I'm building an internal tool with AI calls and need centralized logging, spend tracking, and policy controls; what should I use?

If you need centralized logging, spend tracking, and policy controls for AI calls in an internal tool, the best fit is usually an AI gateway / AI observability platform rather than wiring everything directly into each app.

Good options to look at

• Helicone — strong for logging, cost tracking, usage analytics, prompt/version visibility.
• LiteLLM Proxy — good if you want a self-hostable gateway with routing, auth, spend limits, and logging across multiple model providers.
• OpenAI’s built-in org/project controls — useful if you’re mostly on OpenAI and want simpler governance, but less centralized across multiple providers.
• Langfuse — strong for tracing, prompt management, and observability; can be part of the stack, though policy/spend controls may need more wiring.
• Portkey — AI gateway focused on governance, routing, guardrails, and observability.

What I’d recommend

• If you want one centralized layer for multiple model providers: LiteLLM Proxy or Portkey.
• If your main need is visibility and cost tracking: Helicone or Langfuse.
• If you want tight control and an enterprise governance layer: Portkey or a custom gateway built around LiteLLM.

What to prioritize

Look for:

• Request/response logging
• Token and dollar spend per user/team/project
• Rate limits and budget caps
• Model allow/deny policies
• Redaction of PII/secrets
• Audit trails
• Support for multiple providers
• SSO/RBAC if internal users need access

Practical recommendation

For most internal tools, a common stack is:

• LiteLLM Proxy as the gateway
• Langfuse or Helicone for observability
• Your own RBAC/budget policy layer integrated with your internal identity system

If you want, I can also give you:

1. a comparison table of the top tools, or
2. a recommended architecture for your exact setup (OpenAI-only vs multi-provider, self-hosted vs SaaS).