Olivares AI vs LLM gateways & observability (LiteLLM, Langfuse)

A common, sensible self-hosted stack pairs an LLM gateway (for example LiteLLM) with an LLM observability platform (for example Langfuse). If you have one, you might reasonably ask whether you need a control plane at all. This page answers that honestly — including the cases where the answer is no.

The short version

LiteLLM and Langfuse are about the model calls your application makes: route them, trace them, manage prompts, track cost per call. Olivares AI is about every agent in your estate and everything it reads or writes — databases, object stores, MCP servers, tools, files — and whether that matches what policy permits. Different altitude. They compose; we ingest the same OpenTelemetry gen-ai signal they emit.

What that stack does well (use it for this)

• LiteLLM — a unified, OpenAI-compatible gateway in front of many providers: routing, fallbacks, retries, virtual keys, per-key budgets and rate limits, and cost accounting on the model calls that pass through it.
• Langfuse — LLM engineering and observability: request/response traces, prompt management and versioning, evaluations, datasets, and a developer-facing UI for debugging chains.

If your problem is “instrument my app’s LLM calls, debug prompts, and manage model access from one endpoint,” this stack is excellent and self-hostable. You do not need a control plane to do that, and we will not pretend otherwise.

Where Olivares AI is structurally different

Dimension	LLM gateway + observability	Olivares AI
Unit of concern	A model call (prompt → completion)	An agent and every resource it reads/writes — DBs, object stores, MCP, tools, files
Vantage point	In the request path (proxy/SDK); sees what the app sends	Out of band, read-first; observes telemetry, native audit and a kernel backstop — never in the data path
Source of truth	What the app/proxy reports	Self-reported telemetry corroborated against the system’s own ledger — pgAudit (read vs write), CloudTrail (object access), eBPF backstop
The key question	”What did this prompt do, and what did it cost?"	"Is this agent using access nobody granted?” — Permitted-vs-Observed drift
Enforcement	Gateway can gate model calls (keys, budgets)	Deny-closed gates on actions and resource access: approvals, the Claude Code hooks PEP, MCP tool gating, kill switches
Audit artifact	Traces / logs for debugging	Append-only, hash-chained, Ed25519-signed ledger, off-box verifiable, exportable as OSCAL evidence packages
Deployment posture	Self-hostable	Self-hosted or air-gapped; data plane never leaves your boundary; AGPL, source-available

The load-bearing difference is ground truth. An observability trace tells you what the application said it did. It cannot tell you that an agent reached a table the trace never mentioned. Olivares AI cross-checks the cooperative signal against the data plane, so “what the agent touched” is a corroborated fact, not a self-report. See Analyst vocabulary for why that is the first of our three lanes.

It’s “and”, not “or” — we ingest your telemetry

Olivares AI is not a replacement for your gateway or your tracing tool, and it does not want to be in the request path they occupy. It consumes the same signal: the control plane ingests OpenTelemetry GenAI semantic-convention spans, the same gen-ai telemetry these tools emit and consume. So a healthy arrangement is:

• Keep LiteLLM as your model gateway and Langfuse for developer-facing tracing and prompt work.
• Point the OTel gen-ai stream at Olivares AI as one corroborating source, and let the access map, drift detection and the ledger do the estate-wide governance layer on top.

→ Ingest OpenTelemetry GenAI · Enterprise OTel for Claude Code

When you should not reach for Olivares AI

Honesty cuts both ways. You probably do not need this control plane if:

• Your only goal is tracing and debugging LLM calls in one or two apps, with a prompt playground — Langfuse alone is a better fit.
• You just need a multi-provider gateway with budgets and failover — that is LiteLLM’s job, and we integrate with that pattern rather than reimplement it.
• You have no estate to govern: a single service, a single model, no agents touching databases/object stores/MCP, and no audit or regulatory obligation.

Olivares AI earns its place when the questions become estate-wide and adversarial: which agents exist, what can each actually reach, where is access drifting from policy, can I prove it to an auditor, and can I stop a bad action deny-closed — all without sending that picture to someone else’s cloud.