Securing cloud AI agents is not a prompt-engineering problem, and AI agent tool permissions are exactly where the risk concentrates. The controls that survive production are a deny-first tool permission layer, an execution identity scoped to a single agent or workload, and an approval gate on every third-party tool the …
Prompt Caching Economics Compared Across Providers
Prompt caching is the cheapest cost lever most LLM teams have not fully exploited: providers charge a fraction of the normal input rate for any prompt prefix your workload reuses, and the discounts are large enough to reorder architecture decisions. DeepSeek bills disk-cache hits at $0.014 per million tokens against …
Kubernetes GPU Scheduling with Node Pools: The Setup
Kubernetes GPU scheduling with node pools comes down to one contract: a vendor device plugin registers the accelerators with the kubelet, the node advertises a schedulable extended resource such as nvidia.com/gpu, and your containers consume it through resource limits. The scheduler then treats a GPU like any other allocatable resource. …
Cut Inference Spend With Model Routing: Practical Guide
Model routing is the highest-leverage cost decision in an LLM stack. Most production traffic does not need a frontier model, and a router that sends simple queries to a cheap endpoint while escalating the hard ones keeps quality where it matters and cuts the bill everywhere else. The evidence is …
LLM Evaluation Pipelines in CI: The Engineer Setup
LLM evaluation pipelines in CI turn model and prompt quality from a manual review into a build gate: a versioned test set, a runner that executes it, and a threshold that fails the pipeline when scores drop. For most engineering teams the practical stack is two open-source tools. promptfoo evaluates …
AWS Console in Brazil: What Engineers Need to Know
Brazilian cloud engineers interact with the AWS Management Console daily, but the experience differs meaningfully from what counterparts in us-east-1 or eu
Choosing Embedding Models for Semantic Search in 2026
Choosing embedding models for semantic search in production comes down to five axes: retrieval quality on a benchmark that matches your languages, context length, dimension and its index cost, Matryoshka (MRL) support for dimension flexibility, and total operating cost across API calls or self-hosted GPUs. Managed APIs such as Cohere …
KV Cache and Batching: Raising LLM Serving Throughput
LLM serving throughput is decided by two memory-side mechanics before any GPU upgrade matters: how the engine stores the key-value (KV) cache and how it refills batches during decoding. Profiling published with the vLLM paper found that in systems that pre-allocate contiguous cache for each request, only 20.4% to 38.2% …
Multi-Region Failover for AI APIs: The Engineer Setup
Multi-region failover for AI APIs is no longer a bespoke project: the three hyperscalers now ship managed routing for model inference, and the engineering work has moved to configuration, policy, and verification. Amazon Bedrock’s global cross-Region inference profiles price approximately 10% below standard regional inference while sending requests to regions …
Serverless Inference Cold Starts: An Engineer’s Guide
Serverless inference cold starts are two stacked problems, not one. The platform provisions compute, attaches storage and pulls your container image; only then does the inference engine load model weights into GPU memory, compile execution graphs and accept traffic. On GPU platforms the second stage dominates: as RunPod documents from …
Fine-Tuning vs Prompt Engineering: Cost Trade-offs
Prompt engineering wins on cost for most production workloads, because inference dominates lifetime spend and prompt-side levers — caching, compression, model routing — cut that bill without any training run. Fine-tuning pays back only in a narrow band: high-volume, stable tasks where shrinking the prompt removes more token cost than …
LLM Observability in Production: The Engineer Setup
Production LLM observability stands on two legs: distributed tracing that records every model call, and evaluation that scores whether those calls were any good. The OpenTelemetry GenAI semantic conventions now give both legs a shared foundation, because they define a common span vocabulary for generative AI operations that any SDK …