Published on Jun 22, 2026
Amazon Bedrock is AWS's answer to a real problem: generative AI is powerful, but building with it from scratch is hard. You need GPUs, model pipelines, security layers, and months of setup before you can ship anything. Bedrock removes all of that. It's a fully managed service that gives you direct API access to top foundation models from Anthropic, Meta, Mistral, and Amazon. No servers to provision, no infrastructure to manage.
Whether you're a startup evaluating AI tools or a technical lead planning your next product, this guide explains exactly what AWS Bedrock is, how it works, what it costs, and when it makes sense to use it.
AWS Bedrock is a fully managed generative AI service that lets you build, customize, and deploy AI applications using pre-trained foundation models (FMs) through a single API. You don't manage infrastructure, train models, or handle GPUs. Amazon handles all of that behind the scenes. You can read the official service overview on the Amazon Bedrock product page .
Think of it as a unified platform where you choose the model that fits your use case, connect it to your data if needed, and call it via API. The models themselves come from multiple leading AI labs, not just Amazon.
As of 2026, Amazon Bedrock serves over 100,000 organizations worldwide, from early-stage startups to global enterprises across healthcare, finance, e-commerce, and media.
The service works in three layers.
First, you pick a foundation model. Bedrock hosts models from providers like Anthropic (Claude), Meta (Llama), Mistral, Stability AI, and Amazon's own Titan family. You can compare them in the console or test them via API before committing. OpenAI models are now also generally available on Bedrock, which makes it the broadest model marketplace on any cloud platform.
Second, you customize if needed. You can connect the model to your own data using a knowledge base (this is called retrieval-augmented generation, or RAG), or fine-tune the model with your proprietary datasets. This is how you move from a generic model that answers any question to one that understands your product, your customers, and your company's terminology.
Third, you call the API. Once your setup is ready, your application sends prompts and receives completions, summaries, embeddings, or images. The exact output type depends on which model you're using. Bedrock handles scaling automatically.
Most AI platforms lock you into one model family. Bedrock gives you a choice. The current lineup includes Claude 3 and Claude 3.5 from Anthropic, Llama 3 from Meta, Mistral models, Stability AI for image generation, Cohere for search and embeddings, and Amazon Titan for text and multimodal tasks.
This matters in practice. A customer support use case might fit Claude well. A document search use case might benefit from Cohere embeddings. You can run both through the same Bedrock API without managing separate vendor relationships or changing your infrastructure.
RAG is the technique that makes Bedrock genuinely useful for business applications. Instead of relying solely on what the model was trained on, you give it access to your own documents, databases, or product content at query time.
You upload your data, Bedrock indexes it into a vector store, and the model retrieves relevant context before generating a response. This reduces hallucinations, keeps answers factual, and lets you build AI assistants that actually know your business.
For teams that need more than RAG can offer, Bedrock supports full model fine-tuning and continued pre-training with proprietary data. You supply labeled examples or domain-specific text, and Bedrock trains a customized version of the model that stays within your AWS environment.
Fine-tuning 100 million tokens costs roughly $200. Storing the resulting model runs around $5 per month. For most companies, this is the point where a generic model becomes a genuine competitive asset.
Bedrock Agents lets you build AI systems that don't just answer questions: they take actions. An agent can query your database, call an external API, check inventory, and return a synthesized result, all in a single flow without human involvement at each step.
Amazon introduced Bedrock AgentCore in 2025 as the dedicated platform for building and deploying production agents at scale. It handles memory, tooling, security, and orchestration in one place.
One of the more practical features for businesses: Guardrails lets you define what the model should and shouldn't do. You can block harmful content, restrict topics, filter specific phrases, and set content policies tailored to your use case. According to AWS, Guardrails blocks up to 88% of harmful content and achieves up to 99% accuracy in detecting correct model responses.
For regulated industries, this makes compliance measurably easier.
Bedrock inherits AWS's full security posture. Data is encrypted in transit and at rest. You control access through IAM policies. Models can run inside a VPC. Critically, Bedrock never uses your data to train its base models. Your inputs and outputs stay yours.
AWS holds over 140 security and compliance certifications, which matters when you're building for healthcare, finance, or any sector with strict data governance requirements.
Bedrock uses pay-as-you-go pricing based on the number of tokens processed (for text models) or images generated (for image models). There are three pricing modes.
On-demand pricing charges per API call. You pay for what you use and can start experimenting in minutes without any commitment. As a reference point: processing 10,000 input tokens and 100,000 output tokens per day with Claude Instant costs roughly $7–8 per month.
Provisioned throughput reserves a fixed processing capacity for a specific model. You pay per hour regardless of actual usage, but the per-token rate is lower, and performance is more predictable for high-volume production workloads.
Batch processing is for large, non-time-sensitive jobs. Batch processing typically comes with a 50% discount over on-demand rates and is suited for bulk document processing, embeddings generation, or large dataset analysis.
Additional costs to budget for: data transfer in and out of AWS, storage for knowledge base embeddings, and storage for any fine-tuned models.
Yes. Amazon Bedrock includes a free tier for new AWS accounts, and it covers a meaningful amount of usage across several models.
As of 2026, the free tier includes:
The free tier resets monthly and applies within the first 90 days of activation for most model families. It's enough to build a working prototype, test RAG with your own data, and validate whether a given model fits your use case before you spend anything.
💡 Pro tip: Use the free tier period to run the same prompt against three or four models in the Bedrock playground. Claude tends to win on nuanced language tasks. Titan Lite is faster and cheaper for simpler classification or summarization. Llama performs well if you need a model with more permissive licensing for commercial use. Getting this comparison done before you start building saves a painful model switch later.
For current pricing by model, check the AWS Bedrock pricing page .
This is the most common question from teams already using AWS.
SageMaker is a full machine learning platform. It's built for data scientists who want to train custom models, build ML pipelines, manage notebooks, and run experiments. It requires ML expertise and infrastructure management.
Bedrock is for application developers who want to use AI without managing ML infrastructure. You start from powerful pre-trained models and customize as needed. No training from scratch required.
The two services complement each other. Many teams use both: Bedrock for application-layer AI features, SageMaker for custom model training and MLOps.
The most common use case. Connect a Claude or Titan model to your product knowledge base via RAG, and you have a support assistant that understands your documentation, answers in your brand's voice, and escalates edge cases to a human.
Legal teams use Bedrock to summarize contracts. Marketing teams use it to draft campaign copy. News organizations use it to create article summaries. The pattern is the same: pass in structured or unstructured text, get a useful output without manual effort.
Claude and Llama models perform well on code generation tasks. Teams are using Bedrock to build internal developer tools, automate code review, and generate test cases for existing codebases.
For industries dealing with high volumes of documents, insurance claims, medical records, legal filings, Bedrock can classify, extract, and summarize information faster and cheaper than manual review.
Using Bedrock Agents, you can automate multi-step processes that previously required human coordination. An agent might receive a customer inquiry, check the order status in your database, pull account history, and return a resolution. All without a support rep involved.
For a detailed example of building an AI product on Bedrock, see our guide: AWS Bedrock: The Smart Way for Startups to Build Scalable, Secure AI Solutions .
Bedrock is a good fit for teams that match at least one of these descriptions.
According to the McKinsey's State of AI 2025 report , 75% of organizations now use AI in at least one business function, but most still struggle to scale it effectively. The bottleneck is rarely model quality. It's infrastructure complexity. Bedrock addresses that directly.
You want to ship AI features fast. Bedrock gets you from zero to a working prototype in hours, not weeks. You skip the infrastructure setup entirely.
You're already on AWS. Integration with S3, Lambda, CloudWatch, IAM, and other AWS services is native. If your stack is already in AWS, Bedrock is the path of least resistance for adding AI.
You need enterprise security. If data governance, compliance, or privacy are non-negotiable for your industry, Bedrock solves a lot of problems before they become problems: no data retention for training, VPC isolation, full encryption.
You want model flexibility. Bedrock's multi-model approach means you're not betting your architecture on one AI provider. As the model landscape evolves, you can switch or combine models without rewriting your application layer.
You don't have ML engineers in-house. Bedrock is built for product and application developers. You don't need to understand gradient descent to build a useful AI feature with it.
Bedrock is not a replacement for everything ML-related.
If you need to train a fully custom model on proprietary data from scratch, you're looking at SageMaker or a dedicated ML infrastructure. Bedrock fine-tuning is powerful but works within the boundaries of existing foundation models.
If your use case requires real-time inference at very high throughput with tight latency SLAs, provisioned throughput on Bedrock can help, but for truly specialized, performance-critical AI workloads, a more custom architecture may be warranted.
And if your data cannot leave your on-premises environment under any circumstances, Bedrock's fully managed cloud approach may not meet your compliance requirements without additional controls.
Getting up and running takes four steps.
AWS also offers a free tier for Bedrock that lets you process a limited number of tokens per month at no cost. That's enough to build and test a prototype before committing to production spending.
Perfsys is a certified AWS Select Tier Services Partner that helps startups and growing companies build production-ready AI applications on AWS. We've used Bedrock to help clients ship AI MVPs in weeks, including a mental health support assistant that reduced infrastructure costs by 90% compared to the client's original architecture estimate.
If you're evaluating whether Bedrock fits your use case, or you want a technical team to handle the build, our MVP development services and AWS consulting team are the right starting point.
Our team has shipped Bedrock-based applications for startups across healthcare, e-commerce, and SaaS. We can scope your project and tell you what's realistic in your timeframe and budget.
Eugene Orlovsky
CEO & Founder | Serverless architect with 10+ years of hands-on experience designing cloud-native architectures on AWS, backed by multiple AWS certifications. He is writing bridges deep technical expertise with real-world business strategy, covering topics from AWS best practices to scaling tech-driven organizations.
~10x
faster incident response
100K+
Lambda invocations tracked per month
30
customer deployments under unified observability
Perfsys built automated serverless observability for Nogalis — a single-tenant SaaS running 30 isolated AWS accounts — delivering 10x faster incident response and 100K+ Lambda invocations tracked monthly, with zero manual setup per new customer.
85%
Reduction in form creation time
3×
Faster grant application completion
3
months full platform delivery
Perfsys built four AI services for EPIRA.ai on AWS Bedrock, automating grant form creation (80–90% faster) and pre-application eligibility scoring. Delivered in 3 months by 2 engineers with a fully serverless, scalable architecture.
Need to move fast? Our cloud team is ready to scale, secure, and optimize your systems. Get serverless expertise, 24/7 support, and seamless CI/CD pipelines when you need it most.
