How Oberhahn compares
Security, context efficiency, and real-time visibility, side by side.
Plenty of tools now claim to track AI spend, but most were built for something else and added AI reporting later. Oberhahn is built for real-time AI visibility: what teams and agents send to models, where secrets leak, and who is driving the spend.
The three questions we hear most, answered head-on. Hover any card to see how Oberhahn compares to that tool.
We already run Datadog. Why add a tool just for AI?
Datadog watches your whole cloud. Oberhahn turns AI usage into a live, attributed map of every person and agent, and flags the unattended loops quietly running up the bill.
Our engineers trace LLMs in LangSmith. Isn't that enough?
LangSmith helps engineers debug one app. Oberhahn gives the whole org real-time spend, security, and per-person attribution across every team and provider.
Why not just use the dashboards our AI providers already give us?
Provider consoles are siloed per vendor with no attribution. Oberhahn unifies OpenAI, Anthropic, and Gemini into one view with security and per-person cost.
More comparisons by category
Other tools teams often weigh against Oberhahn, grouped by what they do, so you can jump straight to the comparison that fits how you track, secure, and attribute AI usage.
General-purpose monitoring platforms that now bolt on LLM traces alongside infrastructure, logs, and application metrics.
Datadog
Full-stack observability across infrastructure, APM, and logs, with a fast-growing AI suite: LLM Observability, Agent Observability, and AI cost management.
New Relic
All-in-one observability with usage-based pricing spanning APM, infrastructure, and logs.
Grafana
Open-source dashboards and visualization, with Grafana Cloud adding prebuilt AI and agent observability.
Dynatrace
Enterprise observability with AI-driven root-cause analysis across apps, infrastructure, and logs.
Developer tools for instrumenting, debugging, and evaluating a specific LLM application in and around the codebase.
LangSmith
Tracing, evaluation, and prompt engineering for building and shipping LLM apps and agents.
Langfuse
Open-source LLM/agent observability with tracing, evals, prompt management, and dashboards for application teams.
Helicone
LLM logging, caching, and cost tracking via an AI gateway/proxy or async SDK logging.
Arize Phoenix
Open-source LLM and agent tracing and evaluation for debugging, experimentation, and model performance.
Routers and first-party consoles that sit in front of model providers and report usage for the traffic they proxy.
Provider dashboards
First-party consoles (OpenAI, Anthropic, Gemini) that report usage and billing for their own APIs.
Portkey
An AI gateway that routes to many models with observability, caching, and guardrails.
OpenRouter
A unified API to hundreds of models, with an activity dashboard, workspaces, and spend controls.
LiteLLM
Open-source proxy and SDK that gives one OpenAI-style API across 100+ models, with keys, budgets, and logs.
How we keep this honest
When a competitor wins on a feature, we say so. Oberhahn is not a general observability platform, and it is not a billing system.
Every comparison lists where the other tool is the right choice. Many of these products run happily alongside Oberhahn.
We review these pages quarterly and update them when products change materially. Your data always exports as JSON or CSV.
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