{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"aws-bedrock","slug":"aws-bedrock","name":"AWS Bedrock","type":"platform","url":"https://aws.amazon.com/bedrock","page_url":"https://unfragile.ai/aws-bedrock","categories":["llm-apis"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"aws-bedrock__cap_0","uri":"capability://text.generation.language.multi.provider.foundation.model.access.via.unified.api","name":"multi-provider foundation model access via unified api","description":"Bedrock abstracts multiple foundation model providers (Anthropic Claude, Meta Llama, Mistral, Cohere, Stability AI, Amazon Titan) behind a single AWS API endpoint and authentication layer. Requests route to the selected model through AWS's managed infrastructure, eliminating the need to manage separate API keys, endpoints, or SDKs for each provider. Model selection happens at request time via the modelId parameter, enabling dynamic provider switching without code changes.","intents":["I want to compare outputs from multiple LLM providers without managing separate API integrations","I need to switch between Claude and Llama based on cost or latency without refactoring my application","I want a single IAM-based authentication mechanism for all my AI model access"],"best_for":["enterprises standardizing on AWS infrastructure","teams evaluating multiple model providers for production workloads","builders seeking vendor lock-in reduction through abstraction"],"limitations":["Model availability varies by AWS region; not all models available in all regions","API surface area is lowest-common-denominator across providers; advanced provider-specific features may not be exposed","Latency includes AWS routing overhead vs direct provider API calls"],"requires":["AWS account with Bedrock service enabled in target region","IAM credentials with bedrock:InvokeModel permission","boto3 (Python 3.9+) or AWS SDK for JavaScript/Java/Go/Rust"],"input_types":["text prompts","structured JSON for multi-turn conversations","image data (base64-encoded for vision models)"],"output_types":["text completions","structured JSON responses","streaming token sequences"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-bedrock__cap_1","uri":"capability://memory.knowledge.knowledge.base.backed.retrieval.augmented.generation.rag","name":"knowledge base-backed retrieval-augmented generation (rag)","description":"Bedrock Knowledge Bases enable document ingestion, chunking, and vector embedding into AWS-managed vector stores (using Amazon OpenSearch or native Bedrock vector storage). When a user query arrives, Bedrock automatically retrieves semantically relevant document chunks and injects them into the LLM context window before generation. This pattern reduces hallucination by grounding responses in indexed proprietary data without requiring manual RAG pipeline orchestration.","intents":["I want to build a chatbot that answers questions about my company's internal documentation without fine-tuning","I need to ingest PDFs, web pages, and structured data into a searchable knowledge base automatically","I want retrieval and generation to happen in a single API call without managing embedding models separately"],"best_for":["enterprises with large document repositories (compliance, product docs, internal wikis)","teams building customer support chatbots grounded in knowledge bases","non-ML teams wanting RAG without infrastructure complexity"],"limitations":["Chunking strategy is fixed; no fine-grained control over chunk size, overlap, or splitting logic","Embedding model is AWS-managed; cannot use custom or specialized domain embeddings","Retrieval happens synchronously; latency scales with knowledge base size and query complexity","No built-in reranking or semantic filtering beyond vector similarity"],"requires":["AWS account with Bedrock Knowledge Bases enabled","S3 bucket for document storage or direct API upload","IAM permissions for bedrock:CreateKnowledgeBase and bedrock:RetrieveAndGenerate","Documents in supported formats: PDF, TXT, DOCX, PPTX, HTML, JSON, CSV"],"input_types":["documents (PDF, DOCX, TXT, HTML, JSON, CSV)","text queries","structured metadata for filtering"],"output_types":["generated text with source citations","retrieved document chunks with relevance scores","structured JSON with generation and retrieval metadata"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-bedrock__cap_10","uri":"capability://safety.moderation.vpc.and.private.endpoint.access.for.data.isolation","name":"vpc and private endpoint access for data isolation","description":"Bedrock supports AWS PrivateLink VPC endpoints, enabling organizations to invoke models without routing traffic through the public internet. Requests stay within the AWS network, meeting data residency and network isolation requirements. This capability is critical for enterprises handling sensitive data or operating in restricted network environments.","intents":["I need to invoke Bedrock models from my VPC without exposing traffic to the internet","I want to ensure all AI inference stays within my organization's private network","I need to meet data residency requirements that prohibit internet-routed traffic"],"best_for":["enterprises with strict network security requirements","organizations handling sensitive data (PII, financial, healthcare)","teams operating in restricted network environments (air-gapped networks)"],"limitations":["VPC endpoint setup adds operational complexity","Private endpoint access may introduce additional latency vs public endpoints","Requires VPC configuration and security group management"],"requires":["AWS account with VPC configured","VPC endpoint for Bedrock service","Security group rules allowing traffic to the endpoint","IAM permissions for VPC endpoint creation and management"],"input_types":["Bedrock API requests from within VPC"],"output_types":["model responses routed through private network"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-bedrock__cap_11","uri":"capability://automation.workflow.cross.region.model.availability.and.failover","name":"cross-region model availability and failover","description":"Bedrock models are available across multiple AWS regions, enabling applications to invoke models from geographically distributed regions for latency optimization and disaster recovery. Applications can implement failover logic to switch regions if primary region becomes unavailable. Model IDs and APIs are consistent across regions, simplifying multi-region deployments.","intents":["I want to reduce latency by invoking models from the region closest to my users","I need to implement disaster recovery with automatic failover to another region","I want to distribute inference load across multiple regions for resilience"],"best_for":["global applications requiring low-latency inference","enterprises with disaster recovery requirements","teams optimizing for geographic distribution"],"limitations":["Model availability varies by region; not all models available in all regions","Cross-region failover requires client-side logic; no automatic failover","Cross-region traffic may incur data transfer costs","Consistency of model versions across regions is not guaranteed"],"requires":["AWS account with Bedrock enabled in multiple regions","Client-side failover logic or load balancing","IAM credentials with permissions in all target regions"],"input_types":["Bedrock API requests"],"output_types":["model responses from selected region"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-bedrock__cap_12","uri":"capability://data.processing.analysis.cost.monitoring.and.optimization.via.aws.cost.explorer","name":"cost monitoring and optimization via aws cost explorer","description":"Bedrock integrates with AWS Cost Explorer, enabling detailed cost tracking by model, region, and time period. Organizations can set up cost alerts, analyze spending trends, and identify optimization opportunities (e.g., switching to cheaper models or using batch inference). Cost data is granular and updated daily, supporting informed cost management decisions.","intents":["I want to track how much I'm spending on each AI model","I need to identify which applications are driving the highest AI costs","I want to set up alerts if my AI spending exceeds a budget threshold"],"best_for":["cost-conscious teams optimizing AI spending","enterprises with chargeback models for AI usage","organizations managing multiple AI applications with shared budgets"],"limitations":["Cost data is updated daily; real-time cost tracking is not available","Cost Explorer requires manual analysis; no automated cost optimization recommendations","Granularity is limited to model and region; cannot track costs by prompt or user"],"requires":["AWS account with Cost Explorer enabled","Bedrock usage in the account","IAM permissions for Cost Explorer access"],"input_types":["Bedrock usage data"],"output_types":["cost reports by model and region","spending trends and forecasts","cost alerts and notifications"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-bedrock__cap_2","uri":"capability://planning.reasoning.agentic.task.decomposition.and.tool.orchestration","name":"agentic task decomposition and tool orchestration","description":"Bedrock Agents enable autonomous task execution by decomposing user requests into sub-tasks, invoking external tools (APIs, Lambda functions, databases), and iterating until completion. The agent uses chain-of-thought reasoning to decide which tools to call, in what order, and how to interpret results. Tool definitions are registered via JSON schemas, and Bedrock handles prompt engineering, error recovery, and state management across multi-step workflows.","intents":["I want to build an AI assistant that can autonomously book meetings, check calendars, and send confirmations","I need an agent that can query my database, transform data, and generate reports without hardcoding workflows","I want to delegate complex multi-step tasks to an LLM without manually orchestrating each step"],"best_for":["teams building autonomous AI assistants for business processes","enterprises automating multi-step workflows that require external tool integration","builders prototyping agent-based applications without building custom orchestration frameworks"],"limitations":["Agent reasoning is non-deterministic; same input may produce different tool sequences across runs","No built-in long-term memory; agents cannot learn from past interactions or maintain state across sessions","Tool invocation latency compounds with each step; multi-step agents may exceed acceptable response times","Error handling relies on LLM's ability to interpret tool failures; complex error scenarios may require manual intervention","No native support for parallel tool execution; tools are invoked sequentially"],"requires":["AWS account with Bedrock Agents enabled","Tool definitions in JSON schema format (OpenAPI 3.0 compatible)","Lambda functions, HTTP endpoints, or AWS service integrations for tool implementations","IAM permissions for bedrock:InvokeAgent and access to underlying tool services"],"input_types":["natural language task descriptions","structured JSON with task parameters","multi-turn conversation history"],"output_types":["task completion status with results","tool invocation logs and reasoning traces","structured JSON with final output and intermediate steps"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-bedrock__cap_3","uri":"capability://data.processing.analysis.model.evaluation.and.comparative.benchmarking","name":"model evaluation and comparative benchmarking","description":"Bedrock Model Evaluation enables side-by-side testing of multiple models against the same test dataset with configurable evaluation metrics (accuracy, latency, cost, safety scores). Evaluations run in batch mode, generating comparative reports that quantify performance differences across models. This capability helps teams select the optimal model for their use case based on empirical data rather than marketing claims.","intents":["I want to benchmark Claude vs Llama vs Mistral on my specific task before committing to production","I need to measure latency and cost trade-offs between different models for my workload","I want to validate that a cheaper model meets my quality requirements before switching"],"best_for":["teams evaluating models for production deployment","cost-conscious builders optimizing model selection for budget constraints","enterprises requiring empirical validation before vendor selection"],"limitations":["Evaluation metrics are predefined; custom evaluation logic requires external implementation","Batch evaluation introduces latency; results are not real-time","Evaluation costs are separate from inference costs; large test datasets can be expensive","No built-in A/B testing for production traffic; evaluation is offline only"],"requires":["AWS account with Bedrock Model Evaluation enabled","Test dataset in CSV or JSON format with prompts and expected outputs","IAM permissions for bedrock:EvaluateModel","Sufficient AWS credits for batch inference across multiple models"],"input_types":["test datasets (CSV, JSON)","prompt templates","expected outputs or reference answers"],"output_types":["comparative performance reports","per-model metrics (accuracy, latency, cost)","visualizations and rankings"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-bedrock__cap_4","uri":"capability://safety.moderation.guardrails.based.content.filtering.and.safety.enforcement","name":"guardrails-based content filtering and safety enforcement","description":"Bedrock Guardrails apply configurable safety policies to both user inputs and model outputs, filtering harmful content, enforcing topic restrictions, and detecting jailbreak attempts. Policies are defined declaratively (e.g., 'block requests about illegal activities', 'redact PII in outputs'), and Bedrock evaluates all requests against these rules before and after generation. Failed requests return structured rejection reasons, enabling applications to provide user-friendly error messages.","intents":["I want to prevent users from asking my chatbot to help with illegal activities","I need to ensure my AI assistant never outputs personally identifiable information","I want to restrict my agent to answering only questions about my product, not politics or religion"],"best_for":["enterprises deploying AI in regulated industries (healthcare, finance, legal)","teams building customer-facing AI products requiring safety compliance","organizations needing audit trails of safety violations for compliance reporting"],"limitations":["Guardrails are rule-based; sophisticated jailbreaks may bypass filters","PII redaction relies on pattern matching; context-dependent PII may not be detected","Custom guardrail logic is limited; complex safety policies require external validation","False positive rate may be high for strict topic restrictions, blocking legitimate requests"],"requires":["AWS account with Bedrock Guardrails enabled","IAM permissions for bedrock:ApplyGuardrail","Guardrail policy definitions in AWS Bedrock format"],"input_types":["user prompts","model outputs","structured metadata for context"],"output_types":["pass/fail verdict","rejection reason and category","redacted content (if applicable)","structured JSON with safety metadata"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-bedrock__cap_5","uri":"capability://code.generation.editing.custom.model.fine.tuning.with.managed.infrastructure","name":"custom model fine-tuning with managed infrastructure","description":"Bedrock Fine-Tuning enables training custom model variants on proprietary datasets without managing GPUs or training infrastructure. Users upload training data (text pairs for instruction-following or domain-specific examples), specify hyperparameters, and Bedrock handles data preprocessing, distributed training, and model checkpointing. Fine-tuned models are deployed as custom model IDs and invoked through the same unified API as base models.","intents":["I want to adapt Claude to my company's writing style and domain terminology without building a training pipeline","I need to fine-tune a model on proprietary data while keeping that data within my AWS account","I want to create a specialized model for my use case without managing CUDA, distributed training, or model serving"],"best_for":["enterprises with proprietary datasets and domain-specific requirements","teams seeking to reduce inference costs through smaller, specialized models","organizations requiring data residency compliance (fine-tuning stays within AWS)"],"limitations":["Fine-tuning is expensive; costs scale with dataset size and training duration","Training time is non-trivial; fine-tuning jobs may take hours to days","Limited control over training hyperparameters; Bedrock abstracts low-level tuning","Fine-tuned models inherit base model limitations; cannot fundamentally change model capabilities","No built-in evaluation of fine-tuned models; requires external validation"],"requires":["AWS account with Bedrock Fine-Tuning enabled","Training dataset in JSONL format (minimum 100 examples, recommended 1000+)","IAM permissions for bedrock:CreateModelCustomizationJob","S3 bucket for training data and output model artifacts"],"input_types":["training data (JSONL with prompt-completion pairs)","validation data (optional, for early stopping)","hyperparameter configuration"],"output_types":["fine-tuned model artifact","training metrics and loss curves","custom model ID for inference"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-bedrock__cap_6","uri":"capability://text.generation.language.streaming.token.by.token.response.generation","name":"streaming token-by-token response generation","description":"Bedrock supports streaming inference where model outputs are returned as a sequence of tokens in real-time, enabling low-latency user experiences for chat applications. Clients receive tokens as they are generated rather than waiting for the full response, reducing perceived latency and enabling progressive UI updates. Streaming is available for all text generation models and integrates with Bedrock's unified API.","intents":["I want my chatbot to display responses token-by-token for a more interactive experience","I need to reduce perceived latency by streaming long-form outputs to the user immediately","I want to cancel long-running generations if the user stops waiting"],"best_for":["teams building interactive chat interfaces","applications requiring real-time response feedback","builders optimizing for user experience in latency-sensitive scenarios"],"limitations":["Streaming adds complexity to client-side code; requires handling partial responses and stream termination","Token-level streaming may increase total request overhead vs batch responses","Error handling is more complex; errors may occur mid-stream after partial output","Streaming is not compatible with some advanced features (e.g., structured output validation)"],"requires":["AWS SDK with streaming support (boto3 3.x+, JavaScript SDK v3+)","HTTP/2 or WebSocket support for efficient streaming","Client-side code to handle streaming responses and token buffering"],"input_types":["text prompts","multi-turn conversation history"],"output_types":["token stream (EventStream format)","partial text chunks","metadata events (stop reason, token count)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-bedrock__cap_7","uri":"capability://data.processing.analysis.batch.inference.for.cost.optimized.bulk.processing","name":"batch inference for cost-optimized bulk processing","description":"Bedrock Batch API enables submitting large numbers of inference requests asynchronously with lower per-token costs than real-time inference. Requests are queued, processed during off-peak hours, and results are written to S3. This capability is optimized for non-latency-sensitive workloads like content generation, data labeling, or report generation where cost matters more than speed.","intents":["I want to generate summaries for 100,000 documents at the lowest possible cost","I need to process a large dataset through my model without paying premium real-time inference rates","I want to schedule bulk inference jobs to run during off-peak hours"],"best_for":["teams processing large datasets offline","cost-sensitive applications where latency is not critical","enterprises running nightly batch jobs for content generation or data processing"],"limitations":["Batch processing introduces latency; results may take hours to days","No real-time feedback; cannot monitor individual request progress","Batch API has different error handling; failed requests are logged but not retried automatically","Minimum batch size may apply; very small batches may not be cost-effective"],"requires":["AWS account with Bedrock Batch API enabled","S3 bucket for input and output data","Input data in JSONL format with request specifications","IAM permissions for bedrock:SubmitBatchJob and S3 access"],"input_types":["JSONL file with batch requests","S3 URI pointing to input data"],"output_types":["JSONL file with results written to S3","job status and completion metadata","error logs for failed requests"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-bedrock__cap_8","uri":"capability://text.generation.language.prompt.engineering.and.optimization.guidance","name":"prompt engineering and optimization guidance","description":"Bedrock provides built-in prompt engineering recommendations and best practices for each model, helping developers optimize prompts for quality and cost. The service includes prompt templates, examples, and guidance on structuring inputs for different tasks (summarization, classification, generation). This reduces trial-and-error in prompt development and accelerates time-to-production.","intents":["I want guidance on how to structure prompts for my specific task","I need to understand best practices for each model to get better outputs","I want to optimize my prompts to reduce token usage and costs"],"best_for":["teams new to LLM development seeking best practices","developers optimizing prompts for production quality","non-ML teams building AI applications without deep prompt engineering expertise"],"limitations":["Guidance is generic; domain-specific prompt optimization requires experimentation","No automated prompt optimization; recommendations are advisory only","Prompt quality still depends on user implementation; guidance does not guarantee results"],"requires":["AWS account with Bedrock access","Familiarity with prompt engineering concepts"],"input_types":["task descriptions","example prompts"],"output_types":["prompt templates","best practice recommendations","optimization suggestions"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-bedrock__cap_9","uri":"capability://safety.moderation.enterprise.compliance.and.audit.logging.via.cloudtrail","name":"enterprise compliance and audit logging via cloudtrail","description":"Bedrock integrates with AWS CloudTrail to log all API calls, model invocations, and configuration changes for compliance and audit purposes. Logs include request metadata, model selection, user identity, and timestamps, enabling organizations to track AI usage, detect anomalies, and demonstrate compliance with regulatory requirements. Logs are immutable and centralized in CloudTrail.","intents":["I need to audit all AI model usage for compliance with HIPAA or SOC 2","I want to track which users are invoking which models for cost allocation","I need to detect unauthorized or suspicious AI usage patterns"],"best_for":["enterprises in regulated industries (healthcare, finance, legal)","organizations with strict compliance and audit requirements","teams implementing AI governance and usage monitoring"],"limitations":["CloudTrail logs do not include prompt or response content; only metadata is logged","Log analysis requires external tools; CloudTrail does not provide built-in anomaly detection","CloudTrail has retention limits; long-term archival requires additional configuration"],"requires":["AWS account with CloudTrail enabled","S3 bucket for CloudTrail log storage","IAM permissions for CloudTrail configuration"],"input_types":["Bedrock API calls"],"output_types":["CloudTrail logs in JSON format","audit trails with timestamps and user identity","configuration change history"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aws-bedrock__headline","uri":"capability://tool.use.integration.managed.service.for.foundation.models","name":"managed service for foundation models","description":"AWS Bedrock is a managed service that provides access to a variety of foundation models through a unified API, enabling enterprises to develop AI applications with ease and security.","intents":["best managed service for foundation models","foundation models for enterprise AI applications","top APIs for accessing AI models","foundation model platforms for developers","managed AI model services comparison"],"best_for":["enterprise AI development","accessing multiple foundation models"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["AWS account with Bedrock service enabled in target region","IAM credentials with bedrock:InvokeModel permission","boto3 (Python 3.9+) or AWS SDK for JavaScript/Java/Go/Rust","AWS account with Bedrock Knowledge Bases enabled","S3 bucket for document storage or direct API upload","IAM permissions for bedrock:CreateKnowledgeBase and bedrock:RetrieveAndGenerate","Documents in supported formats: PDF, TXT, DOCX, PPTX, HTML, JSON, CSV","AWS account with VPC configured","VPC endpoint for Bedrock service","Security group rules allowing traffic to the endpoint"],"failure_modes":["Model availability varies by AWS region; not all models available in all regions","API surface area is lowest-common-denominator across providers; advanced provider-specific features may not be exposed","Latency includes AWS routing overhead vs direct provider API calls","Chunking strategy is fixed; no fine-grained control over chunk size, overlap, or splitting logic","Embedding model is AWS-managed; cannot use custom or specialized domain embeddings","Retrieval happens synchronously; latency scales with knowledge base size and query complexity","No built-in reranking or semantic filtering beyond vector similarity","VPC endpoint setup adds operational complexity","Private endpoint access may introduce additional latency vs public endpoints","Requires VPC configuration and security group management","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.013Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=aws-bedrock","compare_url":"https://unfragile.ai/compare?artifact=aws-bedrock"}},"signature":"RSeKM1Erd2ePBYUnuFR1ocf6GwhdH1YocSNSW8SgFJCT9Lq+W47va9k85G8W9zsbE5WwyUiQEyBlgBbEWFTqDg==","signedAt":"2026-06-21T00:03:02.301Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/aws-bedrock","artifact":"https://unfragile.ai/aws-bedrock","verify":"https://unfragile.ai/api/v1/verify?slug=aws-bedrock","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}