Industry range roughly 8-20% in 2026, improved from ~70-75% efficiency (≈25-30% overhead) in 2024-era TEE implementations.
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total monthly confidential-compute cost—premium over standard ($)
—premium over standard (%)
—$/1M tokens confidential vs standard
Standard vs confidential
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Sensitivity to the overhead assumption
Everything else held at the values above, swept across TEE performance overhead, since this is the single most provider- and hardware-generation-dependent input. The highlighted row is closest to your current overhead setting.
| Overhead | Confidential cost | Premium ($) | Premium (%) |
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How this connects to other tools
This calculator prices one specific decision: how much more confidential/TEE-mode inference costs than standard, non-confidential inference for the same workload. If you need to price the underlying standard GPU-hour or per-token cost that this premium sits on top of, use the GPU cloud cost calculator, GPU inference cost calculator or GPU rental cost calculator — none of those model the confidential-compute surcharge, they price raw standard GPU-hours. If your compliance question is really about where inference physically runs rather than how it's isolated in memory, the edge vs cloud calculator covers that separate on-device-vs-cloud tradeoff. And if the driver behind needing TEE at all is EU regulatory exposure specifically, the EU AI Act compliance cost calculator prices the broader compliance-program cost that a TEE requirement is often just one line item within.
How this calculator works
The Confidential Computing (TEE) AI Inference Cost Calculator starts from your monthly requests × average tokens per request to get total monthly tokens — at the defaults, 1,000,000 × 500 is 500,000,000 tokens. Dividing that by 1,000,000 and multiplying by the standard (non-confidential) cost per 1M tokens gives the standard monthly cost — $1,000.00 at the $2.00/1M default. Confidential/TEE mode doesn't add a flat markup on top of that — it reduces effective throughput, because the GPU spends cycles on memory encryption, cryptographic attestation and operates with restricted access to hardware performance counters used for optimization. This calculator models that as confidentialComputeCost = standardMonthlyCost ÷ (1 − overhead% ÷ 100), which is mathematically the same as saying the GPU does the same work in less usable time — at the 12% default, $1,000 ÷ 0.88 = $1,136.36.
On top of that compute cost, some providers charge a small attestation service fee per request to cover the cryptographic handshake that proves the workload is genuinely running inside a verified TEE before sensitive data is sent — at the default $0.0001/request × 1,000,000 requests, that's $100.00/month. Adding compute cost and attestation cost gives the total confidential-compute cost ($1,236.36 at the defaults), and subtracting the standard cost gives the premium in dollars ($236.36) and percent (23.6%) — equivalently, effective cost per 1M tokens rises from $2.00 standard to about $2.47 confidential. The overhead percentage is the input to watch most closely: TEE implementations have improved substantially, with industry reporting moving from roughly 70-75% efficiency (25-30% overhead) in 2024-era H100 confidential-compute and early TDX/SEV-SNP stacks to roughly 85-92% efficiency (8-15% overhead) on current-generation H100/H200 hardware and more mature driver stacks — use the sensitivity table below to see how much that single assumption moves your monthly bill.
Frequently asked questions
What is confidential computing for AI inference?
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.
How much more does TEE/confidential inference cost?
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.
Is the overhead the same for every GPU?
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.
When is the TEE premium worth paying?
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.