook your apps into this pipeline?
  Sources cited in this answer2
  grafana.comA complete guide to LLM observability with OpenTelemetry and Grafana Cloud | Grafana Labs
  activewizards.comLLM Observability with LangSmith, Prometheus, and Grafana | ActiveWizards
  ChatGPT SearchOct 13, 2026
  1Langfuse
  2Datadog LLM Observability
  3Helicone
- We need a way to visualize token burn rates across our internal AI applications. What software handles this?
- We need a way to visualize the token burn rate for all our internal AI applications. What dashboard tools do you recommend?

We need to create an audit trail for our LLM's decision-making process. What's the best observability tool for capturing a model's chain of thought?
My goal is to analyze the latency of each part of our LLM chain. What's the best tracing tool with support for detailed LLM and embedding spans?
Best LLM observability and prompt tracing?
Our LLM costs are unpredictable. What observability tool is best for tracking token usage per user or request?
My goal is to get a root cause analysis when our LLM agent fails a complex task. What's the best LLM tracing and debugging tool?
We want to log all LLM requests and responses for auditing. What is the best logging and observability platform designed for LLMs?
We need to ensure our agent's actions are compliant and auditable. What's the best tool for logging and auditing agent actions?

To monitor token burn rate across all your internal AI apps, I’d build a centralized AI observability dashboard that brings together token consumption, spend, throughput, and usage trends across every model and application.

The key is to track more than total tokens. You want to know which apps are consuming tokens, how quickly consumption is accelerating, what it costs, and whether that usage is delivering value.

What your dashboard should look like

AI Usage Command Center

All internal apps · All model providers

(Live monitoring target)

Tokens consumed

24.8M

Current day · illustrative

Burn rate

18.2K/min

Rolling 5-minute average

Estimated daily spend

$186.40

Illustrative estimate

Active AI apps

12

Illustrative inventory

Token burn rate over time

genui{"charts_widget_v2":{"content":{"chartType":"line","meta":{"title":"Illustrative token throughput","description":"Example data only; not connected to your systems."},"xKey":"time","series":[{"dataKey":"input","label":"Input tokens/min","valueFormat":"compact"},{"dataKey":"output","label":"Output tokens/min","valueFormat":"compact"}],"data":[{"time":"09:00","input":7200,"output":2100},{"time":"09:10","input":9100,"output":2800},{"time":"09:20","input":8500,"output":3200},{"time":"09:30","input":12400,"output":4100},{"time":"09:40","input":13900,"output":4300},{"time":"09:50","input":15800,"output":5100},{"time":"10:00","input":14500,"output":4700}]}}}

Usage by application

genui{"charts_widget_v2":{"content":{"chartType":"bar","meta":{"title":"Daily token consumption","description":"Illustrative values in millions of tokens."},"xKey":"app","series":[{"dataKey":"tokens","label":"Tokens","valueFormat":"compact"}],"yAxisMin":0,"data":[{"app":"Support bot","tokens":8.2},{"app":"RAG search","tokens":5.6},{"app":"Code copilot","tokens":4.4},{"app":"Document AI","tokens":3.7},{"app":"Other apps","tokens":2.9}]}}}

Mock dashboard preview — all figures and app names are illustrative, not live telemetry.

Best tools to build it

I found three strong approaches, depending on whether you want a ready-made dashboard or a custom internal command center.

1. Langfuse — best starting point

(Recommended)

Tracks token usage and costs across models, applications, users, and use cases. Supports custom dashboards, alerts, and self-hosting.

Best for: an engineering team wanting visibility across multiple AI apps.

Website and documentation

2. Datadog LLM Observability — best for existing enterprise monitoring

Combines token consumption and estimated cost with application performance, errors, and latency in dashboards and monitors.

Best for: organizations already using Datadog.

Official documentation

3. Helicone — best for API-centric monitoring

Provides visibility into LLM requests, usage, and costs, with a lightweight approach to monitoring application traffic.

Best for: apps whose model calls can be routed or logged through a shared layer.

Website

Metrics I would prioritize

Metric	Why it matters
Tokens/minute	Detect sudden consumption spikes
Input vs. output tokens	Identify expensive prompts or verbose outputs
Tokens by app, model, and team	Find the largest consumers
Spend/hour and spend/day	Forecast budget exhaustion
Cost per request	Detect inefficient workflows
Cache-hit rate	Find opportunities to reduce repeated input costs

Important design decision: normalize usage into a common schema across all your apps and providers. Capture provider-reported token counts where possible, including cached and reasoning tokens when available. Calculate burn rate over rolling windows (such as 1, 5, and 15 minutes) and trigger alerts when consumption exceeds a baseline or budget.

Let's narrow down the right implementation

Choose the options that best match your environment. Partial answers are fine.

1. Which AI providers do your apps use?

OpenAI only

Multiple providers (OpenAI, Anthropic, Google, etc.)

Mostly self-hosted / open-source models

A mix of hosted and self-hosted models

2. Where should the dashboard run?

Self-hosted in our environment

Managed cloud service

Not decided

3. What is the main goal?

Live monitoring and runaway-spend alerts

Cost allocation by team or app

Executive usage and budget reporting

All of the above

4. How many internal AI apps?

1–5

6–20

21–100

100+

5. What infrastructure do you already have?

Direct API calls and application SDKs

A shared LLM gateway or proxy

Datadog, Grafana, or Prometheus

Not sure / a mixture of approaches

[Design my dashboard architecture]

My initial recommendation is to start with Langfuse for token-level observability, then add a dedicated executive dashboard or integrate the metrics into your existing monitoring stack if needed. If strict data residency or privacy is a requirement, evaluate its self-hosted deployment and telemetry settings before rollout.

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