Mancer AI
Operated by Sunlit Software, Inc.
1 model route currently listed at zero price.
https://neuro.mancer.tech/oai/v1Models mentioned
mytholiteEquivalent paid value
How this was valued: The published allowance or a defensible paid comparison is not precise enough to calculate.
Free-model quota and paid comparison are not published numerically.
Limits and terms
- Numeric Quota
- not published
- Official Claim
- Free models continue working even with a negative credit balance.
What happens to your prompts?
Default terms permit retention, third-party disclosure, model training, and service improvement unless the user opts out.
- Plan Scope
- Mancer AI hosted inference platform and its catalog of open and proprietary third-party models.
- Prompt Retention
- Customer Content may be used and retained to provide, maintain, develop, improve, secure, and enforce the service; no prompt-specific maximum retention period is published.
- Response Retention
- Outputs fall within Customer Content under the same unspecified retention and use policy.
- Ordinary Logging
- Account, request, security, fraud, usage, and service information is retained while the account exists and afterward as needed for claims, fairness records, and legal requirements.
- Model Training
- Mancer's standard terms allow it to train models with Customer Content and to provide content to third parties for their business purposes unless the user follows the platform's opt-out process.
- Product Improvement
- Customer Content may be used to develop and improve Mancer's services by default.
- Human Or Operator Access
- Retained content can be processed for development, improvement, security, abuse prevention, support, and legal purposes; no operator-blind commitment is offered.
- Subprocessors And Routing
- Requests may use multiple open-source or proprietary models with their own licenses and restrictions; third-party content access is allowed by default unless opted out.
- Deletion Controls
- A platform opt-out is offered for future training and third-party business access. Account deletion does not guarantee immediate removal of all violation, claim, or legally required records.
- Caveat
- Training and third-party business use are opt-out, not opt-in, and the public documents provide no fixed inference-content retention period.
Governing documents
- Terms Of Use
- https://mancer.tech/terms
- Privacy Policy
- https://mancer.tech/privacy
Eligibility
- Account Required
- Yes
- Payment Method Required
- No
Primary sources
Before you build with Mancer AI
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. The recorded base endpoint is https://neuro.mancer.tech/oai/v1, 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. 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 “Not private”: Default terms permit retention, third-party disclosure, model training, and service improvement unless the user opts out. 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 6 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.