Open models in your own cloud, at the level of assurance your reviewers demand.
Secure Inference runs open-weight models on vLLM, SGLang, Triton or Dynamo inside your own AWS account: single-tenant, private subnets, your keys, your audit log, full models at full GPU performance. Three tiers answer three different questions that security reviewers ask, and each one ships with the evidence to answer it.
The three tiers
Secure LLM Inference
"Keep our data in our cloud."
Open-weight models served in your own VPC: single-tenant, private subnets, security groups, KMS-managed keys, audit logging, role-based access. Full models, full GPU performance, your account boundary. Reviewed against the AWS Well-Architected Framework.
Who asks for it: mainstream regulated enterprises moving code generation, document processing and internal assistants onto open models.
Zero-Egress Inference
"Prove it cannot phone home."
A hardened, kernel-level sandbox around the inference stack with zero egress by default, including when running open models from sources you do not control. Each deployment ships a signed evidence pack: the deny policy, complete egress logs, configuration attestation and signed provenance. The proof is the product.
Who asks for it: banks and financial institutions, automotive and heavy industry, anyone whose security review asks for evidence rather than assurance.
Secure AI Enclave
"Not even the operator can see it."
Confidential computing in AWS Nitro Enclaves, a hardware trusted execution environment: data protected in use, no storage, network or operator access, and model weights released only to an enclave that has proved its identity through attestation.
Who asks for it: government, drug discovery and biotech, and product teams whose models and prompts are the business.
What the tiers share
One platform.
The same deployment blueprint, runbooks and runtimes underneath; a tier is a level of assurance, not a different product.
Provenance built in.
The signing and ledger components from our Trust & Provenance work produce the evidence pack in Tier 2 and the attestation record in Tier 3, so what the model saw and where it ran are recorded in the same way.
Customer-resident.
Everything runs in your AWS account. Traction Layer holds none of your data.
Compliance posture.
Controls and logs aligned to NIST AI RMF and EU AI Act Article 12. SOC 2 and ISO/IEC 42001 are on our roadmap and not held.
Traction Layer AI aligns its controls and audit records to NIST AI RMF, ISO/IEC 42001 and EU AI Act Article 12. Alignment is not certification. Traction Layer AI does not hold SOC 2 or ISO/IEC 42001 certification today; both are on our certification roadmap.
Sizing before you commit
Before you commit GPUs, Inference Performance Assurance benchmarks your own models on the platform under your traffic shape, so sizing is measured rather than estimated.