Question 1

What is confidential computing for AI inference?

Accepted Answer

Confidential computing for AI inference runs your model and your data inside a hardware-isolated Trusted Execution Environment (TEE) so that memory contents — prompts, model weights, activations — stay encrypted even from the cloud provider's own operating system, hypervisor and administrators. NVIDIA implements this as a confidential-compute mode on H100 and H200 GPUs, where the GPU memory is encrypted and a cryptographic attestation proves to the client that the workload is actually running inside a genuine, unmodified TEE before any sensitive data is sent. On the CPU side, Intel TDX (Trust Domain Extensions) and AMD SEV-SNP (Secure Encrypted Virtualization) provide the equivalent isolation for the host machine coordinating the GPU. The practical effect is that a hospital, bank or government agency can send patient records or financial data to inference running on a third-party cloud's shared GPU fleet with a hardware-backed guarantee that the provider itself cannot read the plaintext data or exfiltrate proprietary model weights, which is why regulated-industry deployments increasingly require it as a checkbox alongside standard controls like encryption-in-transit and access logging.

Question 2

How much more does TEE/confidential inference cost?

Accepted Answer

At this calculator's defaults — $2.00 per 1M tokens standard cost, 12% performance overhead, a $0.0001 per-request attestation fee, 1,000,000 monthly requests averaging 500 tokens each — confidential-mode inference costs about $1,236.36/month versus $1,000.00/month standard, a premium of roughly $236.36 or 23.6%, pushing effective cost from $2.00 to about $2.47 per 1M tokens. Most of that gap is the compute-throughput overhead (the GPU does the same encryption and attestation work regardless of request size, so it processes fewer tokens per second in confidential mode), not the flat attestation fee, which is comparatively small unless request volume is very high with small payloads. The overhead percentage is the input to watch: it varies meaningfully by GPU generation and provider, so this dollar premium should be treated as illustrative until you've confirmed the actual overhead your specific provider and hardware combination delivers.

Question 3

Is the overhead the same for every GPU?

Accepted Answer

No. Confidential-compute overhead depends on the GPU generation, the specific workload (batch size, sequence length, model architecture) and how the provider implements attestation and key management around it. Earlier TEE-capable accelerators and first-generation confidential-mode software stacks reported efficiency in roughly the 70-75% range relative to non-confidential mode — meaning 25-30% overhead — because early memory-encryption and attestation paths added substantial serialization and bandwidth cost. Newer H100/H200 confidential-compute implementations and more mature driver and firmware stacks have pushed that up to roughly 85-92% efficiency, i.e. an overhead closer to 8-15%, as vendors optimized the encryption engines and reduced the attestation checks that sit on the hot path. Smaller batch sizes and shorter sequences tend to show proportionally higher overhead because the fixed per-request attestation and encryption cost is amortized over less actual compute, while large-batch, long-context workloads dilute that fixed cost and show overhead closer to the low end of the range.

Question 4

When is the TEE premium worth paying?

Accepted Answer

The TEE premium is worth paying whenever a regulatory framework, contractual data-processing agreement, or internal risk policy requires hardware-backed proof that a third party cannot access plaintext data or model weights during inference — healthcare workloads touching PHI, financial services handling account or transaction data, government and defense deployments, and any B2B AI vendor whose enterprise customers demand it in a security questionnaire are the clearest cases. It's much harder to justify for internal tooling, prototyping, public or already-anonymized data, or any workload where a signed data-processing agreement and standard encryption-at-rest/in-transit controls already satisfy your compliance obligations, because a 10-25% cost increase compounds fast at scale for no benefit if nobody is actually requiring the hardware attestation. The decision usually isn't really a cost-optimization question at all — it's whether a specific compliance requirement or customer contract mandates TEE, in which case the premium is simply the price of being allowed to serve that workload, and this calculator exists to size that price rather than to argue you out of paying it.

Calculadora de custos de inferência de IA de computação confidencial (TEE)

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Perguntas frequentes

O que é computação confidencial para inferência de IA?

Quanto custa mais o TEE/inferência confidencial?

A sobrecarga é a mesma para cada GPU?

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