LLM.API
1 model route currently listed at zero price.
https://api.llmapi.ai/v1Models mentioned
zaya1-8bEquivalent paid value
How this was valued: The published allowance or a defensible paid comparison is not precise enough to calculate.
A request-rate limit is published, but response sizes and a like-for-like paid rate are not.
Limits and terms
- Without Purchased Credits
- 5 requests per 10 minutes
- After Adding Credits
- 20 requests per minute for free models
What happens to your prompts?
The gateway defaults to zero retention and no training, but upstream providers are independently governed and optional logging changes retention.
- Plan Scope
- LLM.API gateway. The gateway's defaults and optional All Data Mode are separate from each selected AI provider's independent terms.
- Prompt Retention
- Gateway default is zero content retention beyond transaction time. Optional All Data Mode retains prompts and outputs for up to 90 days for analytics, semantic caching, and debugging.
- Response Retention
- Same gateway policy as prompts; cached responses and All Data Mode create retained content.
- Ordinary Logging
- Request metadata is maintained for billing and analytics. Account, billing, fraud, security, and service-improvement data may be processed independently of customer instructions.
- Model Training
- LLM.API says it does not train, fine-tune, evaluate, benchmark, or improve models with content processed under its DPA. Upstream AI providers may use inputs or outputs for improvement or training under their own terms.
- Product Improvement
- The gateway may use controller-side operational data for service improvement, but its DPA prohibits model improvement with processor content. All Data Mode enables product features using retained content.
- Human Or Operator Access
- LLM.API says it does not inspect routed content except where All Data Mode is enabled; authorized personnel processing personal data must be under confidentiality obligations.
- Subprocessors And Routing
- Requests go to an independently governed AI provider that is expressly not treated as LLM.API's subprocessor. Customers must assess and contract with that provider themselves.
- Deletion Controls
- All Data Mode can be disabled and its retained content deleted from the dashboard at any time. Upstream deletion and data-subject requests remain provider-specific.
- Caveat
- The gateway's zero-content-retention and no-training promises do not bind the final AI provider. The terms prohibit submitting personal, confidential, or third-party material without all necessary rights and authorizations.
Governing documents
- Terms Of Use
- https://llmapi.ai/terms/
- Data Processing Agreement
- https://llmapi.ai/dpa/
Eligibility
- Account Required
- Yes
- Payment Method Required
- No
Primary sources
Before you build with LLM.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
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.llmapi.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”: The gateway defaults to zero retention and no training, but upstream providers are independently governed and optional logging changes retention. 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 5 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.