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The AWS Bedrock LLM node connects your workflows to Amazon Bedrock’s managed model catalog. Use it to access foundation models from providers like Anthropic Claude, Amazon Titan, and Meta Llama through your AWS account — enabling use cases such as generating investment research summaries, extracting data from regulatory filings, or building compliant AI workflows that keep data within your AWS environment.

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Core Functionality

  • Generate text completions using models available through AWS Bedrock
  • Authenticate with your own AWS credentials for enterprise-grade access control
  • Process system instructions and dynamic prompts with variable interpolation
  • Stream responses in real time for long-running generations
  • Return structured JSON output with optional schema enforcement
  • Track token usage and credit consumption per run
  • Apply content moderation, PII detection, and safety guardrails
  • Retry failed executions automatically with configurable intervals

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Tool Inputs

  • System Instructions — (String) Instructions that guide the model’s behavior, tone, and how it should use data provided in the prompt
  • Prompt — (String) The data sent to the model. Type {{ to open the variable builder and reference outputs from other nodes
  • Model * — (Enum (Dropdown), default: us.anthropic.claude-sonnet-4-5) Select from available Bedrock models
  • Use Personal AWS Credentials — (Boolean, default: No) Toggle to authenticate with your own AWS credentials
  • AWS Access Key ID — (String) Your AWS access key. Only visible when Use Personal AWS Credentials is enabled
  • AWS Secret Key — (String) Your AWS secret key. Only visible when Use Personal AWS Credentials is enabled
  • AWS Region — (Dropdown, default: US East N. Virginia) The AWS region for Bedrock API calls. Only visible when Use Personal AWS Credentials is enabled
  • JSON Schema — (String) JSON schema to enforce structured output format. Only visible when JSON Response is enabled in the settings panel
* indicates a required field

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Tool Outputs

  • response — (String (or Stream<String> when streaming)) The generated text response from the model
  • prompt_response — (String) The combined prompt and response content
  • tokens_used — (Integer) Total number of tokens consumed (input + output)
  • input_tokens — (Integer) Number of input tokens sent to the model
  • output_tokens — (Integer) Number of output tokens generated by the model
  • credits_used — (Decimal) VectorShift AI credits consumed for this run

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Overview

The Bedrock LLM node in workflows lets you place an AWS Bedrock model directly on the canvas, optionally authenticate with your own AWS credentials, and configure model behavior through the settings panel. This is ideal for organizations that require data to stay within their AWS environment or need access to Bedrock-exclusive models.

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Use Cases

  • Compliant document processing — Process sensitive financial documents using models hosted within your AWS account, ensuring data residency and compliance requirements are met.
  • Multi-model evaluation — Compare outputs from different foundation models (Claude, Titan, Llama) available on Bedrock to find the best fit for your financial analysis tasks.
  • Automated report generation — Generate quarterly portfolio reports by combining market data with client holdings, using enterprise-grade AWS infrastructure.
  • Regulatory filing extraction — Parse and extract structured data from SEC filings or tax documents using JSON mode, keeping all data within your AWS boundary.
  • Internal knowledge Q&A — Build internal chatbots that answer compliance questions by grounding responses in your organization’s policy knowledge base.

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How It Works

  1. Add the node to your workflow. From the toolbar, open the AI category and drag the AWS node onto the canvas. It appears as “Bedrock” on the node.
  2. Write your System Instructions. Enter instructions in the System Instructions field to define the model’s behavior, tone, and how it should use any data provided in the prompt.
  3. Configure the Prompt. In the Prompt field, type {{ to open the variable builder and reference outputs from upstream nodes.
  4. Select a model. Use the Model dropdown to choose from available Bedrock models, including us.anthropic.claude-sonnet-4-5 and other foundation models.
AWS Bedrock node showing the Model dropdown
  1. Enable streaming (optional). Click the Streaming toggle on the node face to receive responses token-by-token. When streaming is enabled, the response output type changes to Stream<String>.
  2. Use personal AWS credentials (optional). Toggle Use Personal AWS Credentials to enable authentication with your own AWS account. Three fields appear:
    • AWS Access Key ID — Your AWS access key
    • AWS Secret Key — Your AWS secret access key
    • AWS Region — Select your preferred AWS region (e.g., US East N. Virginia)
  3. Open settings. Click the gear icon (⚙) on the node to open the settings panel, where you can configure token limits, temperature, retry behavior, safety features, and more.
  4. Connect outputs. Click the Outputs button to open the outputs panel. Wire the response output to downstream nodes. Use tokens_used, input_tokens, output_tokens, and credits_used for monitoring.
AWS Bedrock node connected to upstream and downstream nodes
  1. Run your workflow. Execute the pipeline. The Bedrock node processes its inputs through AWS and returns the generated response along with usage metrics.

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Settings

All settings below are accessed via the gear icon (⚙) on the node.
SettingTypeDefaultDescription
ProviderDropdownBedrockThe LLM provider.
Max TokensInteger64000Maximum number of input + output tokens the model will process per run.
Reasoning EffortDropdownDefaultControls the depth of reasoning the model applies to its response.
VerbosityDropdownDefaultControls the verbosity of model responses.
TemperatureFloat0.5Controls response creativity. Higher values produce more diverse outputs; lower values produce more deterministic responses. Range: 0–1.
Top PFloat0.5Controls token sampling diversity. Higher values consider more tokens at each generation step. Range: 0–1.
StreamBooleanOffStream responses token-by-token instead of returning the full response at once.
JSON ResponseBooleanOffReturn output as structured JSON. When enabled, a JSON Schema input appears for optional schema enforcement.
Show SourcesBooleanOffDisplay source documents used for the response. Useful when combining with knowledge base inputs.
Toxic Input FiltrationBooleanOffFilter toxic input content. If the model receives toxic content, it responds with a respectful message instead.
Safe Context Token WindowBooleanOffAutomatically reduce context to fit within the model’s maximum context window.
Retry On FailureBooleanOffEnable automatic retries when execution fails.
Max # of re-tryInteger—Maximum number of retry attempts. Visible when Retry On Failure is enabled.
Max Interval b/w re-tryInteger—Interval in milliseconds between retry attempts.
PII Detection
NameBooleanOffDetect and redact personal names from input before sending to the model.
EmailBooleanOffDetect and redact email addresses from input.
PhoneBooleanOffDetect and redact phone numbers from input.
SSNBooleanOffDetect and redact Social Security numbers from input.
Credit Card InfoBooleanOffDetect and redact credit card numbers from input.
Show Guardrail StatusDropdown—Controls whether guardrail status is included in the output.

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Best Practices

  • Use personal AWS credentials for production workloads. This ensures data stays within your AWS account and lets you leverage AWS IAM policies for fine-grained access control.
  • Select the appropriate region. Choose an AWS region close to your users or one that meets your data residency requirements for compliance-sensitive financial workflows.
  • Use JSON mode for structured extraction. When pulling financial metrics from documents, enable JSON Response and provide a schema for consistent, machine-readable output.
  • Enable Safe Context Token Window for variable-length inputs. Prevents token-limit errors when processing documents of unpredictable size like earnings transcripts or multi-page filings.
  • Monitor token usage for cost management. Bedrock charges per token — connect tokens_used and credits_used to tracking nodes to monitor spend across high-volume processing.
  • Apply PII detection for regulated data. When handling client financial data subject to regulatory requirements, enable PII toggles including SSN detection.

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Common Issues

For troubleshooting common issues with this node, see the Common Issues documentation.