Unbiased Pareto
A finite allowance for new accounts; it does not recur.
Models mentioned
pareto-26.9Equivalent paid value
How this was valued: The published allowance or a defensible paid comparison is not precise enough to calculate.
The seven-day evaluation allocation and rate limit are unpublished, so Pareto's current token prices cannot produce a defensible trial value.
Limits and terms
- Duration Days
- 7
- Free Evaluation Allocation
- amount not published
- Rate Limit
- unpublished
- Recurrence
- No
- Availability
- Anyone may request an evaluation allocation; new beta accounts are manually reviewed.
What happens to your prompts?
Unbiased says content is not retained beyond processing or used for training without written consent, but its newer data-policy notice says exact prompt and response retention terms are supplied before onboarding rather than publicly.
- Plan Scope
- Unbiased Pareto seven-day free evaluation allocation and beta API.
- Prompt Retention
- General terms say submitted content is not retained beyond processing except when required by law or for abuse prevention for up to 30 days, while the newer data-policy notice says the exact raw prompt retention window is supplied to members before onboarding.
- Response Retention
- The same public ambiguity applies to model responses; the member-specific onboarding terms govern the exact window.
- Ordinary Logging
- Account, request, usage, security, and abuse-prevention metadata are processed; the public terms do not provide a complete field-level retention schedule.
- Model Training
- Unbiased says it does not use submitted content to train or improve models without written consent.
- Product Improvement
- Content-based improvement requires written consent under the general terms; aggregate operational analysis may still occur.
- Human Or Operator Access
- Authorized personnel may access content when necessary for service operations, support, security, abuse review, or legal compliance.
- Subprocessors And Routing
- Requests may be routed through disclosed subprocessors and model providers including AWS, Anthropic, OpenAI, and xAI, subject to the selected route and onboarding terms.
- Deletion Controls
- Account or legal requests may apply, but no public self-service per-request deletion control is documented.
- Caveat
- Strong no-training language is favorable, but the public policy defers the controlling prompt and response retention window to member-specific pre-onboarding terms.
Governing documents
- Terms And Data Policy
- https://unbiased.ai/terms/
Eligibility
- Account Required
- Yes
- Application Or Manual Review Required
- Yes
- Payment Method Required
- not explicitly documented
Primary sources
Before you build with Unbiased Pareto
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
This snapshot records 1 model ID. Match the exact ID against the provider's live catalog before using it in code; zero-price routes and aliases can rotate while an older documentation page remains online. No stable base endpoint is published in this record, so use the linked provider documentation to identify the current request URL and protocol.
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. The reviewed evidence explicitly says no payment card is required. Re-check the signup flow because eligibility and billing controls can change after the snapshot. 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”: Unbiased says content is not retained beyond processing or used for training without written consent, but its newer data-policy notice says exact prompt and response retention terms are supplied before onboarding rather than publicly. 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 4 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.