Lelapa AI Vulavula API
A recurring no-cost API allowance with provider-published access terms.
https://api.lelapa.ai/v1Models 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: The free plan supplies 100 successful calls/month; the same-service Developer Pass is $9.99 per 1,000 calls, so the linear value is $0.999/month. The smallest replacement plan still costs $9.99/month.
Limits and terms
- Successful API Calls Per Month
- 100
- Unsuccessful Calls Count
- No
- API Key Max Age Days
- 90
- Translation Recommended Max Words
- 100
What happens to your prompts?
Lelapa says client data is not used for model training, but audio and transcript retention is governed by a DPA whose terms and TTL are not public.
- Plan Scope
- Vulavula hosted transcription and translation API Free plan.
- Prompt Retention
- Translation text and uploaded audio are processed under Lelapa's client agreement and DPA, but the public policy does not publish a content-retention maximum.
- Response Retention
- Transcript and translation-output retention is governed by the same nonpublic DPA terms; no public TTL was verified.
- Ordinary Logging
- Account, usage, security, and technical metadata may be retained; content-versus-metadata boundaries and durations are incomplete.
- Model Training
- Lelapa says client data is not used to train its models.
- Product Improvement
- No broad content-based improvement right was found, but the public policy does not fully describe all API quality workflows.
- Human Or Operator Access
- Authorized access follows the client agreement and DPA; public materials do not define the review scope.
- Subprocessors And Routing
- Lelapa and its disclosed service providers process requests; no external foundation-model routing layer is documented for Vulavula.
- Deletion Controls
- Privacy requests are available, while API audio, transcript, and translation deletion timing depends on the nonpublic DPA.
- Caveat
- The no-training commitment is favorable, but the unpublished DPA retention terms prevent a private classification.
Governing documents
- Privacy Policy
- https://lelapa.ai/privacy
- Terms Of Service
- https://lelapa.ai/terms
Eligibility
- Account Required
- Yes
- Payment Method Required
- No
- Residency Restriction
- not documented
Primary sources
Before you build with Lelapa AI Vulavula API
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.lelapa.ai/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”: Lelapa says client data is not used for model training, but audio and transcript retention is governed by a DPA whose terms and TTL are not public. 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 7 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.