Respan span-01 Lite
A recurring no-cost API allowance covering 1 cataloged model.
https://api.respan.ai/api/v1Models mentioned
span-01-freeEquivalent paid value
How this was valued: The published allowance or a defensible paid comparison is not precise enough to calculate.
The daily cap is not numeric and no finite free token/request envelope is public. A request-size model cannot supply the missing request count. Pro costs $0.02/M but is a distinct model, not an exact Lite paid comparison.
Limits and terms
- Daily Cap
- Numeric amount unpublished; resets at 00:00 UTC.
- Reset
- 00:00 UTC
- Behaviors Per Request
- No per-request behavior-count limit is documented; this does not remove the daily cap.
What happens to your prompts?
The public policies do not establish span-01-free content retention, training exclusions or operator-access controls.
- Plan Scope
- span-01-free native /scores inference; generic public legal policies lack API-content detail.
- Prompt Retention
- No span-01-specific retention promise found; observability traces may separately be stored.
- Response Retention
- No score-specific retention period found.
- Ordinary Logging
- Generic service and diagnostic usage logging disclosed.
- Model Training
- No explicit plan-specific exclusion or permission found for private /scores inputs.
- Product Improvement
- Personal information may support service improvement; API-content scope is unresolved.
- Human Or Operator Access
- Not documented for free-classifier inputs.
- Subprocessors And Routing
- First-party classification model; DPA/BAA available on request, not reviewed.
- Deletion Controls
- Generic account/privacy requests; no public score-input deletion controls found.
- Conflict
- Public contribution licensing is not treated as proof that private API traces are published or licensed.
Governing documents
- Terms Of Service
- https://www.respan.ai/legal/terms-of-use
- Privacy Policy
- https://www.respan.ai/legal/privacy-policy
Eligibility
- Account Required
- Yes
- Payment Method Required
- No
Primary sources
Before you build with Respan span-01 Lite
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 2026-10-06; 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://api.respan.ai/api/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 “Partially private”: The public policies do not establish span-01-free content retention, training exclusions or operator-access controls. 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.