Clarifai
$5 for new accounts; it does not recur.
https://api.clarifai.comModels 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 one welcome bonus spread across its 30-day validity. A possible second bonus is excluded because eligibility is conditional.
Limits and terms
- Signup Credit USD
- 5
- Expires After Days
- 30
- Recurrence
- No
- Maximum Welcome Bonuses
- 2
- Payg Requests Per Second Up To
- 100
- Caveat
- The current PAYG account documentation says up to 100 RPS; it is not a guarantee for every model route.
What happens to your prompts?
Inputs and predictions are stored by default and the general terms retain broad service-development rights.
- Plan Scope
- Clarifai cloud platform, inference, stored Inputs, prediction history, and customer-created training; private and Community-shared data differ.
- Prompt Retention
- Inputs and resulting predictions are stored by default so customers can review, search, and manage them in the portal; no universal automatic deletion period is published.
- Response Retention
- Prediction history is stored by default until the customer manages or deletes it.
- Ordinary Logging
- Request counts, compute usage, billing, monitoring, and other non-sensitive operational metadata are logged; more detailed logging features require opt-in according to current product documentation.
- Model Training
- Current docs say private data is not used to train Clarifai or other platform models unless the customer explicitly shares inputs and annotations with the Community.
- Product Improvement
- Aggregate usage and performance patterns may improve proprietary models without private input data. The general terms also authorize use of Your Content to develop and improve the service, creating a broader contractual permission than the narrower docs describe.
- Human Or Operator Access
- Stored private content is treated as confidential by default and is accessible under platform, support, security, and customer-authorized controls; Community sharing makes selected content public to that context.
- Subprocessors And Routing
- Clarifai and its subprocessors host the platform; third-party models can carry separate manufacturer or developer licenses.
- Deletion Controls
- Customers can manage stored inputs and prediction history and request account or personal-data deletion, subject to fraud, dispute, fee, and legal-retention exceptions.
- Caveat
- There is a material scope tension between the terms' broad service-improvement right and docs' no-private-data-training promise. The audit treats explicit training as opt-in but does not treat private content as contractually excluded from all product improvement.
Governing documents
- Terms Of Service
- https://clarifai.com/company/terms
- Privacy Policy
- https://clarifai.com/company/privacy-policy
Eligibility
- Phone Verification Required
- Yes
- Payment Method Required Initially
- No
- Payment Method Required To Recharge
- Yes
Primary sources
Before you build with Clarifai
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://api.clarifai.com, 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 “Not private”: Inputs and predictions are stored by default and the general terms retain broad service-development rights. 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.