Beam
$30 in credits refresh on a published schedule.
https://{app}-{deployment}-v{version}.app.beam.cloudModels 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: Face value of Beam's recurring shared compute credit. The value is provider spend, not a separate $30 allocation for every deployed model.
Limits and terms
- Plan Price USD Per Month
- 0
- Included Credit USD Per Month
- 30
- Reset
- monthly
- GPU Concurrency
- 5
- Cpu Concurrency
- 30
- API Requests
- unlimited
What happens to your prompts?
Beam permits storage and expressly permits use of customer data and queries to measure and improve the service.
- Plan Scope
- Beam cloud compute, APIs, and customer-deployed models under Smartshare's public terms.
- Prompt Retention
- Beam's terms say it may hold and store Your Data on the customer's behalf, but they do not publish an inference-payload retention period or a zero-retention default.
- Response Retention
- Outputs and other customer-created data can be stored under the same customer-data provisions; no fixed deletion schedule is published.
- Ordinary Logging
- Account, browser, IP, site-use, and service metadata are collected, and customer queries, submitted models, and usage metadata may be processed to measure and improve the service.
- Model Training
- not documented
- Product Improvement
- The terms expressly permit Beam to use customer data, queries, submitted models, and usage metadata to measure and improve the service.
- Human Or Operator Access
- Stored data may be processed to provide, monitor, support, and improve the service; the public documents do not define operator-access restrictions for inference content.
- Subprocessors And Routing
- Beam uses infrastructure and service subprocessors including Google and Sentry and offers a DPA on request for EU personal data.
- Deletion Controls
- Personal-data correction and deletion requests are available, but no service-content deletion deadline or self-service inference-history control is documented.
- Caveat
- The public agreement provides broad improvement permission and is silent on model training and inference retention. Do not submit sensitive data without a negotiated DPA and clearer controls.
Governing documents
- Terms And Conditions
- https://docs.beam.cloud/v2/security/terms-and-conditions
- Privacy Policy
- https://docs.beam.cloud/v2/security/privacy-policy
Eligibility
- Account Required
- Yes
- Payment Method Required
- Yes
- Plan Selection Required
- Developer pay-as-you-go
Primary sources
Before you build with Beam
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://{app}-{deployment}-v{version}.app.beam.cloud, but the provider's current API reference remains authoritative for paths, authentication, and request shape.
Confirm account and billing boundaries
An account is required, so public catalog evidence cannot by itself prove the limits, balance, or billing behavior attached to your workspace. 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 “Not private”: Beam permits storage and expressly permits use of customer data and queries to measure and improve the service. 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 10 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.