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Enclave offers wallet-based, per-second confidential GPU compute for teams that need attestation before sending sensitive model data. Enclave is a self-serve confidential GPU compute platform for teams that want to run sensitive AI or ML workloads without treating the infrastructure operator as fully trusted. The practical hook is simple: you deploy a WebAssembly app from the browser, choose CPU and GPU shares, pay per second in ETH or USDC, then verify CPU and GPU attestation before sending data. The site exposes the important security details instead of hiding them behind a sales form: AMD SEV-SNP on the current fleet, NVIDIA GPU confidential-computing mode, TLS key binding inside the enclave, and a browser-verifiable attestation flow. Use it when you need a short-lived endpoint for private inference experiments, sensitive data processing, customer demos, or proof-of-concept work where ordinary GPU rental feels too exposed. It is also useful as a research target for teams evaluating whether confidential GPU workloads fit their threat model. What to check before using it: Verify attestation yourself before sending private data. Start with a small top-up because payments are final and unspent runtime is not withdrawable. Read the shared-GPU caveat: operator isolation is hardware-backed, but co-tenant isolation on shared cards depends on Wasm sandboxing, process boundaries, and the NVIDIA driver rather than per-tenant hardware partitioning. Use a full-GPU deployment when your threat model requires no co-tenants on the card. Treat the service as beta infrastructure until you have tested deployment, logs, costs, and failure behavior with non-critical workloads. Visible pricing on the Enclave page lists GPU share at $6/hour for a full card plus CPU share at $3/hour for a full node, metered per second with whole-percent shares. The vendor gives example points such as 10% GPU plus 5% CPU at about $0.75/hour and 100% GPU plus 10% CPU at about $6.30/hour. This is a TOOL candidate, not a blog story: the value is a concrete resource builders can try, evaluate, or bookmark when confidential AI compute matters.
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Auriko is a newly launched LLM routing layer for teams that want one API, provider failover, budget controls, and cache-aware cost routing without paying a markup on model calls. Auriko is worth testing if your agent or app traffic already spans several model providers and you keep fighting token-cost drift, cache behavior, or failover rules by hand. It exposes an OpenAI-compatible API, supports routing strategies for cost, latency, throughput, and reliability, and lets teams use their own provider keys, Auriko platform keys, or both. The practical angle is cost control for repeated prompts, agent loops, RAG blocks, and long-running coding workflows. Auriko's technical report says its benchmark covered more than 80,000 API requests across 37 models and found positive cost reduction against all tested comparator targets. Treat that as vendor-run evidence, not a guarantee for your workload. Check What to verify before switching traffic --- --- Workload fit Repeated context, tool schemas, long instructions, and multi-turn sessions should benefit more than one-off calls. Cost claim Re-run your own prompts because provider prices, cache rules, and model availability change quickly. Output stability Routing can change latency and behavior even when the requested model name stays the same. Data policy Confirm zero-data-retention, BYOK, provider selection, and enterprise controls before sending sensitive prompts. Integration Start with the OpenAI-compatible endpoint or SDK before wiring custom routing rules. Use it as a measurement project first: mirror a small slice of traffic, compare total cost, error rate, latency, and output quality, then decide whether cache-aware routing belongs in production.
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