The best platforms for monitoring LLM traffic and API usage across teams are gateway or proxy tools such as Portkey and Helicone. They capture every request across providers and attribute tokens, cost and latency to users, teams or virtual keys. For deeper tracing, evaluation and drift detection, add Langfuse, Arize AI, LangSmith or Datadog LLM Observability. If you instead need to monitor how ChatGPT, Claude and Perplexity mention your brand, use an AI visibility platform such as Sight AI.
This review covers two related categories. The first is AI visibility monitoring, which tracks how LLMs talk about your brand in their outputs. The second is LLM observability, which covers performance, latency, cost and quality tracking for teams building with LLMs. Understanding ai brand visibility tracking tools and how ai models mention brands has become as important as traditional SEO for many growth-focused teams.
Each platform was assessed on five criteria: monitoring depth, quality of actionable insights, ease of integration, pricing transparency, and real-world utility for marketers, founders and technical teams.
Quick comparison
| Platform | Category | Integration | Strongest at | Open source |
|---|---|---|---|---|
| Sight AI | AI visibility | No code | Brand mentions and prompts across AI models | No |
| Langfuse | Observability | SDK / OpenTelemetry | Tracing, prompt management, self-hosting | Yes |
| Arize AI | Observability | SDK (Phoenix) | Embedding drift, evaluation, RAG | Phoenix only |
| Helicone | Observability | Proxy (URL change) | Cost, latency and per-user usage | Yes |
| W&B Weave | Observability | Python SDK | Experiment-to-production lineage | Partly |
| Datadog LLM Observability | Observability | Datadog agent / SDK | Correlation with APM and infrastructure | No |
| LangSmith | Observability | Native LangChain / SDK | Agent run trees, datasets | No |
| Patronus AI | Evaluation | API / SDK | Hallucination and compliance scoring | No |
| Portkey | Gateway + observability | Gateway (URL change) | Multi-provider routing, virtual keys, team budgets | Gateway yes |
1. Sight AI
Best for: Marketers, founders and agencies tracking brand presence across AI-generated answers
Sight AI is an AI visibility platform. It monitors how your brand appears across ChatGPT, Claude, Perplexity and other leading AI models. It also includes content generation and indexing tools to actively improve your presence in AI-generated answers.
Where This Tool Shines
Sight AI connects brand visibility tracking directly to content action, which gives it a distinct position in the monitoring landscape. Most observability tools tell you what is happening technically. Sight AI tells you three things: what AI models are saying about your brand, which competitor names appear alongside yours, and what content you need to publish to shift those outcomes.
Its prompt-level tracking is particularly valuable. Instead of vague brand mention counts, Sight AI shows which specific queries trigger mentions of your brand versus a competitor. You can then prioritize content creation around the gaps that matter most.
Key Features
AI Visibility Score: Tracks brand mention frequency and sentiment across 6+ AI platforms and rolls them into a unified score you can benchmark over time.
Prompt-Level Tracking: Shows exactly which user queries trigger your brand to appear in AI-generated responses.
13+ AI Content Agents: Specialized agents generate SEO- and GEO-optimized articles, listicles and guides.
IndexNow Integration: Automatically submits new content to search engines for faster discovery.
Competitive Monitoring: Tracks how rival brands appear in AI responses so you can benchmark your visibility against competitors.
Best For
Marketers, growth-focused founders and agencies who treat AI search presence as a strategic channel. Sight AI fits teams whose goal is to understand and improve how AI models represent their brand. It is not a tool for debugging LLM application code. For a structured approach to measuring results, see this 6-step framework for measuring AI visibility performance.
Pricing
A free tier is available. Visit trysight.ai for current plan details.
2. Langfuse
Best for: Engineering teams wanting open-source LLM tracing with self-hosting flexibility
Langfuse is an open-source LLM observability platform. It offers detailed tracing, prompt management and evaluation for teams building LLM-powered applications.
Where This Tool Shines
Langfuse has a strong following among developers, largely because of its open-source foundation. Teams can self-host the entire platform to keep sensitive data on their own infrastructure. That matters for regulated industries and privacy-conscious organizations.
Its trace and span visualization is detailed. When you debug a multi-step LLM chain, you can see exactly which step introduces latency or quality issues.
Key Features
Open-Source with Self-Hosting: The full codebase is on GitHub, so you can deploy on your own infrastructure.
Trace and Span Visualization: Shows inputs, outputs, latency and cost at every step of a chain.
Prompt Versioning: Manage and version prompts centrally, and track how each change affects quality metrics.
Built-In Evaluation Framework: Run custom scoring functions against logged traces.
Broad Integrations: Supports OpenAI, LangChain, LlamaIndex and OpenTelemetry.
Best For
Engineering teams that want strong observability without vendor lock-in and have the capacity to self-host.
Pricing
The self-hosted version is free and open source. Cloud plans include a free tier, with paid plans for higher volumes.
3. Arize AI
Best for: Enterprise teams needing deep ML and LLM observability with embedding analysis
Arize AI is an enterprise ML and LLM observability platform. It includes Phoenix open-source tracing, embedding drift detection and evaluation tools for production AI systems.
Where This Tool Shines
Arize grew out of traditional ML monitoring, and that experience shows in its LLM features. Embedding drift detection is the standout. It flags when the distribution of inputs to your application shifts in ways that could degrade output quality, ideally before users notice.
The Phoenix open-source library is a free entry point for tracing and evaluation. The Arize cloud platform adds enterprise dashboards, alerting and collaboration on top.
Key Features
Phoenix Open-Source Library: Free LLM tracing and evaluation that works without the Arize cloud platform.
Embedding Drift Detection: Catches shifts in input distribution before they cause quality regressions.
Automated Hallucination Evaluation: Built-in evaluators score responses for hallucination and relevance.
RAG Pipeline Monitoring: Tracks retrieval quality alongside generation quality.
Enterprise Dashboards and Alerting: Alerts on quality, latency and cost metrics.
Best For
Enterprise ML teams that need real-time drift detection and sophisticated evaluation, and can accept a steeper learning curve.
Pricing
Phoenix is free. Arize cloud has a free tier, and enterprise pricing is available on request.
4. Helicone
Best for: Teams wanting instant LLM monitoring with minimal setup effort
Helicone is a lightweight LLM monitoring platform. It uses a proxy to log every request and track cost and latency, with almost no setup.
Where This Tool Shines
The proxy-based architecture is Helicone's biggest selling point. You change one base URL in your API calls instead of instrumenting your code, and Helicone captures every request.
Its dashboards break down cost by user, model and endpoint in real time. This helps teams catch runaway usage before it becomes a billing surprise.
Key Features
One-Line Proxy Integration: A single URL change, with no SDK required.
Real-Time Cost and Latency Dashboards: Live token usage, cost and latency for every call.
Request and Response Logging: Searchable logs you can filter by user, model and custom properties.
User-Level Analytics: Segment usage and cost by user or team.
Multi-Provider Support: Works with OpenAI, Anthropic and other major providers.
Best For
Startups and product teams that need cost and performance visibility quickly.
Pricing
A free tier is available, and paid plans unlock higher volumes and advanced features. Check Helicone's pricing page for current request limits.
5. Weights & Biases (W&B Weave)
Best for: ML teams extending existing W&B workflows into LLM application monitoring
Weights & Biases is an established MLOps platform. Its Weave extension adds LLM tracing, prompt versioning and evaluation, all connected to W&B experiment tracking.
Where This Tool Shines
If your team already uses W&B, Weave slots in without a separate platform. Weave links prompt experiments, evaluations and production traces. This lineage helps teams that move often between research and production.
The collaborative dashboards let team members share traces, annotate results and compare experiments in one workspace.
Key Features
Weave LLM Tracing Layer: Purpose-built tracing through a clean Python API.
Prompt Versioning with Lineage: Records each prompt change alongside the experiment results it produced.
W&B Ecosystem Integration: Connects to the model registry and artifact management.
Collaborative Dashboards: Shared review and annotation of traces.
Custom Evaluation Pipelines: Run custom scoring functions against logged traces.
Best For
ML teams already invested in the W&B ecosystem.
Pricing
Free for individual users. Team and enterprise plans use usage-based pricing.
6. Datadog LLM Observability
Best for: Enterprise engineering teams with existing Datadog infrastructure monitoring
Datadog LLM Observability brings LLM traces into the Datadog platform. You see them in the same place as your infrastructure metrics and application performance data.
Where This Tool Shines
Datadog's main value is unification. With LLM traces next to APM data, logs and infrastructure metrics, you can link an LLM latency spike to a database slowdown or a recent deployment. That kind of correlation is hard to replicate across separate tools.
Datadog now also offers automated quality evaluation. It is not as deep as specialized tools like Patronus, but it covers the fundamentals.
Key Features
Unified Observability: LLM traces sit alongside APM data, logs and infrastructure metrics.
Cost Attribution and Token Tracking: Breaks down tokens and cost by service, endpoint or team.
Automated Quality Evaluation: Scores responses for relevance and runs basic safety checks.
Alerting on LLM-Specific Metrics: Monitors latency, error rates and cost anomalies.
Multi-Provider Support: Integrates with OpenAI, Anthropic, Amazon Bedrock and more.
Best For
Platform teams already running Datadog. It is less compelling for teams outside the Datadog ecosystem, given the cost structure.
Pricing
Sold as an add-on to Datadog plans and priced by spans ingested. Contact Datadog for a quote.
7. LangSmith
Best for: Teams building with LangChain or LangGraph who need deep native debugging
LangSmith is LangChain's developer platform for debugging, testing and monitoring LLM applications. It offers native support for LangChain chains, agents and LangGraph workflows.
Where This Tool Shines
For LangChain and LangGraph users, LangSmith's native integration is hard to beat. Its run tree visualization shows what happened at each decision point of a multi-step agent run.
Annotation queues give you a structured way to collect human feedback, which then feeds into evaluation datasets.
Key Features
Native LangChain and LangGraph Tracing: Tracing works with almost no setup in LangChain-based applications.
Run Tree Visualization: Shows each step of a multi-step agent run, with inputs, outputs and intermediate results.
Dataset Management and Evaluation: Build evaluation datasets from production traces and run automated evaluations.
Prompt Playground: Compare outputs across prompt variants side by side.
Human Feedback Annotation Queues: Structured workflows for reviewing and labeling outputs.
Best For
Teams whose primary framework is LangChain or LangGraph.
Pricing
A free developer tier is available. Plus and Enterprise tiers add higher volumes and team collaboration features.
8. Patronus AI
Best for: Enterprise teams with strict compliance and hallucination detection requirements
Patronus AI is an LLM evaluation platform. It focuses on hallucination detection, safety scoring and compliance checks for enterprise AI deployments.
Where This Tool Shines
Patronus is built for the evaluation side of monitoring. Its hallucination scoring goes deeper than general observability tools. That matters most in financial services, healthcare and legal, where inaccurate outputs carry real risk.
You can also build custom evaluators for domain-specific quality checks.
Key Features
Automated Hallucination Detection: Scores outputs for factual accuracy at scale.
Toxicity and Policy Compliance Evaluation: Flags outputs that violate safety or policy standards.
Custom Evaluator Creation: Encode your organization's own quality standards.
Continuous Monitoring with Alerting: Alerts you when quality falls below set thresholds.
Enterprise Compliance Reporting: Produces audit-ready reports.
Best For
Regulated enterprises deploying LLMs in customer-facing or high-stakes contexts.
Pricing
Custom enterprise pricing. Contact Patronus AI for details.
9. Portkey
Best for: Teams wanting unified LLM routing, cost management and observability in one gateway
Portkey is an AI gateway that sits between your application and your LLM providers. It combines routing across providers with built-in observability, caching, fallbacks and cost management.
Where This Tool Shines
Portkey actively manages LLM traffic instead of only observing it. It routes requests to the right provider, falls back to another provider during outages, and caches semantically similar requests to cut costs. You get monitoring as a byproduct of traffic management.
Virtual keys give each team member or service its own rate limits and permissions. This avoids sharing raw API keys across a growing team.
Key Features
Unified Gateway for 200+ LLM Providers: Routes requests across providers, with automatic fallback logic.
Built-In Observability: Logs requests and tracks cost and latency at the gateway, with no extra instrumentation.
Semantic Caching: Avoids paying twice for near-duplicate requests.
Guardrails and Output Validation: Applies input and output rules at the gateway.
Virtual Key Management: Issues keys with granular limits for auditable access control across teams.
Best For
Teams running multiple providers that want cost control, reliability and observability handled at the infrastructure level.
Pricing
A free tier is available. Paid plans scale by request volume and features.
How to monitor LLM traffic and API usage across teams
Most searchers comparing these tools want team-level visibility into shared LLM spend. A practical setup has five steps:
- Centralize traffic. Route every provider call through one gateway or proxy, such as Portkey or Helicone, so no team bypasses monitoring with a raw API key.
- Tag every request. Attach metadata such as team, feature, environment and user ID. Without tags, you cannot attribute cost or set per-team limits.
- Issue scoped keys. Give each team or service its own virtual key with rate limits and a budget cap.
- Alert on anomalies. Set alerts for p95 latency, error rate (429s, 5xx and timeouts), and daily cost relative to a trailing baseline. A sudden 2–3x jump in tokens per request usually signals a prompt or loop bug.
- Layer tracing and evaluation. Add Langfuse, LangSmith, Arize or Datadog for step-level traces, quality scores and drift detection on the features that matter most.
The trade-off is between setup effort and depth. Gateways deploy in minutes but see only requests and responses. SDK-based tracing takes more work but shows retrieval steps, tool calls and agent decisions.
Which Platform Is Right for You
The LLM monitoring landscape in 2026 has settled into two distinct categories. The right platform depends on which problem you are trying to solve.
If your priority is understanding how ChatGPT, Claude and Perplexity represent your brand in their answers, Sight AI is the only platform in this list built specifically for that job. It connects visibility tracking to content creation and indexing, so you can act on what you find.
For teams building LLM-powered applications, the choice depends on stack and priorities:
- Langfuse and Arize Phoenix are strong open-source options if you want to self-host.
- Helicone and Portkey win on setup speed and multi-team usage tracking.
- LangSmith is the natural choice for teams deep in the LangChain ecosystem.
- Datadog LLM Observability fits teams already running Datadog.
- W&B Weave fits teams already using the W&B MLOps stack.
- Patronus AI stands out when hallucination detection and compliance documentation come first.
Many teams end up using two platforms: one for AI brand visibility monitoring and another for technical LLM observability. The two problems are different, and the tools that solve each well are built differently.
Frequently asked questions
What is the best platform for monitoring LLM traffic across multiple providers?
For multi-provider production traffic, a gateway is the most reliable choice. Portkey routes calls to more than 200 providers through a single endpoint, adds fallbacks and virtual keys, and logs cost and latency for every request. Helicone offers a lighter proxy with strong cost dashboards. If you already use Datadog, its LLM Observability product adds multi-provider traces next to your existing infrastructure metrics.
Which LLM monitoring tools detect latency and cost anomalies in production?
Helicone, Portkey and Datadog all track per-request latency, error rates and token cost, and all support alerts. Datadog is strongest for anomaly alerting because it can correlate LLM spikes with infrastructure events. Langfuse and LangSmith show step-level latency inside chains, which helps you find the root cause. The setup section above explains which thresholds to alert on.
Which platforms offer real-time drift detection for LLM applications?
Arize AI is the strongest option. It monitors embedding drift to catch shifts in input distribution before output quality drops, and Phoenix provides an open-source starting point. Langfuse, LangSmith and W&B Weave let you track evaluation scores over time, which surfaces quality drift. Patronus AI adds continuous hallucination scoring for regulated use cases where accuracy drift carries risk.
What is a good LLM evaluation and monitoring stack for production?
A common stack has three layers. A gateway such as Portkey or Helicone handles traffic, keys and cost. A tracing and evaluation tool such as Langfuse, LangSmith or Arize handles debugging and quality scores. Datadog or a specialist like Patronus covers alerting or compliance. Pick one tool per layer to avoid duplicate instrumentation, and start with the gateway, since it requires the fewest code changes.
Is LLM observability the same as AI visibility tracking?
No. LLM observability monitors your own application's requests, latency, cost and output quality. AI visibility tracking monitors how public models such as ChatGPT, Claude and Perplexity mention your brand when users ask them questions. Engineering teams need the first and marketing teams need the second. Sight AI covers AI visibility, while the other eight tools in this review cover observability.
If you are ready to understand exactly how AI models talk about your brand across the platforms your customers use, start tracking your AI visibility today. You will see where your brand appears, where it is missing, and what content will move the needle.
