Skyflow alternative for LLM and agent traffic

Skyflow and Maskflare both replace sensitive values with tokens and let authorized code get the originals back. The difference is where the data lives and how much of your architecture changes. That's the question this page tries to answer honestly. It's written by Maskflare.

What is Skyflow?

Skyflow was founded in 2019 in Palo Alto and built its name on the data privacy vault: you store PII, payment and health data in Skyflow's vault, keep tokens in your own databases, and detokenize under fine-grained access policies. It covers PCI for payments, PHI for healthcare and data residency, with integrations for AWS, Google Cloud, Snowflake, Databricks and others.

As of October 2026 Skyflow's homepage leads with Runtime AI Data Control. Its agents page describes a layer between data sources, MCP servers, agents and models that masks, sanitizes and rehydrates data in real time based on policy, identity and context, using what Skyflow calls a polymorphic engine. For MCP it offers a gateway proxy and an embeddable MCP Server SDK. Earlier it launched a GPT Privacy Vault (2023), later marketed as the LLM Privacy Vault. Recent partnerships include Descope for identity-aware MCP controls and Glean. Pricing isn't published; the site routes to demos and sales.

When is Skyflow the better choice?

• You want sensitive data out of your own databases altogether. A vault that holds the originals while your systems hold only tokens shrinks your PCI and data residency scope well beyond AI. Maskflare doesn't store your records; it only keeps the values behind tokens, encrypted, for 30 days so answers can be restored.
• Restore decisions depend on who the agent is. Skyflow ties rehydration to agent identity and runtime context. Maskflare's restore is switched on per gateway key, which is simpler and coarser.
• You need per-region storage for the same dataset. Skyflow's data residency controls are built for keeping records in-region across a global footprint.
• Payments are central. Card data tokenization with a vault is a mature Skyflow use case.

When is Maskflare the better choice?

• You want it running this week. Change your SDK's base URL to https://<your-gateway>/v1/openai (or /anthropic, /gemini and six more) and masking starts on the next request. No data migration, no schema changes, no vault design workshop.
• You'd rather try before a sales call. The trial is self-serve: 28 days or 10,000 requests, whichever comes first.
• Employees use AI tools directly. Maskflare's desktop agent and browser protection mask what people type into ChatGPT, Claude, Gemini, DeepSeek and Perplexity, and restore values in the answer. That's outside a vault's reach.
• Coding agents are in the picture. Hooks for Claude Code, Cursor and Codex CLI block prompts containing secrets and mask tool input and output.
• You want a clear detection story. 574 detectors in 53 packs, including 47 country packs and 232 credential formats, plus a local named-entity layer, with an explain view showing which rule matched.
• EU hosting by default. Maskflare runs in the EU, with on-premises deployment for enterprise.

Skyflow vs Maskflare, feature by feature

What does the Maskflare setup look like?

Here's a support assistant built on Gemini. Before, it sent full customer messages to the model. After, the same code runs through the gateway:

from google import genai

client = genai.Client(
    api_key="mf_live_...",
    http_options={"base_url": "https://<your-gateway>/v1/gemini"},
)

resp = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="Customer Ada Lovelace (card 4532 7781 0021 9047) disputes a CHF 2,400 charge. Draft a reply.",
)
# Gemini saw tokens for the name and card; the reply you get back has the real name.

For retrieval pipelines, deterministic tokens mean the same email becomes the same token every time, so embeddings and search still work without the raw value. The RAG guide covers that pattern.

The honest objections

“A vault is more secure than a gateway.” For data at rest, a well-run vault reduces how many systems hold raw PII, and that's a real benefit. For AI traffic the risk is different: values leaving in prompts to a third-party model. A gateway addresses that without moving your data. Some companies will want both.

“Skyflow has identity-aware rehydration for agents.” It does, and if you need different restore rules per agent identity, that's a point in its favor. Maskflare separates keys by environment (mf_dev_, mf_stg_, mf_live_) and purpose, and redaction-purpose keys can't call forwarding routes, but it doesn't make per-agent identity decisions.

“Skyflow covers agents and MCP too.” It does, through its MCP gateway and SDK. The practical difference is scope and setup. Skyflow's agent controls are tied to its vault and policy model. Maskflare's MCP route, coding-agent hooks and desktop agent work without any vault, and the admin MCP server (uvx maskflare-mcp) lets your own agents read usage and propose rule changes that a person approves in the console.

“We already use Skyflow for payments.” Then keep it for payments. Maskflare can sit on the AI path in front of your models and agents without touching the vault.

What does a realistic split look like?

Take a mid-sized fintech. Card data and identity documents already sit in a vault for PCI reasons. Now the product team is adding an AI assistant that reads support tickets, the data team wants embeddings over CRM notes, and 300 employees use ChatGPT daily.

• Card numbers in the payments system stay vaulted. Nothing changes there.
• Support tickets sent to the model go through the Maskflare gateway. Names, IBANs and phone numbers become tokens; the assistant's draft reply comes back with the real values.
• CRM notes for retrieval are masked with deterministic tokens before embedding, so ada@example.com always maps to the same token and search still works.
• Employees get the desktop agent through Intune or Jamf. What they type into ChatGPT is masked before it leaves the laptop, and restored in the answer.
• Agents that call internal tools through MCP use the gateway's MCP route, so tool arguments and results are masked before the model sees them.

In that setup the vault and the gateway don't compete. The vault reduces where raw data is stored; the gateway controls what leaves for third-party models. Each team keeps the tool that matches its problem, and nobody has to re-architect a working payments flow to ship an AI feature.

What Maskflare doesn't do

• It isn't a system of record. Originals behind tokens expire after 30 days, so it can't replace a vault for long-term tokenized storage.
• Restore decisions aren't tied to individual agent identities; they're set per gateway key.
• Its SOC 2 Type II and ISO 27001 audits are in progress.