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#briefScores 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.
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
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…
Lock-in risk: higher = more lock-in (worse). Overall already inverts this dimension.
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
Lakera Guard
Depot pick for a dedicated prompt-injection firewall in front of any LLM. NeMo when you need programmable…
Galileo
Pick for production hallucination measurement + guardrails. Braintrust/Promptfoo for lighter eval loops.
OpenAI Moderation
Default first-pass content filter for agent I/O. Pair with Llama Guard (Groq) or NeMo Guardrails when you…
Groq
Use when latency is the product. Do not build your whole company on one Groq model SKU — they rotate.
Guardrails AI
OSS programmable rails peer to NeMo Guardrails. Lakera when you want a hosted injection firewall API…
Arthur AI
Monitoring/evals peer with a real $60 Premium. Prefer Braintrust/Langfuse for tracing defaults; Arthur when…
HiddenLayer
Enterprise AI security peer to Prisma AIRS / Cisco AI Defense. Lakera/NeMo/Guardrails for builders…
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.
| # | Provider | Overall | Fit | $/perf | Latency | DX | As of |
|---|---|---|---|---|---|---|---|
| 1 | OpenAI Moderation | 8.6 | 8.2 | 9.8 | 9.2 | 9.4 | 2026-09-14 |
| 2 | Portkey | 8.3 | 8.8 | 8.2 | 8.6 | 9.0 | 2026-09-14 |
| 3 | Lakera Guard | 8.2 | 8.8 | 7.8 | 8.4 | 9.0 | 2026-09-14 |
| 4 | Guardrails AI | 8.2 | 8.4 | 8.8 | 7.8 | 8.2 | 2026-09-14 |
| 5 | Groq | 8.0 | 7.8 | 9.0 | 8.2 | 8.8 | 2026-09-14 |
| 6 | Arthur AI | 7.9 | 8.2 | 8.0 | 8.0 | 8.2 | 2026-09-14 |
| 7 | NVIDIA NeMo GuardrailsDepot Pick | 7.9 | 8.8 | 7.5 | 8.0 | 7.0 | 2026-09-14 |
| 8 | Galileo | 7.5 | 8.4 | 6.2 | 8.0 | 7.8 | 2026-09-14 |
| 9 | Patronus AI | 7.3 | 8.4 | 5.5 | 8.0 | 7.2 | 2026-09-14 |
| 10 | Palo Alto Prisma AIRS (ex-Protect AI) | 7.3 | 8.6 | 5.0 | 8.8 | 6.2 | 2026-09-14 |
| 11 | Cisco AI Defense (ex-Robust Intelligence) | 7.1 | 8.4 | 4.8 | 8.6 | 6.0 | 2026-09-14 |
| 12 | HiddenLayer | 6.9 | 8.4 | 4.0 | 8.2 | 6.0 | 2026-09-14 |
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%