LLM Guard Cost in 2026: Archived Code and Production TCO

LLM Guard pricing begins at zero for the software license. Protect AI released the code under the MIT license, then archived the repository on July 9, 2026. That leaves a buyer with a different cost question: what will it take to run, patch, observe and eventually replace an unmaintained scanner stack inside a production AI application?

I would keep the word "free" out of the approval memo. The invoice may start at zero, but the engineering queue does not.

TL;DR

• LLM Guard carries no software license charge under its MIT license.
• Protect AI archived the repository and says the project and associated models are no longer actively developed or maintained.
• Production TCO includes compute, scanner execution on both sides of the model call, integration, monitoring, review, patch ownership and replacement work.
• Price the deployment with measured payloads and latency rather than a guessed per-request rate.

The license price and lifecycle state

The LLM Guard license grants broad permission to use, copy, modify, merge, publish, distribute, sublicense and sell copies of the software. Procurement can therefore put a zero in the software-license row for the open-source code.

The current LLM Guard repository adds the lifecycle fact that changes the rest of the worksheet. GitHub records that the owner archived it on July 9, 2026 and the repository notice says the project and its associated Hugging Face models are no longer under active development or maintenance. The repository is read-only.

That status transfers maintenance decisions to the adopter. A team can pin the current code and keep running it. It can also fork the repository. Either choice needs a named owner for dependency updates, scanner-model compatibility, vulnerability triage and regression testing. The MIT license permits the work. It does not supply the people.

The nearby DeepInspect versus LLM Guard comparison covers placement and policy scope. This article owns the narrower budget and lifecycle decision.

Compute follows the scanner path

LLM Guard can inspect an input before the application calls an LLM and inspect the generated output before the application returns it. The TCO model should preserve those as separate execution legs. A request that uses several input scanners and several output scanners creates more work than a request checked by one lightweight rule.

Protect AI's deployment documentation describes CPU and GPU installation options, Uvicorn and Gunicorn, Docker and Kubernetes. Its Docker note recommends allocating at least 16 GB of RAM. The same page documents out-of-memory controls, scanner reduction, lower-memory model loading, lazy loading and longer Kubernetes probe delays while models start.

Those details belong in the capacity test. Record memory after startup, warm-request latency, cold-request latency, concurrency, queue depth and error rate for the exact scanner set. Repeat the test with representative long prompts and long responses. A screenshot of a green container beside an empty request queue proves very little.

My preference is a replay file with ordinary prompts, large retrieved contexts, adversarial text and expected decisions. Run it against every candidate instance size. The result gives finance a measured workload instead of a fictional unit price.

Integration and latency create labor costs

An application team has to call the input scanners, handle sanitized text or a refusal, send permitted content to the model, and then process the output through a second scanner path. Every model route needs the integration. Streaming responses, file inputs, retries, asynchronous jobs and agent loops need explicit treatment.

Map those routes before estimating labor. The AI gateway architecture guide provides a useful route inventory, even when the final design keeps LLM Guard in-process. Mark the exact call site, scanners, timeout, retry rule, user-facing error and log destination for each route.

Latency has a product cost as well as an infrastructure cost. Scanner execution can delay both the outbound request and the returning answer. Teams should measure the added time at normal and peak concurrency, then decide which scanners run synchronously. A security review should reject any design that silently skips the scan after a timeout unless that bypass is an approved policy.

The physical artifact I want is one printed route map with red dots at every scanner call. If an LLM arrow has no red dot, the application has an uninspected path.

Operations need a named maintenance owner

An archived dependency needs a written operating boundary. Assign ownership for the fork or pinned release, Python dependencies, model artifacts, container base image, build pipeline, scanner configuration and vulnerability response. Define the evidence required before any dependency change reaches production.

Observability also needs its own budget line. Capture scanner name and version, configuration version, input or output stage, execution time, result, failure reason and the application's final action. Avoid storing raw sensitive prompts merely to make debugging convenient. The retention design should separate operational telemetry from protected content.

False positives create review work. Use a labeled corpus to count permitted requests blocked by each scanner and prohibited requests that pass. Route disputed decisions to an owner who can change configuration through a reviewed release. An archive status makes this discipline more important because upstream fixes and newly tuned model releases are no longer arriving through normal project maintenance.

For independent evidence design, AI audit-log immutability explains the difference between an application event and a separate policy record. LLM Guard verdicts remain part of the application path unless the adopter builds another write path.

The TCO worksheet

Build the budget as a set of named rows rather than one "LLM Guard" total:

• Software license: zero for the MIT-licensed code, with legal review recorded separately.
• Runtime compute: development, test and production instances sized against the selected scanners.
• Model storage and startup: artifact storage, image size, warm capacity and deployment delay.
• Integration: every prompt and response path, including retries, streams, files and agent calls.
• Observability: metrics, alerts, decision logs, storage and incident investigation.
• Quality review: corpus maintenance, false-positive adjudication and release acceptance.
• Maintenance: dependency updates, vulnerability triage, fork management and security fixes.
• Migration reserve: replacement evaluation, dual running, policy translation, replay and rollback.

Keep model-provider inference outside these rows. Keep the application's own compute outside them too. The worksheet should show which costs exist because the scanner stack is present.

A production approval should include an exit trigger. Examples include an unpatchable dependency, scanner incompatibility with the application runtime, unacceptable latency, or a control requirement the current stack cannot meet. Attach the migration reserve to that trigger rather than leaving replacement work as a future surprise.

DeepInspect

DeepInspect evaluates identity, role, data classification, destination and versioned policy on authenticated HTTP AI traffic. It records each routed decision independently before permitted traffic reaches the model. Local scanner execution and application branches remain separate evidence surfaces.

Use the same replay corpus to test the in-process scanner and the HTTP policy route, then keep each result attached to the component that produced it. That gives procurement a cost map and engineering a control map. Book a technical deep dive at deepinspect.ai.