Shield360 Overview

Shield360 answers two questions about AI in your organisation: what is in use and what does it cost, and what are the AI applications you build actually doing.

• Visibility and spend. A lightweight endpoint collector on macOS, Windows and Linux reports AI usage, spend and shadow AI from every machine, with no proxy, no root certificate and nothing in the path of a request.
• AI engineering. OpenTelemetry-native SDKs trace LLM and agent calls. Evaluations, prompt management and cost tracking use the same production data.

Visibility

See every AI tool, model, website and provider in use across your machines, who used it, and what it cost:

• Endpoint collector: one command per machine, or push it with your MDM. It reads what AI tools already write to disk and which application is in front. It never intercepts traffic.
• AI spend report: what AI costs with your subscriptions and without, attributed to people, machines, projects, models and tools, with idle seats and savings.
• Shadow AI: personal plans, per-token API keys, AI websites, AI desktop apps and scripts calling providers directly, judged per person against who holds a seat.
• Sensitive data: personal data, card numbers and credentials found in stored prompts and responses, without copying the values anywhere.
• Privacy by design: sensitive collection is off until policy turns it on, API keys never leave a machine whole, and every record is isolated to its tenant.

Observe

LLM observability and AI agent observability for agents built with CrewAI, LangGraph, or any agent framework. Shield360 brings OpenTelemetry-native instrumentation so you can analyze how your agents perform in production with full stack visibility:

• Tracing - Instrument once via OpenTelemetry-native SDKs for 90+ LLMs and agent frameworks to produce OpenTelemetry traces.
• Lowest level transparency - Exceptions Monitoring surfaces errors with full stack traces and span context so you know exactly where and why something failed. All telemetry is automatically redacted for sensitive data before it leaves your stack.
• Understand cost and latency - Track token consumption, spend per model, provider, and request. Monitor GPU utilization alongside LLM call latency in custom dashboards built on your raw telemetry.

Develop

Prompt management, prompt versioning, and model testing tools for teams building AI applications:

• Prompt versioning and deployment - Prompt Hub treats prompts as versioned artifacts. Edit, version, and deploy to any environment without code changes. Roll back instantly when needed.
• Collaborate on prompts - Edit prompts as a team with full change history. Every version is tracked and linked to the evaluations and traces it produced, giving you full traceability from prompt to output.
• Model comparison and A/B testing - OpenGround runs side-by-side LLM prompt testing across models, comparing cost, latency, and output quality so you pick the right model before you ship.

Coding Agents

Coding agent observability for Claude Code, Cursor, and Codex. Understand token consumption, AI spend, and developer productivity across your entire engineering team:

• One-command setup - Install the shield360 CLI and wire any vendor in under a minute. No SDK, no code changes in your repos.
• Session monitoring - Track cost per session, token usage, lines added and removed, model acceptance rate, commits, and PRs per user and per team.
• AI spend visibility - See exactly how much each developer, team, and project spends on AI coding tools. Break down cost by model and vendor across Claude Code, Cursor, and Codex in one place.
• Privacy controls - Automatic secret redaction, usage breakdowns by repo, and cohort-floor anonymization so individual data stays protected.

Improve

LLM evaluation in production and development using the same evaluators. Close the loop between what you ship and what you measure:

• Online LLM evaluation - LLM-as-a-judge runs automatically on your live traces in production. Create custom evaluators with your own scoring criteria and quality gates. No setup required.
• Offline evaluation and CI/CD testing - Use the same evaluators via the programmatic evaluations API for regression testing in CI pipelines and local development. Catch quality regressions before they reach production.
• Traceability - Every evaluation score is linked back to the exact prompt version, model, and trace that produced it so you know what changed and why quality shifted.

Manage

Manage the agents, keys, and pricing behind your AI stack from one place:

• Track and manage agents - See every agent version running in production, monitor health, and manage deployments from the /agents page.
• Vault - Centrally store and rotate LLM API keys that agents retrieve at runtime. No restarts, no code changes, no secrets in your codebase.
• AI cost tracking - Costs under Monitor combines spend analytics, Manage models, and Auto Pricing. Shield360 ships with pricing for popular models and applies it across traces, dashboards, and cost reports.

Otter

Otter is a built-in AI copilot that works across all of Shield360. Query your telemetry, analyze traces, improve prompts, and generate dashboards through natural language:

• Natural language queries - Ask questions about traces, costs, and tokens. Otter converts them to SQL and runs them inline.
• Trace and span analysis - Get structured AI analysis across performance, reliability, cost, token efficiency, and execution path.
• Prompt improvement - Ask Otter to review and suggest improvements to any Prompt Hub prompt.
• Dashboard generation - Describe the dashboard you want and Otter generates a complete importable layout with working queries.
• Resource management - Create rules, contexts, vault secrets, and custom models through conversation.

Get Started

See AI usage and spend

Enrol a machine and see usage, spend and shadow AI in five minutes

Instrument an AI Agent

Production-ready AI Observability in 2 steps with zero code changes

Observe coding agents

Track spend and usage for Claude Code, Cursor, & more across teams

Deploy Shield360

Use the managed service or self-host the full platform

Evaluate LLM responses

Score live traces automatically with prebuilt LLM-as-a-judge evaluators

Frequently asked questions

What is Shield360?

Shield360 is an AI visibility and AI engineering platform. Its endpoint collector shows what AI is in use across your machines, what it costs and who is using it, including shadow AI. Its OpenTelemetry-native SDKs trace LLM and agent calls, track cost and token usage, run automated evaluations and manage prompts.

Does Shield360 intercept network traffic?

No. The collector installs no certificate, changes no proxy setting and opens no listening socket. It reads files AI tools already write and tables the operating system already keeps, so a request can never fail because Shield360 observed it.

Can I self-host Shield360?

Yes. Use the managed service at app.shield360.ai, or self-host the platform so telemetry, prompts and collector data never leave your infrastructure.

Does Shield360 require code changes to instrument my app?

No. Shield360’s SDK auto-instruments 90+ LLMs, agent frameworks, and vector databases with zero code changes, or you can call shield360.init() once for manual instrumentation if you want more control - both produce the same OpenTelemetry traces.

How is Shield360 different from other LLM observability tools?

Most tools see only the applications you instrument. Shield360 also sees AI use you didn’t instrument, on every machine, and attributes its cost to people, projects and subscriptions. It combines that with observability, evaluation, cost tracking and prompt management in one platform.