AI & Cloud Infrastructure
The cloud, data and model foundations an AI workload needs to run in production.
About the serviceRunning AI in production on AWS and Azure: architecture, security, cost and operations
Running AI in production on AWS or Azure means giving a model the same controls as any business system: identity, secrets, logging, cost limits, fallbacks and a named owner for every operating task. Managed services such as Amazon Bedrock and Azure OpenAI in Microsoft Foundry host the models, but the architecture, data boundaries and day-to-day operations remain your decisions to make and assign. This guide is for IT and operations leaders at companies of roughly 20 to 200 people.
Start with the guideArticles and guides
The encryption techniques that matter, and what the industry uses by defaultThe types of encryption that matter in practice are AES-256-GCM for stored data, TLS 1.3 for traffic, envelope encryption with a cloud key service for data at rest, Argon2id hashing for passwords, and hybrid post-quantum key exchange, which browsers already use. This guide is for operations, IT and finance leaders who need to know the current default for each job and the questions to put to a vendor. Never invent your own cryptography.Read How to build a system that handles hundreds of millions of data pointsA high volume data pipeline works in stages: a durable ingestion buffer, stream processing, raw data kept cheaply in object storage as the source of truth, and pre-aggregated rollups that answer the questions people actually ask. The design choice that matters most is deciding those questions first, because they set what you aggregate and how long you keep detail. This guide is for operations and IT leaders at companies of roughly 20 to 200 people who collect sensor, image or event data at volume.Read How Meta handles billions of requests, and what your company should copyMeta (formerly Facebook) serves its social graph through layers: load balancers, a very large cache, and a sharded MySQL tier behind it. A company of 20 to 200 people should copy the order of operations, which is to measure, index, cache, add read replicas, partition, and shard last, because most never need sharding. This guide explains each technique and when it starts paying off.Read When to use pub/sub, a queue or Kafka: a plain guide for growing businessesUse pub/sub when one event must reach several systems, a queue when each task should go to one worker, with retries and a handler that tolerates the occasional duplicate, and a direct API call when the caller needs the answer now. Most businesses of 20 to 200 people should start with a managed queue or topic on AWS or Azure, and move to a Kafka-style log only when several consumers must replay history at their own pace or event volume grows past what a queue handles comfortably.Read How Vercel runs modern frameworks, and when to choose it over AWS or AzureVercel suits web front ends and framework applications owned by a small team, because it derives the infrastructure from your code and bills for active compute. AWS or Azure suits work that needs private networking, a specific compliance posture, long-running or GPU workloads, or integration with an existing cloud estate, and many setups use both. This guide is based on Vercel's documentation as of October 2026 and is written for technical and operations leaders choosing where to host a business web application.Read What is WebRTC? How real-time voice and video work, and when to buy it instead of building itWebRTC is the open set of web standards that lets browsers and apps send live audio, video and data to each other with very short delay, encrypted by default. Businesses meet it in video support, in-browser phone lines and voice AI agents. Buy it through a managed provider unless you have a team to run relay and media servers. Written for technical leads and the operations leaders who sponsor them, at companies of roughly 20 to 200 people.Read Why Postgres became the default database, and is it really faster?Postgres became the default because it is reliable, extensible and free, not because it is faster than every other SQL database. No engine is fastest across all workloads, and MySQL, SQL Server or Oracle can win for specific ones. This guide explains where Postgres came from, what makes it strong, and where it is weak, for leaders choosing the database behind a business application.Read How to write an AI acceptable use policy for employeesAn AI acceptable use policy tells employees which AI tools they may use, what data may go into them, how closely to check the output, and who to ask when a new tool shows up. This guide for companies of roughly 20 to 200 people includes a sample policy you can copy, a plan-by-plan comparison of vendor data terms and a rollout plan. A workable policy fits on two pages, matches its rules to how sensitive the data is, and names one owner who keeps it current.Read AI data security and governance for mid-size companies: a practical checklistAI data security and governance means knowing which data each AI workflow touches, approving the sources it may use, checking what your AI provider does with prompts and outputs, and making the AI respect the same permissions as the person using it. This guide for companies of roughly 20 to 200 people also covers defenses against prompt injection, logging with sensible retention and human approval for consequential actions, with frameworks such as the NIST AI RMF and ISO/IEC 42001 giving that work a structure.Read AWS vs Azure for AI: Bedrock or Foundry, how to chooseFor most companies, the right cloud for AI is the one where their identity, data and technical team already sit. As of October 2026, Amazon Bedrock and Microsoft Foundry both offer models from OpenAI, Anthropic and other providers, so model choice rarely settles the question alone. Identity, data location, compliance scope, skills and existing agreements usually do, and this guide for companies of roughly 20 to 200 people puts the comparison table first.Read How to connect AI to your ERP, CRM and data warehouseConnect AI to your ERP, CRM and data warehouse through the vendor's supported APIs, webhooks, event streams or an integration platform, not by writing straight to the database. Start with read access, send every write through a named approver until the workflow has proven itself, and check API limits, licenses, permissions and data quality before you commit to a design.Read Planning a legacy application migration to the cloudA legacy application migration to the cloud starts with deciding, application by application, whether to retire, retain, rehost, replatform, refactor or replace it. Then map dependencies and data, set a cost baseline, plan cutover with a tested rollback, and treat security and identity as part of the move rather than an afterthought. Add AI or automation during the migration only where you are already changing that part of the system.Read Model-agnostic AI architecture: avoiding vendor lock-inA model-agnostic AI architecture puts one internal interface between your application and any model provider, so swapping models is a configuration change rather than a rewrite. It matters because providers publish retirement rules: Microsoft Foundry retires generally available models 12 to 18 months after launch, and OpenAI, Anthropic and Amazon Bedrock set notice periods from 45 days to 6 months. The real vendor lock-in usually sits elsewhere: in proprietary vector stores, agent frameworks, fine-tunes and contract commitments.Read Monitoring AI and LLM applications in production: what to trackMonitoring AI in production means tracking four things uptime checks miss: answer quality, cost per task, drift when inputs change, and whether the human approval step is keeping up. Log every model call and tool action with sensitive data protected, sample real outputs for review, and alert on thresholds a named owner agreed in advance. Write down how to roll back before you need to.Read RAG vs fine-tuning vs prompting: how to give AI your company's knowledgeUse RAG when the AI must answer from changing company documents and cite sources, and fine-tuning when it must behave consistently, such as a fixed format or narrow classification. Start with prompting, because a small, stable body of context often needs neither. This guide is written for business buyers at companies of roughly 20 to 200 people.Read
Tools and checklists
AI data readiness checkCheck the data behind one workflow across five areas, and get the three steps that would make it ready for AI.Open the tool AI cloud readiness checkEight questions on running an AI workload on AWS or Azure. See your readiness by area and what to set up first.Open the tool AI acceptable use policy builderAnswer questions about the AI tools your people use, the data they handle and who reviews what, and get a draft policy your team can adapt.Open the tool AWS or Azure for AI: decision checklistQuestions to answer before choosing AWS or Azure for an AI workload, covering identity, data location, model access, skills, support and commercial terms.See the checklist
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