Amazon Bedrock
$100 for new accounts; it does not recur.
https://bedrock-mantle.{region}.api.aws/openai/v1Models mentioned
The offer covers a dynamic catalog, provider-selected route, or model class without stable model IDs in this snapshot.
Equivalent paid value
How this was valued: The base $100 AWS signup credit is spread over the six-month Free Plan. The additional earnable $100 is conditional and excluded, so this is a conservative minimum.
Limits and terms
- Signup Credit USD
- 100
- Additional Earnable Credit USD
- 100
- Free Plan Duration Months
- 6
- Credit Expiry Months From Account Creation
- 12
- Recurrence
- No
What happens to your prompts?
Bedrock is zero-retention and no-training by default, but named model routes have current abuse-review retention exceptions.
- Plan Scope
- Amazon Bedrock model inference; model-specific abuse rules and customer-enabled stateful features can differ from the default.
- Prompt Retention
- Bedrock defaults to zero data retention and does not store model inputs or outputs. Current exceptions include up to 30-day retention for classifier-flagged OpenAI GPT-5.4/5.5/5.6 traffic and all Anthropic Claude Fable 5 traffic, plus legally required CSAM handling.
- Response Retention
- Same default and model-specific exceptions as inputs. Customer-created agents, knowledge bases, invocation logging, batch files, and other AWS storage persist under their configured lifecycles.
- Ordinary Logging
- Bedrock retains metering and operational metadata, and customers can deliberately enable model-invocation logging to CloudWatch or S3. Content is automatically screened for abuse.
- Model Training
- AWS says it does not use Bedrock inputs or outputs to train or improve base models and does not share them with third-party model providers for that purpose.
- Product Improvement
- Customer content is excluded from base-model improvement; aggregate service telemetry and customer-provided feedback are separately governed.
- Human Or Operator Access
- Bedrock uses a zero-operator-access model by default. Stored abuse exceptions can be reviewed only for the specified safety purpose; customer support or customer-configured logs create separate access paths.
- Subprocessors And Routing
- AWS hosts access to Amazon and third-party foundation models without sharing prompts with the original model providers, but model licenses and use policies still apply.
- Deletion Controls
- Ordinary stateless payloads are not stored. Customers control CloudWatch, S3, agents, knowledge bases, batch inputs, and other feature state through AWS retention and deletion settings.
- Caveat
- Model-specific safety exceptions now prevent treating every Bedrock route as strict ZDR. Inspect the selected model and any enabled logging or stateful feature.
Governing documents
- Customer Agreement
- https://aws.amazon.com/agreement/
- Service Terms
- https://aws.amazon.com/service-terms/
- Privacy Notice
- https://aws.amazon.com/privacy/
- Data Processing Addendum
- https://d1.awsstatic.com/legal/aws-gdpr/AWS_GDPR_DPA.pdf
Eligibility
- New AWS Customer Only
- Yes
- Payment Method Required
- Yes
Primary sources
Before you build with Amazon Bedrock
Read the classification narrowly
This record has current first-party evidence for some zero-cost hosted inference. The label describes the bounded offer supported by evidence on 2026-09-19; it does not guarantee permanence, production suitability, uptime, latency, model quality, or access from every region.
Resolve the live model route
No stable model ID is recorded. The offer may use a dynamic catalog, a provider-selected route, or a model class, so resolve the current machine-readable ID before writing a fixed production configuration. The recorded base endpoint is https://bedrock-mantle.{region}.api.aws/openai/v1, but the provider's current API reference remains authoritative for paths, authentication, and request shape.
Confirm account and billing boundaries
The public evidence does not settle whether an account is required. Confirm the signed-in flow before treating anonymous or credential-free access as available. A payment method is required. A zero-cost allowance can still be useful, but protect the account with provider-side budgets or alerts before sending production traffic. The equivalent paid value shown here prices a documented allowance where possible; it is not cash, guaranteed savings, or protection from overage.
Re-read the prompt policy for your plan
This record classifies the reviewed handling as “Partially private”: Bedrock is zero-retention and no-training by default, but named model routes have current abuse-review retention exceptions. Provider policies can distinguish free, paid, enterprise, opted-in, and feature-specific traffic, so verify the governing terms for the exact account and route that will receive your data.
Follow the evidence, then re-check it
The record links 12 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest governing document or live catalog when sources disagree, and submit a correction when a provider changes a material term.
Match the quota to the workload shape
Translate the published allowance into the traffic pattern you actually expect instead of comparing headline totals alone. A daily token pool can look generous while a low requests-per-minute or concurrency ceiling blocks interactive bursts; a high request limit can still fail a long-context job when input, output, or per-request tokens are capped. Separate prompt tokens from generated tokens, include retries and tool calls, and test the largest realistic payload. If the provider documents more than one limit window, the tightest window at your peak load is the practical ceiling. Research, experimental, and community access can also carry eligibility or fair-use constraints that cannot be modeled as a simple number.
Plan fallback without changing the rules
A fallback should preserve more than API syntax. Confirm that the substitute route supports the required modality, context length, streaming behavior, structured output, tools, and safety controls, then compare its prompt-retention and training terms. An OpenAI-compatible request shape does not make providers operationally or contractually equivalent. Decide which errors may trigger a retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. If deterministic output matters, record the model revision and sampling settings; rotating aliases and free-model pools can change behavior even when the endpoint remains available.
Monitor the offer as a dependency
Capture the model ID, response model field, rate-limit headers, usage fields, latency, HTTP status, and any provider request identifier for each test. Watch for authorization failures, quota exhaustion, catalog removal, policy revisions, and dashboard balance changes as separate failure modes. Re-check the provider’s live catalog and governing pages on a schedule proportionate to the workload’s importance, and keep the dated sources that supported your decision. Free capacity is especially suitable for prototypes, evaluations, fallbacks, and bounded workloads when the application can tolerate change; a production dependency still needs observability, an exit path, and an owner responsible for re-verification.
Test one complete request before scaling
Start with the smallest permitted request using the exact credential, model ID, endpoint, and account type you intend to deploy. Record the HTTP status, response headers, usage fields, latency, and any dashboard balance change. Then exercise the failure path: an invalid model, an exhausted quota, or a rate-limit response should fail clearly without silently switching to a paid route. If the provider supports streaming or tool calls, validate those features separately because a free model can expose a narrower capability set than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and avoid sending sensitive data until the observed route matches the reviewed data agreement.