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Best LLMOps & LLM Monitoring Tools (2026)

Compare the best LLMOps tools of 2026 with verified pricing. LangSmith, Helicone, Portkey and more — monitor, debug, and optimize your LLM applications.

✅ Prices verified from official sources📅 Updated March 2026

LLMOps tools help you debug, monitor, evaluate, and optimize AI applications in production. As LLM-powered products move from prototype to production, observability becomes critical — you need to know what your AI is doing, how much it costs, and whether it is working correctly.

1
Heliconeby Helicone
One-line integration to log, monitor, and improve your LLM calls — works with any provider
Free tier$20/mo
2
PromptLayerby PromptLayer
Version control for prompts — track, test, and deploy prompt changes like you deploy code
Free tier$29/mo
3
LangSmithby LangChain
Debug, test, and monitor your LLM apps — the observability platform built by the LangChain team
Free tier$39/mo
4
Portkeyby Portkey
AI gateway that routes, caches, and monitors your LLM calls — one API for 200+ models
Free tier$49/mo
5
Weights & Biasesby W&B
The MLOps platform ML teams actually use — experiment tracking, model registry, and LLM evaluation
Free tier$50/mo
6
Maxim AIby Maxim
AI evaluation and observability platform — test, monitor, and improve LLM apps in production
Free tierFree

Frequently Asked Questions

What is LLMOps?

LLMOps (Large Language Model Operations) refers to the tools and practices for managing LLM applications in production — including tracing, evaluation, prompt management, cost monitoring, and quality assurance. Think of it as DevOps but specifically for AI-powered applications.

What is the best LLMOps tool?

LangSmith ($39/seat/mo) is the most popular, especially if you use LangChain. Helicone ($20/mo) offers the easiest integration (one-line proxy). Portkey ($49/mo) is best for multi-model routing and cost optimization. Weights & Biases ($50/mo) excels at experiment tracking.

Do I need LLMOps tools?

If you are building a prototype or hobby project, probably not. If you are running an LLM application in production with real users, yes — you need observability to debug issues, track costs, and ensure quality. Most tools offer generous free tiers to get started.

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