Best AI Gateway for Routing Between Standard and Confidential Compute Models

TL;DR

Most AI gateways solve one problem well: unifying access to dozens of standard models behind a single API, but none of them support confidential compute. This list ranks five gateways on a narrower, harder question: can a team access both standard models and hardware-isolated TEE inference through the same integration, with per-request evidence for which tier actually ran. ORGN leads because it's the only gateway that natively offers both paths, with Scanner producing verifiable receipts specifically for TEE-routed requests.

Why Most AI Gateways Weren't Built for This Question

AI gateways exist to solve a real, common problem: a team wants to call multiple model providers through one integration instead of maintaining separate SDKs, API keys, and error-handling logic for each. Portkey, LiteLLM, OpenRouter, and Cloudflare AI Gateway all do this well, and for most teams, that's the entire requirement.

The problem is narrower and shows up specifically for regulated or IP-sensitive teams: none of the general-purpose gateways above distinguish between models running on standard shared infrastructure and those running inside hardware-isolated Trusted Execution Environments (TEEs). A gateway can unify access to fifty models and still leave every one of those fifty processing prompts in plaintext memory on infrastructure the gateway provider doesn't control end-to-end. Unifying access and isolating execution are two distinct engineering problems, and most gateways were built only to solve the first.

The five tools below are evaluated specifically on whether, and how, they handle both. For four of them, that means being clear about what they don't do, since claiming confidential compute support without meaning it is exactly the kind of procurement mismatch that fails a security review later.

The 5 Best AI Gateways for Standard and Confidential Compute Model Access

1. ORGN Gateway: The Only Gateway With Native TEE and Standard Model Paths

ORGN Gateway is built around a distinction the other tools on this list don't make: standard models and TEE-backed models are two structurally different execution paths, not two entries in the same model catalog with a routing rule between them.

Model selection in ORGN Gateway is always explicit at the application layer; a developer specifies a model string directly, and there's no policy-driven auto-routing deciding which tier handles a given request. Selecting a TEE model string, such as phala_deepseek_v3_1 or near_qwen3_30b, routes that specific inference call to TDX infrastructure and produces a per-request attestation receipt in Scanner, ORGN's public, no-login attestation explorer. What fields that receipt contains vary by provider; Phala and NEAR return different evidence artifacts, but the receipt itself is independently verifiable without trusting ORGN's own infrastructure logs. Selecting a ZDR model string, such as a vercel_* alias, still runs within the same CDE-layer TDX sandbox at the workspace level, but that specific inference call produces no hardware receipt; the ZDR tier's guarantee is contractual, not cryptographic.

That two-tier structure is the sole differentiator: one gateway, one integration, and a clear, per-request answer about which kinds of evidence exist for any given call.

Who it's for: Teams that need both a broad standard model catalog for everyday work and a verifiable, hardware-attested path for sensitive inference, without maintaining two separate gateway integrations.

Key capabilities:

• Two distinct model tiers accessible through one gateway: TEE-backed (Phala/NEAR, TDX-isolated) and ZDR (Vercel-routed, contractual)
• Per-request attestation receipts in Scanner for TEE-tier calls, independently verifiable against public PKI
• Explicit model-string selection, no dynamic routing between tiers, so evidence trail is always predictable from the request itself
• CDE-layer TDX sandbox isolation applies to the whole workspace regardless of which model tier is called
• Air-gapped and private deployment available on the Enterprise tier

Pricing: Credit-based, pay-as-you-go. Self-serve from $25 in prepaid credits, no subscription required. Enterprise and air-gapped deployment pricing on request.

2. Portkey: Strong Multi-Provider Routing, No Confidential Compute Layer

Portkey is one of the more mature general-purpose AI gateways, built around production-grade routing features, load balancing, automatic fallback, retries, and semantic caching across more than 250 models from over 40 providers.

Where Portkey excels is exactly what a general-purpose gateway should: if a primary provider is rate-limited or degraded, Portkey can automatically fail over to a backup, and its observability layer provides granular visibility into cost and latency per request. What it doesn't offer is any distinction in execution guarantees between the models it routes to; every model behind Portkey is treated identically from an isolation standpoint, because Portkey's routing logic operates at the API layer, not the hardware layer. For teams that only need broad model access with strong reliability engineering, that's not a gap. For teams that need to distinguish between confidential and standard inference within the same gateway, Portkey doesn't provide that layer.

Who it's for: Teams prioritizing production reliability, automatic failover, caching, and cost routing, across a wide model catalog, with no requirement for hardware-level execution isolation.

Key capabilities:

• Routing across 250+ models from 40+ providers with automatic fallback and load balancing
• Semantic caching to reduce redundant inference costs
• Granular per-request observability: cost, latency, token usage
• Guardrails and policy enforcement at the request level (content, not execution environment)

Pricing: Free tier available. Paid plans scale with request volume; Enterprise pricing on request.

3. LiteLLM: Open-Source Flexibility, Self-Hosted Standard Model Access

LiteLLM is the open-source option on this list, a proxy server that normalizes API calls across dozens of providers into a single OpenAI-compatible format, deployable either as a hosted service or entirely self-managed.

The appeal of LiteLLM for privacy-conscious teams is that self-hosting keeps the gateway layer entirely inside infrastructure the team already controls; no data passes through a third-party gateway provider's servers. That's a genuine, valuable control, and it's the same network-boundary isolation pattern that shows up in other self-hosted AI tooling. What it doesn't produce is hardware-level execution proof: a self-hosted LiteLLM instance still runs on a conventional VM, where the hypervisor retains full memory visibility during inference. Network-boundary isolation and hardware-enforced isolation solve different problems; however, even when self-hosted, LiteLLM addresses only the first.

Who it's for: Engineering teams that want full infrastructure control and are comfortable self-hosting, whose privacy requirement is satisfied by keeping the gateway off third-party servers rather than by hardware attestation.

Key capabilities:

• Open-source, self-hostable proxy supporting 100+ LLM providers behind one OpenAI-compatible API
• Full control over deployment infrastructure, no third-party gateway operator in the request path
• Built-in cost tracking, rate limiting, and retry logic
• Active open-source community and provider support

Pricing: Free and open-source for self-hosted deployment. Hosted/managed tier available with usage-based pricing.

4. Cloudflare AI Gateway: Edge-Level Routing With No Isolation Guarantee

Cloudflare AI Gateway sits on Cloudflare's global edge network, adding caching, rate limiting, and analytics in front of calls to standard model providers like OpenAI, Anthropic, and others.

Cloudflare's advantage is its infrastructure scale: requests are cached, rate-limited, and logged at the edge with the same reliability Cloudflare applies across its broader network products. For teams already running application infrastructure on Cloudflare, adding AI Gateway is a low-friction way to get observability and cost control without a new vendor relationship. Like Portkey, though, Cloudflare AI Gateway operates purely at the API and caching layer; it doesn't distinguish between models running in standard multi-tenant infrastructure and models running in any form of hardware-isolated environment, because that distinction isn't part of what the product is built to track.

Who it's for: Teams already invested in Cloudflare's infrastructure who want edge-level caching, analytics, and rate limiting in front of standard model calls, without a confidential compute requirement.

Key capabilities:

• Edge-based caching and rate limiting across supported model providers
• Unified analytics and cost tracking across connected providers
• Low-latency request handling via Cloudflare's global network
• Simple integration for teams already on Cloudflare's platform

Pricing: Usage-based, integrated into Cloudflare's broader platform pricing. Free tier available for lower-volume use.

5. OpenRouter: The Simplest Path to Broad Model Access

OpenRouter's entire value proposition is simplicity: one API key, one endpoint, access to hundreds of models across dozens of providers, with automatic fallback if a specific model is unavailable.

For a developer or small team that wants to experiment across frontier models without negotiating separate agreements with each provider, OpenRouter is often the fastest path to a working integration. Pricing is transparent and pay-as-you-go per model, and the catalog is broad. What OpenRouter doesn't offer, and doesn't claim to, is any execution-environment guarantee beyond whatever the underlying model provider's own infrastructure provides. There's no tier distinction, no attestation layer, and no isolation claim beyond standard API-level access.

Who it's for: Individual developers and small teams who want the simplest possible path to broad model access, with no confidential compute requirement.

Key capabilities:

• Single API key and endpoint for hundreds of models across dozens of providers
• Transparent, pay-as-you-go per-model pricing
• Automatic fallback if a specific model is temporarily unavailable
• Minimal setup, no infrastructure or self-hosting required

Pricing: Pay-as-you-go per model, no subscription. Pricing varies by model selected.

Matching the Gateway to What You Actually Need to Prove

The decision here comes down to one question: does any part of your workload require a verifiable, hardware-level record of where inference ran, or is broad model access with strong routing and observability sufficient?

If part of your workload requires hardware-attested inference, sensitive IP, regulated data, or a compliance review that requests execution-environment evidence, ORGN Gateway is the only tool on this list that offers that path natively, alongside standard model access in the same integration.

If your requirement is purely about reliability and breadth across standard models, Portkey's routing and failover, LiteLLM's self-hosted flexibility, Cloudflare's edge-level caching, or OpenRouter's simplicity are all strong options; the right pick depends on whether you value production-grade reliability engineering (Portkey), full infrastructure control (LiteLLM), existing platform integration (Cloudflare), or fastest time to a working integration (OpenRouter).

Conclusion

Most AI gateways answer the same question well: how do I call many models through a single integration instead of many separate ones? None of the general-purpose options on this list answer a narrower, harder question that only matters once a workload becomes sensitive enough to need it: can this specific request be proven, after the fact, to have run inside an isolated environment? That's not a routing feature. It's an entirely different architecture, and bolting it onto a gateway that wasn't built for it isn't something any of these four tools claim to do.

If part of your stack needs that proof, even occasionally, even for one sensitive workload alongside everything else, get started with ORGN Gateway and see what it looks like to get both standard model access and verifiable TEE inference through the same integration, without maintaining two separate gateways.

FAQs

What's the difference between an AI gateway that routes between providers and one that offers confidential compute?

A routing gateway unifies API access across multiple model providers, handling fallback, load balancing, and observability at the application layer; every model behind it is treated as equally trusted infrastructure. A gateway with confidential compute support goes further, offering at least one execution path in which inference runs inside a hardware-isolated Trusted Execution Environment, with cryptographic evidence afterward proving that isolation was maintained. Most gateways only do the former.

Can a self-hosted gateway like LiteLLM provide the same guarantees as a TEE-backed one?

No, they solve different problems. Self-hosting a gateway keeps requests off a third-party gateway provider's servers, providing real network-boundary control. It doesn't change what happens on the underlying VM during inference: the hypervisor still has full memory visibility. A TEE-backed path encrypts memory at the hardware level during execution, which a self-hosted gateway running on conventional infrastructure doesn't provide, regardless of how tightly the network boundary is controlled.

Does using a gateway with TEE model options mean every request through it is hardware-isolated?

Not necessarily, and this is a common point of confusion. In gateways that offer both standard and TEE-backed models, isolation typically applies per request based on which specific model was called; a standard model call through the same gateway doesn't inherit the TEE guarantee just because the gateway also offers TEE options elsewhere in its catalog. Checking which specific model string was used for a given request is the only way to know which guarantee applied.

Why do TEE-backed inference gateways typically support fewer models than general-purpose ones?

Hardware-isolated inference requires the underlying model provider to support TEE deployment specifically, which is a narrower engineering lift than standard API hosting. As of now, that means TEE-backed catalogs are smaller, often specific model families like DeepSeek or Qwen variants, while ZDR or standard routing can draw from a much broader set of frontier models. The trade-off is between catalog breadth and verifiable isolation, and most gateways that offer both let a team choose per request rather than forcing a single trade-off across all traffic.

What should a security review ask an AI gateway vendor to confirm about model execution?

Ask specifically which models, if any, produce a per-request attestation artifact independently verifiable against a public root of trust, rather than accepting a general claim of "confidential compute support." Many vendors describe standard encrypted transport or a contractual no-retention policy as confidential compute, which is a different and weaker guarantee than hardware-enforced isolation. The more precise question, "which specific model calls generate a receipt I can check myself", surfaces that distinction directly.