{"id":31126,"date":"2026-08-27T10:14:19","date_gmt":"2026-08-27T03:14:19","guid":{"rendered":"https:\/\/renovacloud.com\/?p=31126"},"modified":"2026-08-27T10:14:19","modified_gmt":"2026-08-27T03:14:19","slug":"what-is-amazon-bedrock-2026-guide","status":"publish","type":"post","link":"https:\/\/renovacloud.com\/en\/what-is-amazon-bedrock-2026-guide\/","title":{"rendered":"What is Amazon Bedrock? Foundation Models, Pricing, and How It Works (2026 Guide)"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Amazon Bedrock is the AWS service that gives you API access to foundation models from Anthropic, Amazon, Meta, Mistral AI, OpenAI and a dozen other providers, all through a single endpoint inside your own AWS account. You pick a model, send a prompt, and pay per token. AWS handles the GPUs, the scaling, and the uptime.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The definition is the easy part. What follows is where the money and the architecture decisions live, starting with which of the 60-plus available models suits your workload and why the first invoice rarely matches the pricing page.<\/span><\/p>\n<h2><b>What is Amazon Bedrock?<\/b><\/h2>\n<p><a href=\"https:\/\/aws.amazon.com\/bedrock\/\" rel=\"noopener\"><span style=\"font-weight: 400;\">Amazon Bedrock<\/span><\/a><span style=\"font-weight: 400;\"> is a serverless, fully managed service for building generative AI applications. Requests run inside the AWS perimeter, billing lands on your existing AWS account, and access is controlled by the same IAM policies and KMS keys you already use for Amazon S3 or Amazon RDS.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-31114\" src=\"http:\/\/renovacloud.com\/wp-content\/uploads\/2026\/08\/image1.png\" alt=\"Single API access to many foundation models.\u00a0\" width=\"1024\" height=\"765\" \/><\/p>\n<p><span style=\"font-weight: 400;\">AWS states that Bedrock never stores your data or uses it to train models, with encryption applied both in transit and at rest. For banks, insurers, hospitals, and any company operating under Vietnam&#8217;s data protection rules, that posture is usually the first reason Bedrock lands on the shortlist ahead of a public chatbot API.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Bedrock absorbs five things your team would otherwise build and operate:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">GPU provisioning, capacity planning, and cluster maintenance<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A separate contract, invoice, and API integration for every model vendor<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Vector database setup and tuning for retrieval workloads<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Content filtering and PII redaction built from scratch<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Model swapping, which becomes a configuration change instead of a rewrite<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">You accept AWS as an intermediary between your application and the model. In exchange you get one bill, one security boundary, and one API surface. Most AWS-native teams take that deal without much debate.<\/span><\/p>\n<h2><b>The Foundation Models Available on Amazon Bedrock\u00a0<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">The catalogue has grown from a handful of models in 2023 to well over 60 text models, plus embedding, image, video, and speech models, from roughly 18 providers as of mid 2026.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Picking one comes down to a narrow question. Which is the cheapest model that still clears your quality bar on the specific task in front of you?<\/span><\/p>\n<table style=\"height: 457px;\" width=\"1275\">\n<tbody>\n<tr>\n<td>\n<p style=\"text-align: center;\"><b>Provider<\/b><\/p>\n<\/td>\n<td style=\"text-align: center;\"><b>Representative models<\/b><\/td>\n<td>\n<p style=\"text-align: center;\"><b>Where teams usually put them<\/b><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Anthropic<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Claude Opus, Sonnet, Haiku<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Reasoning, long documents, agent workflows<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Amazon<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Nova Micro, Lite, Pro, Premier, and Titan<\/span><\/td>\n<td><span style=\"font-weight: 400;\">High-volume classification, routing, embeddings<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Meta<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Llama 4, Llama 3.3<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Open-weight deployments, fine-tuning<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Mistral AI<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Mistral Large 3, Ministral, Devstral<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Cost-sensitive European and multilingual work<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">OpenAI<\/span><\/td>\n<td><span style=\"font-weight: 400;\">GPT-5.5, GPT-5.4, gpt-oss<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Frontier reasoning inside an AWS account<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">DeepSeek, Qwen, Z AI, Moonshot<\/span><\/td>\n<td><span style=\"font-weight: 400;\">DeepSeek v3.2, Qwen3, GLM 5, Kimi K2.5<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Cheap high-volume inference, coding tasks<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Stability AI, Luma AI, TwelveLabs<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Stable Image, Marengo, Pegasus<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Image editing, video search and description<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3><b>Anthropic Claude on Bedrock<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Claude is the most requested family in the enterprise projects we scope, mostly for how it handles long documents and structured output. AWS publishes the full<\/span><a href=\"https:\/\/aws.amazon.com\/bedrock\/anthropic\/\" rel=\"noopener\"> <span style=\"font-weight: 400;\">Claude on Bedrock<\/span><\/a><span style=\"font-weight: 400;\"> rate card, including prompt caching rates that cut repeated-input costs sharply for chatbots and document assistants.<\/span><\/p>\n<h3><b>Amazon Nova and Titan<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Amazon&#8217;s own models sit at the bottom of the price range and handle a surprising share of production work. Classification, intent routing, data extraction, and summarisation of short text rarely need a frontier model. Routing those calls to Nova Micro or Nova Lite instead of a frontier model tends to cut that slice of the bill by an order of magnitude.<\/span><\/p>\n<h3><b>Open-Weight Models<\/b><\/h3>\n<p><a href=\"https:\/\/aws.amazon.com\/bedrock\/meta\/\" rel=\"noopener\"><span style=\"font-weight: 400;\">Meta Llama<\/span><\/a><span style=\"font-weight: 400;\">, Mistral, Qwen, and the gpt-oss family are available as managed endpoints, which means you get open-weight economics without running the infrastructure. Bedrock also supports Custom Model Import, so a model you fine-tuned elsewhere can be hosted on Bedrock and billed per Custom Model Unit per minute.<\/span><\/p>\n<h3><b>Frontier and Specialist Models<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">GPT-5.5 arrived on Bedrock in April 2026 at $5.50 per million input tokens and $33.00 per million output tokens in US East (Ohio). At the other end, GLM 4.7 Flash runs at $0.07 input and $0.40 output. That is a 78x spread on input tokens between two models that can both summarise a support ticket. Model choice moves the bill further than anything else you will tune.<\/span><\/p>\n<p><i><span style=\"font-weight: 400;\">&gt;&gt;&gt; Read more:<\/span><\/i><a href=\"https:\/\/renovacloud.com\/en\/why-businesses-need-amazon-bedrock-consulting-in-vietnam\/\"> <i><span style=\"font-weight: 400;\">Why Businesses Need Amazon Bedrock Consulting in Vietnam<\/span><\/i><\/a><\/p>\n<h2><b>How Amazon Bedrock Works<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Six layers sit on top of the model endpoint. Most teams use the first one on day one and add the rest as the application grows past its prototype.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-31124\" src=\"http:\/\/renovacloud.com\/wp-content\/uploads\/2026\/08\/image6.png\" alt=\"Workflow diagram of Amazon Bedrock request layers.\u00a0\" width=\"1024\" height=\"765\" \/><\/p>\n<h3><b>A Single API Across Every Model<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">The Converse API accepts the same request shape for every supported model, which is what makes swapping models a configuration change. Streaming, tool use, and system prompts work consistently across providers. Cross-region inference routes traffic across AWS regions during traffic spikes without charging you extra for the routing.<\/span><\/p>\n<h3><b>Knowledge Bases for Retrieval<\/b><\/h3>\n<p><a href=\"https:\/\/aws.amazon.com\/bedrock\/knowledge-bases\/\" rel=\"noopener\"><span style=\"font-weight: 400;\">Amazon Bedrock Knowledge Bases<\/span><\/a><span style=\"font-weight: 400;\"> handles retrieval augmented generation end to end. The managed version, generally available since June 2026, ships with six native connectors covering Amazon S3, SharePoint, Confluence, Google Drive, OneDrive, and a web crawler, plus automatic syncing and managed vector storage.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Vector storage is the line item that catches people out. The default backing store has historically been Amazon OpenSearch Serverless, which carries a two-unit minimum that works out to several hundred US dollars a month even at zero query traffic.<\/span><a href=\"https:\/\/aws.amazon.com\/blogs\/aws\/category\/artificial-intelligence\/amazon-machine-learning\/amazon-bedrock\/amazon-bedrock-knowledge-bases\" rel=\"noopener\"> <span style=\"font-weight: 400;\">Amazon S3 Vectors<\/span><\/a><span style=\"font-weight: 400;\"> offers up to 90% lower vector storage cost and has become the sensible default for new knowledge bases.<\/span><\/p>\n<h3><b>AgentCore for Production Agents<\/b><\/h3>\n<p><a href=\"https:\/\/aws.amazon.com\/bedrock\/agentcore\/faqs\/\" rel=\"noopener\"><span style=\"font-weight: 400;\">Amazon Bedrock AgentCore<\/span><\/a><span style=\"font-weight: 400;\"> is the agent platform layer, covering runtime, memory, identity, gateway, browser, code interpreter, observability, policy, and evaluations. It works with LangGraph, CrewAI, LlamaIndex, Strands Agents, and the OpenAI Agents SDK, and it supports both MCP and the Agent-to-Agent protocol.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">AgentCore is available in fifteen AWS regions, including Singapore, Tokyo, Sydney, and Mumbai.<\/span><\/p>\n<h3><b>Guardrails for Safety and Compliance<\/b><\/h3>\n<p><a href=\"https:\/\/aws.amazon.com\/bedrock\/guardrails\/\" rel=\"noopener\"><span style=\"font-weight: 400;\">Amazon Bedrock Guardrails<\/span><\/a><span style=\"font-weight: 400;\"> applies policy at the inference boundary. Six safeguard types are available: content filters, denied topics, sensitive information filters, word filters, contextual grounding checks, and Automated Reasoning checks. AWS reports that Guardrails blocks up to 88% of harmful content, and that Automated Reasoning checks validate responses against a formal policy with up to 99% accuracy.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The ApplyGuardrail API works with models hosted outside Bedrock too, which lets one policy layer cover a mixed estate.<\/span><\/p>\n<h3><b>Model Evaluation<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Automated scoring is free. Human evaluation costs $0.21 per completed task, and LLM-as-a-judge evaluation bills the judge model&#8217;s tokens at standard on-demand rates. Building an evaluation set before shipping is the difference between knowing a model swap cost you 2% quality and guessing.<\/span><\/p>\n<h3><b>Fine-Tuning and Customisation<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Bedrock supports fine-tuning, continued pretraining, model distillation, and reinforcement fine-tuning. Custom text models require Provisioned Throughput for inference, and custom model storage runs $1.95 per model per month.<\/span><\/p>\n<p><i><span style=\"font-weight: 400;\">&gt;&gt;&gt; Read more:<\/span><\/i><a href=\"https:\/\/renovacloud.com\/en\/how-to-implement-ai-agents-on-aws\/\"> <i><span style=\"font-weight: 400;\">How to Implement AI Agents on AWS in 2026<\/span><\/i><\/a><\/p>\n<h2><b>Amazon Bedrock Pricing in 2026<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Token rates are the part everyone budgets for. The rest of the bill comes from vector stores, guardrail filters, agent loops, and retries, which is why the first invoice is usually the surprising one.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-31122\" src=\"http:\/\/renovacloud.com\/wp-content\/uploads\/2026\/08\/image5.png\" alt=\"An analyst reviewing a cloud cost optimization dashboard.\u00a0\" width=\"1024\" height=\"765\" \/><\/p>\n<h3><b>How Token Billing Works<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">You pay separately for input tokens and output tokens, at a rate set by the model and the region. Output usually costs three to six times the input rate. Image models bill per image, video models bill per second or per minute, and embedding models bill input tokens only. Regional pricing varies, with Asia Pacific and Europe running roughly 15% to 25% above US East for the same model.<\/span><\/p>\n<h3><b>Service Tiers: Standard, Priority, Flex, and Batch<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">AWS now exposes four<\/span><a href=\"https:\/\/aws.amazon.com\/bedrock\/service-tiers\/\" rel=\"noopener\"> <span style=\"font-weight: 400;\">service tiers<\/span><\/a><span style=\"font-weight: 400;\"> that trade latency against price on the same underlying model.<\/span><\/p>\n<table style=\"height: 302px;\" width=\"1275\">\n<tbody>\n<tr>\n<td>\n<p style=\"text-align: center;\"><b>Tier<\/b><\/p>\n<\/td>\n<td style=\"text-align: center;\"><b>Price relative to Standard<\/b><\/td>\n<td>\n<p style=\"text-align: center;\"><b>Best suited to<\/b><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Priority<\/span><\/td>\n<td><span style=\"font-weight: 400;\">75% premium<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Latency-sensitive user-facing traffic<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Standard<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Baseline<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Interactive applications, default choice<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Flex<\/span><\/td>\n<td><span style=\"font-weight: 400;\">50% discount<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Background jobs that tolerate slower responses<\/span><\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/docs.aws.amazon.com\/bedrock\/latest\/userguide\/batch-inference.html\" rel=\"noopener\"><span style=\"font-weight: 400;\">Batch<\/span><\/a><\/td>\n<td><span style=\"font-weight: 400;\">50% discount<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Bulk jobs delivered to S3, up to 24 hour turnaround<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-weight: 400;\">Nightly enrichment, bulk classification, historical backfills, and content generation pipelines all belong in Batch or Flex. Nothing about the model or the prompt changes, which makes this the least disruptive saving on the list.<\/span><\/p>\n<h3><b>Provisioned Throughput and Reserved Capacity<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Provisioned Throughput reserves model units at a fixed hourly rate, with commitment terms of one month or six months. Six-month terms carry the lower hourly rate. The economics work like Reserved Instances, so the break-even point sits somewhere around 80% sustained utilisation. Below that, on-demand is cheaper.<\/span><\/p>\n<h3><b>Feature Charges Beyond Inference<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">These line items appear on the bill separately from token spend.<\/span><\/p>\n<table style=\"height: 624px;\" width=\"1275\">\n<tbody>\n<tr>\n<td>\n<p style=\"text-align: center;\"><b>Feature<\/b><\/p>\n<\/td>\n<td>\n<p style=\"text-align: center;\"><b>Published rate<\/b><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Guardrails content filters and denied topics<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$0.15 per 1,000 text units<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Guardrails sensitive information filters<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$0.10 per 1,000 text units<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Guardrails contextual grounding checks<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$0.10 per 1,000 text units<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Guardrails Automated Reasoning checks<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$0.17 per 1,000 text units per policy<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Guardrails word filters and regex filters<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Free<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Bedrock Data Automation, standard document output<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$0.010 per page<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Bedrock Data Automation, video standard output<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$0.050 per minute<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Intelligent Prompt Routing<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$1.00 per 1,000 requests<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Simple Prompt Optimizer<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$0.03 per 1,000 tokens<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Human model evaluation<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$0.21 per task<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-weight: 400;\">A text unit covers up to 1,000 characters, and inputs longer than that are billed as multiple units. Full current rates live on the<\/span><a href=\"https:\/\/aws.amazon.com\/bedrock\/pricing\/\" rel=\"noopener\"> <span style=\"font-weight: 400;\">Amazon Bedrock pricing page<\/span><\/a><span style=\"font-weight: 400;\">, which changes often enough that any figure in a blog post deserves verification before it goes into a budget.<\/span><\/p>\n<h3><b>Where Bedrock Bills Usually Go Wrong<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Four patterns account for most of the overruns we find when auditing a Bedrock account:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Agent token amplification:<\/b><span style=\"font-weight: 400;\"> An agent that reasons across several steps can consume five to ten times the tokens visible in the user-facing conversation.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Idle vector stores:<\/b><span style=\"font-weight: 400;\"> A knowledge base with a provisioned OpenSearch collection bills every hour whether anyone queries it or not.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Default model selection:<\/b><span style=\"font-weight: 400;\"> Routing simple extraction tasks to a frontier model when a small model would score identically.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Retries and experimentation:<\/b><span style=\"font-weight: 400;\"> Failed calls, prompt iterations, and evaluation runs all bills at full rate during development.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">Intelligent Prompt Routing addresses the third pattern directly by routing simple prompts to smaller models within the same family, which AWS says can cut costs by up to 30% without a measurable accuracy loss.<\/span><\/p>\n<p><i><span style=\"font-weight: 400;\">&gt;&gt;&gt; Read more:<\/span><\/i><a href=\"https:\/\/renovacloud.com\/en\/cudos-dashboard-setup-consulting\/\"> <i><span style=\"font-weight: 400;\">What Is CUDOS Dashboard Setup Consulting and When Does Your Business Need It?<\/span><\/i><\/a><\/p>\n<h2><b>Amazon Bedrock Compared With Direct APIs and Self-Hosting<\/b><\/h2>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-31120\" src=\"http:\/\/renovacloud.com\/wp-content\/uploads\/2026\/08\/image4.png\" alt=\"Analyst reviewing AI usage and token pricing on a dashboard.\u00a0\" width=\"1024\" height=\"765\" \/><\/p>\n<p><span style=\"font-weight: 400;\">There are three ways to get a foundation model into production, and the right one depends mostly on the rest of your stack.<\/span><\/p>\n<table style=\"height: 450px;\" width=\"1275\">\n<tbody>\n<tr>\n<td>\n<p style=\"text-align: center;\"><b>Factor<\/b><\/p>\n<\/td>\n<td style=\"text-align: center;\"><b>Amazon Bedrock<\/b><\/td>\n<td style=\"text-align: center;\"><b>Direct provider API<\/b><\/td>\n<td>\n<p style=\"text-align: center;\"><b>Self-hosted on AWS<\/b><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Time to first call<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Minutes<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Minutes<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Days to weeks<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Day-zero access to new models<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Usually a short lag<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Immediate<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Depends on weights release<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Billing<\/span><\/td>\n<td><span style=\"font-weight: 400;\">One AWS invoice<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Separate per vendor<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Compute hours<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Data boundary<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Inside your AWS account<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Vendor infrastructure<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Fully yours<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Identity and access control<\/span><\/td>\n<td><span style=\"font-weight: 400;\">IAM and KMS<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Vendor API keys<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Yours to build<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Economics at low volume<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Strong<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Strong<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Poor<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Economics at very high volume<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Good with reserved capacity<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Moderate<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Best above high utilisation<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3><b>When Bedrock Fits Best<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Bedrock is the natural choice when your stack already runs on AWS, when procurement or compliance requires a single vendor relationship, when you expect to compare several models against the same workload, or when data residency inside an AWS region is a contractual requirement.<\/span><\/p>\n<h3><b>When Another Path Fits Better<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">A direct provider API makes sense when you need a model the day it launches and the workload has no residency constraint. Self-hosting earns its keep when a single model runs at high, steady utilisation and your team already operates GPU infrastructure, since the ops overhead only amortises at scale.<\/span><\/p>\n<h2><b>How to Get Started With Amazon Bedrock<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">The first workload can be live in an afternoon. Production readiness takes longer, and the order of these steps matters more than it looks.<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Enable model access:<\/b><span style=\"font-weight: 400;\"> In the Bedrock console, request access to the specific models you plan to use. Access is granted per model, per region.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Choose a region:<\/b><span style=\"font-weight: 400;\"> Singapore, Tokyo, Jakarta, Sydney, and Mumbai are the closest options for Vietnamese workloads. Regional choice affects latency, price, and which models are available.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Test in the playground:<\/b><span style=\"font-weight: 400;\"> Run your real prompts against three or four candidate models before writing any code. Price differences of 50x are common between models that score similarly on a narrow task.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Build an evaluation set:<\/b><span style=\"font-weight: 400;\"> Twenty to fifty representative prompts with expected outputs. This becomes the thing you measure every future change against.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Attach a guardrail:<\/b><span style=\"font-weight: 400;\"> Configure content filters and sensitive information filters before the application touches real users.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Turn on invocation logging and cost allocation tags:<\/b><span style=\"font-weight: 400;\"> Logging to CloudWatch or S3 gives you the audit trail. Tags give you the per-team cost breakdown.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Move what you can to Batch or Flex:<\/b><span style=\"font-weight: 400;\"> Any job that tolerates delay belongs there from day one.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">The AWS<\/span><a href=\"https:\/\/docs.aws.amazon.com\/bedrock\/\" rel=\"noopener\"> <span style=\"font-weight: 400;\">Bedrock documentation<\/span><\/a><span style=\"font-weight: 400;\"> covers each step in detail, and the<\/span><a href=\"https:\/\/aws.amazon.com\/bedrock\/faqs\/\" rel=\"noopener\"> <span style=\"font-weight: 400;\">Bedrock FAQs<\/span><\/a><span style=\"font-weight: 400;\"> answer most of the quota and regional questions that come up in week one.<\/span><\/p>\n<p><i><span style=\"font-weight: 400;\">&gt;&gt;&gt; Read more:<\/span><\/i><a href=\"https:\/\/renovacloud.com\/en\/generative-ai-poc-on-aws\/\"> <i><span style=\"font-weight: 400;\">How to Build a Generative AI PoC on AWS with Renova Cloud<\/span><\/i><\/a><\/p>\n<h2><b>What Amazon Bedrock Means for Businesses in Vietnam<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">AWS research found that 18% of Vietnamese businesses had adopted AI, while 74% remained focused on basic applications. Most of that 74% is stuck in the gap between a working pilot and a production system. That gap is usually about architecture, governance, and who owns the monthly bill.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Bedrock helps because it puts the model inside infrastructure your team already understands. The IAM policies, VPC design, logging, and tagging conventions that govern your existing AWS workloads extend to your AI workloads without a parallel security review. For regulated industries in Vietnam, that continuity is often what gets a project through security review in weeks instead of quarters.<\/span><\/p>\n<p><i><span style=\"font-weight: 400;\">&gt;&gt;&gt; Read more:<\/span><\/i><a href=\"https:\/\/renovacloud.com\/en\/renova-cloud-accelerates-enterprise-ai-as-anthropic-authorized-reseller-in-vietnam\/\"> <i><span style=\"font-weight: 400;\">Renova Cloud Accelerates Enterprise AI as Anthropic Authorized Reseller in Vietnam<\/span><\/i><\/a><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-31118\" src=\"http:\/\/renovacloud.com\/wp-content\/uploads\/2026\/08\/image3.png\" alt=\"Vietnamese developers collaborating with AWS Partner Network.\u00a0\" width=\"1024\" height=\"765\" \/><\/p>\n<h2><b>Build on Amazon Bedrock With Renova Cloud<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Renova Cloud is an AWS Premier Tier Partner and three-time AWS Partner of the Year for Vietnam, with the AWS AI Services Competency and an authorised Anthropic reseller agreement covering Claude for enterprise. Our team has taken Bedrock workloads from proof of concept to production across finance, manufacturing, retail, and logistics, working from offices in Ho Chi Minh City.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We help with model selection against your own evaluation data, retrieval architecture that keeps vector storage costs sensible, Guardrails and governance design that satisfies your compliance team, and FinOps practices that keep the monthly bill predictable as usage grows. Our<\/span><a href=\"https:\/\/renovacloud.com\/en\/services\/generative-ai-on-aws\/\"> <span style=\"font-weight: 400;\">Generative AI on AWS<\/span><\/a><span style=\"font-weight: 400;\"> practice runs the whole path, from a two-week PoC through to a supported production platform.<\/span><\/p>\n<p><a href=\"https:\/\/renovacloud.com\/en\/contact\/\"><b>Talk to our AWS and AI specialists<\/b><\/a><span style=\"font-weight: 400;\"> about what Amazon Bedrock could do for your business.<\/span><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Amazon Bedrock is the AWS service that gives you API access to foundation models from Anthropic, Amazon, Meta, Mistral AI, OpenAI and a dozen other providers, all through a single endpoint inside your own AWS account. You pick a model, send a prompt, and pay per token. AWS handles the GPUs, the scaling, and the [&#8230;]\n","protected":false},"author":18,"featured_media":31117,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[962],"tags":[],"class_list":["post-31126","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-amazon-bedrock"],"_links":{"self":[{"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/posts\/31126","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/users\/18"}],"replies":[{"embeddable":true,"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/comments?post=31126"}],"version-history":[{"count":1,"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/posts\/31126\/revisions"}],"predecessor-version":[{"id":31127,"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/posts\/31126\/revisions\/31127"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/media\/31117"}],"wp:attachment":[{"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/media?parent=31126"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/categories?post=31126"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/tags?post=31126"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}