LLM Observability & Tracing Platforms | Parse

LLM Observability & Tracing Platforms in AI answers

Data as of Oct 15, 2026Based on 650 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 60% debugging agent failures, audit trails, and LangChain ecosystem tracing	debugging agent failures, audit trails, and LangChain ecosystem tracing	60%
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 20% visual tracing and debugging of multi-agent state machines	visual tracing and debugging of multi-agent state machines	20%
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%
10	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%
13	Portkey AI 12%		12%
14	Prometheus 12%		12%
15	OpenAI 10%		10%
16	Arize AI 10%		10%
17	Grafana OSSGrafana product line 9%		9%
18	Anthropic 8%		8%
19	ClickHouse 8%		8%
20	Weights & Biases WeaveWeights & Biases product line 6%		6%
21	GeminiAlphabet product line 5%		5%
22	Comet OpikComet product line 5%		5%
23	Galileo AI · galileo.ai 4%		4%
24	MLflow 4%		4%
25	SigNoz 4%		4%
26	LookerAlphabet product line 4%		4%
27	PostgreSQL 3%		3%
28	CodeBurn 3%		3%
29	ClaudeOwned by Anthropic 3%		3%
30	n8n 3%		3%
31	Google Cloud BigQueryAlphabet product line 3%		3%
32	Traceloop 2%		2%
33	Metabase 2%		2%
34	GPT-4oOpenAI product line 2%		2%
35	SplunkOwned by Cisco 2%		2%
36	TimescaleTiger Data product line 2%		2%
37	PagerDuty 2%		2%
38	Apache KafkaApache Software Foundation product line 2%		2%
39	Snowflake 2%		2%
40	Microsoft Power BIMicrosoft product line 2%		2%
41	Retool 2%		2%
42	OpenLIT 2%		2%
43	AgentOps 2%		2%
44	ccusage 2%		2%
45	Comet 2%		2%
46	New Relic 2%		2%
47	Slack · salesforce.comSalesforce product line 2%		2%
48	Pydantic LogfirePydantic product line 2%		2%
49	OpenObserve 1%		1%
50	Galileo AI 1%		1%
51	Amazon BedrockAmazon product line 1%		1%
52	Jaeger 1%		1%
53	Apache SupersetApache Software Foundation product line 1%		1%
54	Bifrost by Maxim AI 1%		1%
55	Fiddler AI 1%		1%
56	Datadog Agent ObservabilityDatadog product line 1%		1%
57	GPT-5 1%		1%
58	OpenUsage 1%		1%
59	vLLM 1%		1%
60	OpenInference 1%		1%
61	Torii 1%		1%
62	OpenAI Agents SDKOpenAI product line 1%		1%
63	Maxim AI 1%		1%
64	Microsoft AzureMicrosoft product line 1%		1%
65	React 1%		1%
66	LlamaIndex 1%		1%
67	Next.jsVercel product line 1%		1%
68	Power BI 1%		1%
69	Slack · slack.comOwned by Salesforce 1%		1%
70	Openlayer 1%		1%
71	Weights & Biases 1%		1%
72	PromptLayer 1%		1%
73	TokenLens 1%		1%
74	Elastic 1%		1%
75	Vercel AI SDK 1%		1%
76	Jellyfish 1%		1%
77	AgentDbg 1%		1%
78	Fazm 1%		1%
79	Collibra 1%		1%
80	Confident AI 1%		1%
81	Laminar 1%		1%
82	toktap 1%		1%
83	Claude Code ActionAnthropic product line 1%		1%
84	Elastic SecurityElastic product line 1%		1%
85	Google Cloud Pub/SubAlphabet product line 1%		1%
86	AWSAmazon product line 1%		1%
87	BurnRate 1%		1%
88	Honeycomb 1%		1%
89	GPT-4.1 <1%		<1%
90	TraceAgent <1%		<1%
91	Virtana <1%		<1%
92	Microsoft Purview Records ManagementMicrosoft product line <1%		<1%
93	Agent PrismEvil Martians product line <1%		<1%
94	IBM <1%		<1%
95	Google CloudAlphabet product line <1%		<1%
96	Google Cloud PlatformAlphabet product line <1%		<1%
97	Azure OpenAI ServiceMicrosoft product line <1%		<1%
98	Meta AI <1%		<1%
99	TrueFoundry <1%		<1%
100	Azure MonitorMicrosoft product line <1%		<1%

Recommendations by AI engine

ChatGPT SearchGoogle AI Mode

Widest split: OpenTelemetry, 35% on ChatGPT Search and 12% on Google AI Mode.

Mentioned in on each AI engine, by brand
Brand	ChatGPT Search	Google AI Mode
Langfuse	71%	78%
LangSmith	62%	58%
Arize Phoenix	53%	54%
Braintrust	36%	39%
Helicone	29%	25%
OpenTelemetry	35%	12%
LangChain LangGraph	24%	16%
Datadog	17%	17%
LangChain	20%	11%
Grafana	18%	12%

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 15, 2026
  1Langfuse
  2Arize Phoenix
  3Braintrust
  4Prometheus
  5Grafana
  6OpenTelemetry
  7Datadog
  8New Relic
  9Elastic
  I can't build or provide a live interactive dashboard to monitor internal data across your private apps, as I don't have access to your internal systems, private application metrics, or authentication environments.
  
  However, if you are looking to set up tracking or centralized logging for token usage and costs across multiple AI applications, here are a few standard industry approaches and open-source/commercial tools you can use to build your own dashboard:
  
  LLM Observability Platforms : Tools like Langfuse, Arize Phoenix , or Braintrust allow you to aggregate token counts, latency, and costs across different applications and providers via SDKs.
  
  Prometheus & : You can instrument your internal applications to emit custom metrics (e.g., labeled by app name and model) and scrape them into , then visualize the burn rate using .
  
  ChatGPT SearchOct 15, 2026
  1Langfuse
  2Helicone
  3LangSmith
- 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?

ai_tokens_consumed_total

To monitor token burn rate across all your internal AI apps, I’d build a centralized LLM observability and cost dashboard that aggregates usage across every model provider and application, rather than relying on individual provider consoles.

The key is to track more than total tokens: you want to know which apps are burning tokens, how quickly costs are accumulating, which models are responsible, and when usage is likely to exceed budget.

What your dashboard should show

AI USAGE CONTROL CENTER · MOCKUP

Tokens today

128.4M

Input + output

Projected daily spend

$486

Illustrative estimate

Burn rate

$20.25/hr

Current average

Apps reporting

12 / 12

Illustrative coverage

Token consumption by application

Conceptual example only. These are not live measurements from your systems.

Best tools to build this

These are the options I’d shortlist based on the goal of aggregating usage across multiple AI applications.

1. Langfuse

(Best starting point)

Centralized token and cost tracking, model and application breakdowns, dashboards, and threshold alerts. Supports self-hosting for teams that want control over their infrastructure.

Website ·

2. Helicone

Worth evaluating if you want centralized request monitoring through an AI gateway, with model usage, request metadata, and cost visibility.

Website

3. LangSmith

Strong option if your internal apps use LangChain or LangGraph, particularly when you need token accounting alongside traces and debugging.

Website ·

How I’d architect your monitoring system

Your internal AI applications

Agents · Chatbots · RAG · Background jobs

Shared instrumentation layer or AI gateway

Capture every model call, app ID, model, token count, and cost

Central telemetry and metrics store

Normalize providers · Aggregate by minute · Attribute to teams

Live dashboard

Alerts & budgets

For a heterogeneous environment, I’d standardize telemetry around OpenTelemetry's GenAI conventions where supported, so the dashboard is not locked to one model vendor.

Metrics I would prioritize

Burn rate: tokens per second or minute, plus dollars per minute and hour.
Forecast: projected daily and monthly spend based on recent usage.
App attribution: usage by application, team, environment, and owner.
Model efficiency: input vs. output tokens, cached tokens, reasoning tokens where available, and cost per request.
Anomaly alerts: sudden increases in token rate, retry loops, and unusual per-app spending.
Coverage: applications reporting telemetry versus the total inventory.

One important distinction: measure actual token usage from model responses when available, and use model-specific pricing to estimate cost where direct cost data isn't available. Make missing telemetry visible so the dashboard doesn't give you false confidence.

Let's narrow down the right implementation

A few choices will determine the best architecture and toolset.

1. Which model providers do your apps use?

OpenAI only

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

Mostly self-hosted models

A mix of cloud and self-hosted models

2. Where must the dashboard run?

Self-hosted / private infrastructure

Managed cloud service

Either is fine

3. What approach do you prefer?

Build a custom dashboard tailored to our needs

Configure an existing observability platform

Centralize calls through a gateway

Recommend the best option

4. How many apps need monitoring?

1–10

11–50

51–200

200+

5. How fresh must the data be?

Near real time (seconds to a minute)

Every 5–15 minutes

Hourly or daily reporting is enough

[Recommend my dashboard architecture]

LLM Observability & Tracing Platforms in AI answers

Most-mentioned brands

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Recommendations by AI engine

Questions by topic1

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