LLM Guard replacements compared

No. LLM Guard, Protect AI's MIT-licensed prompt and response scanner, was archived on GitHub on July 9, 2026 and is no longer maintained, so it receives no security fixes. Any vulnerability disclosed after that date stays open in your deployment unless your team patches it, and no upstream fix will arrive. That makes the move a security task as well as a feature change. Start by listing every place LLM Guard runs, including staging, CI jobs and developer tools, and record the exact version in use. Then decide how long the current version can stay in production while the replacement is tested. Review the LLM Guard repository to see which scanners depend on its code, since those need an explicit replacement first.

No single option covers every LLM Guard scanner, so you need a scanner-by-scanner mapping before you choose. Write one row per scanner you enable, the risk it addresses and the replacement that covers it. The options in this comparison cover different ground:

• NeMo Guardrails provides input, dialog, output, retrieval and execution rails, plus self-check moderation and jailbreak and injection detection.
• Guardrails AI runs validators from Guardrails Hub, such as regex matching and toxicity detection, and can chain them.
• Meta's Prompt Guard 2 labels prompts as benign or malicious for injection and jailbreak attempts.
• LlamaFirewall layers Prompt Guard 2, AlignmentCheck, CodeShield and custom scanners.

Scanners with no direct equivalent will need custom code or a documented decision to drop them. Confirm each mapping against vendor documentation and your own test results before you retire a scanner.

The main cost is staff time for inventory, design, custom code, migration and testing, plus the infrastructure needed to host any classifier models. Most of this is one-time work. Hosting, model upgrades and monitoring recur. The drivers are the number of scanners enabled, how many custom validators or rails you must write, and how much traffic the test set needs to cover. Model choice matters too: Llama Guard 4 is a 12-billion-parameter model, while Prompt Guard 2 comes in 86M and 22M versions, and the Prompt Guard 2 model card says the 22M version cuts latency and compute costs by 75% with minimal performance trade-offs. If you use NeMo Guardrails with Colang 1.0 configurations, NVIDIA's migration guide says converted configurations still need manual review. License review also takes time, since Llama Guard 4 and Prompt Guard 2 ship under the Llama 4 Community License.

A useful quote follows from a written scope and lists every item the price covers. Ask for the scanner inventory and mapping, the named replacement options and their versions, the custom validators or rails to be built, and the integration points, such as CI, the API gateway and the application. The quote should also name the model hosting assumptions, the test set and acceptance criteria, the documentation and handover, the license review, and anything explicitly out of scope. Ask whether the work is phased, which steps depend on others, and what changes would trigger a new quote. Separate one-time implementation from any recurring support, upgrade or continuous testing retainer. DeployOpen plans and implements these replacements in the client's own infrastructure under fixed-scope agreements, with scope, deliverables and timelines agreed in writing before work starts. It does not publish a price list, so a quote follows from that written scope.

The duration depends on the written scope, and no fixed figure applies to every LLM Guard deployment. The biggest variables are the number of scanners, how much custom code is needed, how quickly model hosting and license decisions are approved, and how large the test set must be. A deployment with a few well-mapped scanners and no custom validators will move faster than one that depends on bespoke detection logic. Ask for a phased plan with milestones, a staging pilot before production cutover, and an explicit point where the old version is retired. Also ask who approves each milestone on your side. Because the team cannot retire LLM Guard until replacements are tested, the timeline should include the period when both systems run in parallel. DeployOpen's engagements are fixed-scope, so the timeline is agreed in writing before work begins.

Yes, if the team has Python and platform engineering skills and a test set that reflects its real traffic. Most of the work is inventory, mapping, configuration and testing, which an internal team can do with good records. Plan for the points where people usually get stuck. NeMo Guardrails configurations that move to Colang 2.x need manual review after conversion, according to NVIDIA's migration guide. LlamaFirewall's AlignmentCheck scanner requires a Together API key, so check that your data handling and vendor approvals allow it before you enable it. Model licenses also need an owner, because Llama Guard 4 and Prompt Guard 2 use the Llama 4 Community License. If internal capacity is limited for some of these steps, DeployOpen can take on defined parts of the work under a fixed-scope agreement.

The work needs three things from your side: data, records and decisions. Provide a sanitized traffic sample so the test set reflects real prompts and responses, along with the current LLM Guard configuration for every application that uses it. Provide a scanner inventory that lists each input and output scanner, the threshold settings and the risk each one addresses. Name an owner who makes decisions on models, licenses and acceptable risk, and who can approve scanner mappings and exceptions. You also need access to staging, CI pipelines and the application teams who will test changes. Without a named owner, model and license questions tend to stall the project, so settle that role before scoping starts. A written scope should state which of these inputs the vendor expects and which the client provides.

Confirm it with the project's own documentation and maintainers, then test it against your inventory. Start by checking the license, the maintenance status and the release history, since those decide whether you can ship it and get fixes later. Next, map each scanner in your LLM Guard inventory to a DeepTeam guardrail and mark any gaps. Ask which models the guardrails depend on, where they run, what latency they add and whether they can run in your own infrastructure or air-gapped network. Then run your test set through a staging deployment and compare detection and false positive results with the current scanners. Record the results and the owner who accepts them. If DeepTeam covers only part of your inventory, the remaining gaps need a second option or custom code, and the scope should say so before work begins.