Plain answers to the questions teams ask as their AI adoption accelerates — what your AI costs, and where your private data flows. Observe-only, live in minutes.
Start by seeing what your AI already does. Most companies adopt AI faster than they can see it — assistants and agents reach data and run up cost with no single view. The first move is not a new policy; it is visibility: one live graph of what your AI reaches and what it costs, so you can go faster and stay in control.
Attribute it. AI cost is opaque because tokens are not tied to who spent them or why. Latten reads your AI's real traffic and shows cost per actor, per model, and per data domain — live — so you can find spend that has no owner and cut it without slowing anyone down.
Often more than you think. An assistant wired to a shared service account can reach everything that account can — at conversational speed. Latten maps the blast radius from your real traffic: what each actor reaches, who asked, and where access runs wider than anyone intended.
AI observability is the live view of what every assistant, agent, and model call is doing: who triggered it, which model ran, what data or service it reached, and what it cost. For company AI, logs are not enough. You need cost and reach attributed to the actor behind each call, so adoption can keep moving without turning into an opaque bill or an unknown access path.
Start by measuring their real reach. An AI agent is only as safe as the service accounts, tools, and data domains it can use. Secure it by attributing every action to the actor behind it, mapping what it reaches from real traffic, and then bounding the wide edges after you can see them.
Add observe-only tracking at the places your app crosses boundaries: the request actor, LLM calls, database reads or writes, external APIs, and agent handoffs. With @latten/sdk, those calls keep returning the same values, while Latten records who acted, what was reached, and what the model cost.
Add the Latten MCP server to Codex and give it one task: provision an observe-only brain if no token exists, read the guide, scan the TypeScript repo, instrument high-confidence crossings with @latten/sdk, open a reviewable PR, verify real traffic, and return the trust receipt plus claim link.
Langfuse is strongest as open-source LLM observability for traces, prompts, evaluations, and product analytics. Latten is different: it maps company AI by actor, cost, data reach, and PII exposure, observe-only, so teams can see the blast radius of agents and copilots before they decide what to bound.
Helicone is built around LLM gateway observability, logging, caching, rate limits, and cost analytics for model calls. Latten is built around company AI governance: who acted, what the AI reached, what it cost, and whether private data exposure is visible without copying values.
LangSmith is strongest for developing, tracing, testing, and evaluating LLM applications, especially in the LangChain ecosystem. Latten is for operating company AI safely: actor-level cost, data reach, PII exposure, attribution, and blast-radius visibility across the agents and apps already running.
Datadog is strongest when the team wants LLM observability inside a broad enterprise monitoring platform. Latten is narrower and more specific: one observe-only graph for company AI cost, data reach, PII exposure, actor attribution, and blast radius.
The best LLM cost monitoring tool depends on the question. If you need traces and model-call analytics, use an LLM observability tool. If you need to know which actor, agent, or data domain drove spend — and what the same AI could reach — use Latten.
An AI agent governance tool should show who the agent acted for, which model ran, what data or service it reached, what PII types were exposed, and what the action cost. Latten shows those crossings on one observe-only graph.
Instrument the boundaries where the app calls models, reads data, and hands work between agents. In the Teeras demo, a customer-discovery sweep can route those crossings through Latten so the run shows what it cost, which model calls happened, and whether private data exposure stayed values-free.
@latten/sdk is observe-only TypeScript instrumentation for AI apps. It tracks LLM calls, data touches, external APIs, cost, reach, actor attribution, and PII exposure while returning the wrapped function's result unchanged.
@latten/installer is a Codex-ready MCP server that provisions an observe-only Latten brain, scans TypeScript apps for LLM/data/API crossings, guides the agent to instrument @latten/sdk, verifies real traffic, and returns a receipt plus claim URL.