You need general reasoning or extraction.
OpenAI, Claude, Gemini, and Bedrock work well for broad language tasks. They can redact text with a prompt when the workload is small, manual, or experimental.
LLM API Alternative
Alternative to LLM-based PII redaction without token costs or data egress.
OpenAI, Claude, Gemini, and Bedrock can redact text with a prompt. Production LLM PII redaction still requires token budgeting, retries, output validation, parsing, writeback, and governance for sensitive data sent to model endpoints. Agent Mask runs private PII redaction in Snowflake or a self-hosted container with no LLM token meter.
Start a trial on Snowflake Marketplace, or get a self-hosted trial key for Docker.
LLM APIsPrompt-based redactionUseful for demos and small payloads
What you manageAI/LLM infrastructureToken cost, retries, validation, egress review, writeback
Agent MaskPrivate redactionNo LLM token meter, deterministic de-identification, PII stays in your chosen runtime
1SELECT agent_mask_en.app_public.mask(
2 [ticket_note, chat_log]
3) AS redacted
4FROM support_tickets;
Input column
Text
ticket_note
Billing question from
Maya Chen
PERSON_1
about invoice INV-1042. Assigned to
Alex Patel
PERSON_2
. Contact:
maya.chen@example.com
EMAIL_ADDRESS_1
or
415-555-0198
PHONE_NUMBER_1
chat_log
10:42
Alex
PERSON_2
:
Maya
PERSON_1
never got the invoice email.
10:43 Maya Chen PERSON_1 replied from maya.chen@example.com EMAIL_ADDRESS_1 and asked Alex Patel PERSON_2 to call 415-555-0198 PHONE_NUMBER_1
10:43 Maya Chen PERSON_1 replied from maya.chen@example.com EMAIL_ADDRESS_1 and asked Alex Patel PERSON_2 to call 415-555-0198 PHONE_NUMBER_1
Choose the right redaction path.
LLMs are flexible general-purpose models with variable output. Agent Mask is built for repeatable private PII redaction and de-identification.
You need repeatable redaction with expert controls.
Agent Mask redacts PII in Snowflake SQL workflows or through a self-hosted PII redaction API across text and documents, with custom entity detection, consistent replacements, and audit/review metadata that shows what was found and where.
LLM-based PII redaction costs show up in production.
An LLM-driven redaction pipeline sounds simple until it has to process unstructured text, documents, and warehouse-scale data.
You pay for the prompt, input, output, and retries
LLM PII redaction is token-metered: the prompt, source text, redacted output, and extra retry or validation calls all count.
Discounts do not remove the meter
Batch discounts can lower model spend, but they change how the team queues and processes redaction. You pay for generative AI instead of a redaction system.
External model endpoints add governance work
Major providers publish data controls. Teams still have to evaluate data movement, retention settings, regional routing, access controls, and audit requirements for sensitive payloads sent to external model endpoints.
LLM APIs vs Agent Mask.
Compare token cost, data movement, output validation, and audit/review metadata behind LLM API redaction versus private Agent Mask deployment.
Dimension
LLM APIs
Agent Mask
Pricing model
LLM APIsToken-metered input, output, cached input, batch modes, regional routing, and model-specific rates.
Agent MaskPrivate runtime planning instead of LLM token metering.
Data movement
LLM APIsIf you use an external LLM API for redaction, sensitive text leaves the current runtime unless your team has already built a private model architecture.
Agent MaskProcesses PII inside your private runtime: Snowflake or self-hosted.
Output control
LLM APIsPrompted output can vary and needs validation, parsing, and defensive formatting.
Agent MaskDeterministic structured response with redacted text, entity ledger, exact match locations, and other metadata
Operational work
LLM APIsYou build the infrastructure, just to build another pipeline. Requires prompts, model routing, retry handling, parsing, logging, writeback, and monitoring.
Agent MaskOne masking function or private API for text and supported documents.
Best use case
LLM APIsGeneral reasoning, summarization, extraction, and flexible language tasks.
Agent MaskRepeatable PII masking, redaction, de-identification, and audit-friendly output in a private runtime.
Reference material
Pricing, privacy, and data-movement notes are all based on public provider documentation.
OpenAI API pricing
Current OpenAI API token pricing, batch pricing, and regional processing notes.
OpenAI data controls
OpenAI documentation for API data use, retention, and Zero Data Retention eligibility.
Claude API pricing
Anthropic model pricing, batch pricing, and regional inference pricing notes.
Gemini API pricing
Google Gemini API token pricing and paid/free data-use distinctions.
Amazon Bedrock pricing
AWS Bedrock pricing for model inference, batch inference, and provider-specific model rates.
Amazon Bedrock data protection
AWS documentation that model providers do not access Bedrock customer prompts or completions.
Agent Mask zero-egress architecture
Agent Mask documentation explaining how Snowflake Native App and self-hosted Docker processing stay inside your chosen runtime without sending payloads to Agent Mask.
Agent Mask self-hosted security
Agent Mask documentation for self-hosted data flow, network requirements, offline license verification, retention, and hardening.
Related comparisons
Keep evaluating redaction options across the same private-runtime, data movement, pricing, and implementation questions.
Agent Mask beat OpenAI Privacy Filter in a 50,000-example PII detection benchmark.
Agent Mask reached 96.9% F1 vs 74.2% for OpenAI Privacy Filter on a 50,000-example English PII benchmark, while also returning redacted output and review metadata.
Snowflake AI_REDACT alternative for long text, documents, and custom entities.
Compare Snowflake AI_REDACT with Agent Mask for PII redaction in Snowflake: long text, documents, custom entities, consistent replacements, and review metadata.
John Snow Labs handles clinical NLP. Agent Mask returns PHI de-identification with audit evidence.
Compare John Snow Labs with Agent Mask for Snowflake PHI de-identification, clinical notes, audit mapping, custom entities, and private deployment.
LLM API redaction FAQ
Answers for teams comparing prompt-based redaction with a private redaction runtime.
When is an LLM prompt enough for PII redaction?
Use an LLM for small, low-volume text that a person can review before the result goes anywhere else. It is a poor fit when redaction has to run repeatedly across warehouse data, long inputs, and downstream systems that expect stable output.
What should redaction return besides masked text?
Production teams need more than a redacted string. They need the values found, where each value appeared, the replacement used, entity types, and audit/review metadata. Because LLMs are generative, their spans, counts, and character offsets can drift or be invented, making them a weak source of truth for index positions and other audit/review metadata.
Why do token costs matter for redaction?
LLM API redaction is metered on input tokens, output tokens, and the extra calls needed for retries or validation. At warehouse or pipeline scale, routine privacy work can become its own LLM API cost center.
What about a private or no-training LLM endpoint?
A private or no-training endpoint reduces training-data risk, but you still have to review the redacted output. Compliance teams usually need audit/review metadata to evaluate consistency before relying on non-deterministic model output.
When should we choose Agent Mask over LLM API redaction?
Choose Agent Mask when redaction has to run repeatedly across warehouse text, documents, tickets, or transcripts without LLM token metering or external model egress. It gives you deterministic de-identification, custom entities, consistent replacements, and review evidence in a private runtime.
Redact PII without LLM token metering.
Start in Snowflake Marketplace or self-host Agent Mask to redact sensitive text and documents without sending them through a general-purpose LLM API.