Ship AI features you can actually trust

Trusted by teams shipping AI in production

• Northwind
• Lumen Health
• Parcelly
• Foundry Labs
• Cobalt
• Riverbend

• ◎

  Full-fidelity tracing

  Every prompt, completion, tool call, token count and latency figure, captured with two lines of SDK code and searchable for 90 days.

• ✓

  Evaluations that mean something

  Run LLM-as-judge, deterministic assertions or human review against curated datasets. Compare runs side by side before you merge.

• ⎇

  Prompt versioning

  Prompts live in Cloudmind with diffs, reviewers and one-click rollback, so a copy tweak never ships without a paper trail.

• ◴

  Regression detection

  Cloudmind replays your golden set on every prompt or model change and blocks the deploy when quality drops beyond your threshold.

• ⚑

  Production monitoring

  Alert on cost spikes, latency drift, refusal rates and eval scores falling below target, straight into Slack or PagerDuty.

• ⊞

  Works with your stack

  Model-agnostic by design. OpenAI, Anthropic, Bedrock, Vertex, LangChain, LlamaIndex or your own custom agent loop.

import OpenAI from "openai";
import { monitorOpenAI } from "@cloudmind-ai/js/openai";

const openai = monitorOpenAI(new OpenAI());

// Everything below is traced automatically: prompt, completion,
// token counts, latency, cost and any errors.
const result = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Summarize this support ticket." }],
});

Faster mean time to diagnose an AI bug

73%

More prompt changes shipped per week

4.2x

Average reduction in token spend

31%

Trace retention on every paid plan

90 days

• For engineers

  Debug AI features faster

  Jump from a user complaint to the exact trace, see the prompt that produced it, and replay it with a fix.

• For product

  Measure quality, not vibes

  Define what good looks like once, then track it on every release without asking an engineer.

• For platform teams

  Roll out AI safely at scale

  SSO, audit logs, data residency and self-hosted deployment for teams with a compliance review to pass.

What is LLM observability?

LLM observability is the practice of capturing every model interaction in your application, including the prompt, the completion, tool calls, latency, token usage and cost, so you can debug failures and measure quality over time. Unlike traditional APM, it has to handle non-deterministic outputs, which is why scoring and evaluation are part of the same workflow.

Does Cloudmind work with models other than OpenAI?

Yes. Cloudmind is model-agnostic. The SDK ships first-class wrappers for OpenAI and Anthropic, integrations for Bedrock, Vertex AI, Azure OpenAI, LangChain and LlamaIndex, and a generic trace API for custom or self-hosted models.

How long does it take to get set up?

Most teams send their first trace within five minutes. Install the SDK, set your API key, and wrap your existing model client. No infrastructure changes and no proxy in the request path.

Do you send my prompt data to a third party?

No. Traces are stored in Cloudmind and never used to train models. You can redact fields client-side before they leave your process, and Enterprise customers can run Cloudmind entirely inside their own VPC.

How much does Cloudmind cost?

The free tier covers 50,000 traces per month with 7-day retention. Team is $99 per month for 1 million traces and 90-day retention. Enterprise pricing is based on volume and deployment model. See the pricing page for a full breakdown.