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Compare LLM gateway and AI privacy infrastructure approaches
Buyers land here from a few different starting points — evaluating LLM gateway alternatives, evaluating AI privacy infrastructure, or comparing gateway architectures directly. Each page below is grounded in public product documentation and is explicit about what Privian does and does not do today. This directory is navigational, not a ranking — the right choice depends on your requirements.
LLM gateway alternatives
Evaluating LLM gateway options
These pages compare Privian, a privacy-first LLM gateway, against routing- and orchestration-focused gateways. If your primary need is broad provider routing, fallbacks or observability, some of these alternatives may be a better fit than Privian on their own; see the full Privian LLM gateway product page for what Privian specifically implements on the request path.
Portkey
Privian vs Portkey
Choose Privian if you want a privacy-first LLM gateway that masks supported personal and sensitive data before prompts reach GPT, Claude and other models.
LiteLLM
Privian vs LiteLLM
Choose Privian if you want a hosted, privacy-first LLM gateway that masks supported personal and sensitive data before prompts reach the model, with one documented data path for enterprise review.
Cloudflare AI Gateway
Privian vs Cloudflare AI Gateway
Choose Privian if you want a privacy-first LLM gateway that masks supported personal and sensitive data before prompts reach GPT, Claude and other models.
Kong AI Gateway
Privian vs Kong AI Gateway
Choose Privian if you want a privacy-first LLM gateway that masks supported personal and sensitive data before prompts reach the model.
AI privacy infrastructure alternatives
Evaluating AI privacy infrastructure
These pages compare Privian against adjacent privacy infrastructure — data privacy vaults and broader AI privacy platforms. The right layer depends on whether your privacy boundary is structured data at rest or prompt content on the way to a model; see the Privian PII masking product page for the specific masking approach Privian uses on the prompt path.
Skyflow
Privian vs Skyflow
Choose Privian if your primary requirement is prompt privacy — masking supported personal and sensitive entities before prompts reach an LLM, with rehydration in the response.
Private AI
Privian vs Private AI
Choose Privian if you are building or scaling an AI product and need to become enterprise-ready quickly — an opinionated, AI-native privacy platform that ships as a hosted gateway on the LLM path, without adding new infrastructure to run or govern.
Architecture comparison
A note on architecture
Several comparisons above are ultimately architecture questions as much as feature questions.
Routing-first gateways (Portkey, LiteLLM, Cloudflare AI Gateway, Kong AI Gateway) are built around provider abstraction, observability and orchestration; sensitive-data handling on the prompt path is typically left to the caller or a separate layer. Privian's architecture instead runs each request through detection, masking and rehydration in a single in-memory pass before the provider call — see the LLM gateway product page for the full request path.
Vault-style privacy infrastructure (Skyflow) and broader AI privacy platforms (Private AI) protect structured data at rest or across many workloads. Privian is scoped specifically to what reaches an LLM provider in a prompt — see PII masking for how supported entities are detected and masked.
Whichever direction you are evaluating from, current plans and limits are published on the pricing page.
How we write these
Comparison methodology
We compare on publicly documented positioning and capabilities. Where we are not confident in a competitor row, we say so rather than guessing, and we do not publish fabricated benchmarks or attack marketing. Each comparison page states a last-reviewed date and notes that competitor claims should be verified against current vendor documentation during evaluation.
Privian is in active beta. The limitations sections on each page list what we do not do today — that list is meant to shrink over time, and pages will be updated as features land.