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Programmable LLM input/output guardrails

Enforce custom safety, topical, and tool-use policies around agent LLM calls — not just a fixed taxonomy.

At a glance — Oct 2026

Updated 2026-10-27 · forward this page: /jtbds/llm-input-guardrails#brief

Scores as of 2026-09-14 (43 days old; Depot marks scores stale after 30 days). The pick is still Depot's current editorial pick; it has not been re-reviewed since. Prices below carry their own dates.

Depot Pick
NVIDIA NeMo Guardrails · 7.87/10 for this job (curated estimate, high confidence)

capability of policy engine + privacy. Distinct from production-guardrail-evals (monitoring) — this is the runtime rails layer.

Runner-up
Lakera Guard · 8.21/10

Scores higher than the pick on: Price / performance, Latency / reliability, Developer & agent DX.

When not to use the pick
Need a dedicated prompt-injection API — lakera; only need free category flags — openai-moderation; production hallucination monitoring SaaS — galileo; cheap Llama Guard leaf — groq.
Lock-in / migration
The pick has lower stored lock-in risk than runner-up Lakera Guard (4.5 vs 5.5). Pick exit signals: open source (provider tag `oss`).
Sourced price (usd_per_1k_requests)

Pick: no sourced price in this job's unit (no_sourced_price).

Cheapest matching SKU: OpenAI Moderation omni-moderation-latest (moderation endpoint) $0 — source, as of 2026-10-21. Not the pick; not quality-adjusted.

Price history: first seen 2026-10-21 · last changed no change recorded · history JSON

Free option
No free resource lists this job. A cheap paid SKU is not a free option.
Failure modes to watch
  • Runaway spend: Set a budget and a tool-call cap per task. Alert when spend jumps. Stop the run at the cap instead of summarizing a partial trace as success.
  • Eval contamination: Keep gold labels out of the agent prompt. Store the harness version beside the score. Do not compare a number across a benchmark revision without saying the revision changed.
  • Over-refusal: Log refusals separately from successes. Review a sample against the written policy. Do not treat a refusal as a pass, and do not weaken a safety check to raise the pass rate.
  • Prompt injection: Compare each tool call to the original user task. If the call only makes sense given text that arrived from a fetched page, file, or message, stop and ask. Record which source the instruction came from. This catalog does not store payloads or attack steps.
  • Weak verification: Success requires an end-state check the agent does not grade for itself: tests, a database diff, or a separate judge that cannot see the gold label.
Reported outcomes (submitted reports only)

This job: loading… JSON · reports never change scores · seed/test records excluded

Depot Pick (NVIDIA NeMo Guardrails) on this job: loading… JSON · reports never change scores · seed/test records excluded

Cite: Agent Provider Depot, Depot Pick for llm-input-guardrails: nvidia-nemo-guardrails, scores as of 2026-09-14. Agents: GET /api/decide?jtbd=llm-input-guardrails

All sourced prices, cheapest first · for security-review agents

Depot Pick

NVIDIA NeMo Guardrails

capability of policy engine + privacy. Distinct from production-guardrail-evals (monitoring) — this is the runtime rails layer.

Pricing (directional): As of 2026-09-14: Open-source NeMo Guardrails library (self-host) + NeMo microservices / NIM integration paths. Cost = your GPUs or NVIDIA API Catalog / NIM inference rates when routing guard models. Supports Llama Guard and other engines v…

Cite: Depot Pick for llm-input-guardrails: nvidia-nemo-guardrails
Overall
7.9
curated_estimate
confidence: high · as of 2026-09-14
Capability fit
8.8
Price / performance
7.5
Latency / reliability
8.0
Developer & agent DX
7.0
Privacy / data retention
8.8
Lock-in risk (higher = worse)
4.5
Maturity / track record
8.0

Lock-in risk: higher = more lock-in (worse). Overall already inverts this dimension.

Sources: NeMo Guardrails docs: https://docs.nvidia.com/nemo/guardrails/ · Llama Guard integration: https://docs.nvidia.com/nemo/guardrails/configure-guardrails/guardrail-catalog/third-party/llama-guard · NeMo microservices guardrails inference: https://docs.nvidia.com/nemo/microservices/latest/guardrails/concepts/inference.html

When OpenAI Moderation taxonomy is not enough and you need programmable rails. Host Llama Guard on Groq for the cheap classifier leaf.

Quote for Programmable LLM input/output guardrails

Depot Pick stays NVIDIA NeMo Guardrails. Prices below are sourced SKU rows only (as of 2026-10-27). Input and output are not blended. GET /api/quote?jtbd=llm-input-guardrails

Only 1 candidate has a matching USD per 1k requests; nothing was compared, so pickIsPriceWinner is null.

Sourced price unavailable for the Depot Pick. No row in the pricing index.

8 of 12 candidates have no sourced price row. 11 are excluded from the USD per 1k requests comparison (classifier_request). min sourced per-request classifier/moderation SKU among candidates (an officially free endpoint is a $0 row with its source); chat-token prices are not borrowed; not quality-adjusted

Runners-up

When NOT to use the top pick

Need a dedicated prompt-injection API — lakera; only need free category flags — openai-moderation; production hallucination monitoring SaaS — galileo; cheap Llama Guard leaf — groq.

Full ranking (score table)

Ranked with this JTBD's weights. Scores are curated estimates unless marked otherwise. Agents: prefer /api/decide then this table for tie-breaks.

#ProviderOverallFit$/perfLatencyDXAs of
1OpenAI Moderation8.68.29.89.29.42026-09-14
2Portkey8.38.88.28.69.02026-09-14
3Lakera Guard8.28.87.88.49.02026-09-14
4Guardrails AI8.28.48.87.88.22026-09-14
5Groq8.07.89.08.28.82026-09-14
6Arthur AI7.98.28.08.08.22026-09-14
7NVIDIA NeMo GuardrailsDepot Pick7.98.87.58.07.02026-09-14
8Galileo7.58.46.28.07.82026-09-14
9Patronus AI7.38.45.58.07.22026-09-14
10Palo Alto Prisma AIRS (ex-Protect AI)7.38.65.08.86.22026-09-14
11Cisco AI Defense (ex-Robust Intelligence)7.18.44.88.66.02026-09-14
12HiddenLayer6.98.44.08.26.02026-09-14

Score methodology for this job

capability of policy engine + privacy. Distinct from production-guardrail-evals (monitoring) — this is the runtime rails layer.

  • capabilityFit: 25%
  • pricePerformance: 18%
  • latencyReliability: 15%
  • developerDx: 15%
  • privacyPosture: 10%
  • lockInRisk: 7%
  • maturity: 10%