WaveSpeedAI
$1 for new accounts; it does not recur.
Models 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: Face value of the one-time signup credit. No period normalization is shown because the public offer does not state an expiry.
Limits and terms
- Signup Credit USD
- 1
- Recurrence
- No
- Expiry
- not published
- Caveat
- Some premium models are unavailable to trial balances; eligible catalog entries have positive prices and draw down the credit.
What happens to your prompts?
A no-training statement exists, but prompt/output TTL, deletion, and third-party model handling remain unresolved.
- Plan Scope
- WaveSpeedAI image, video, and other hosted model APIs and web interface; public and third-party models can have separate terms.
- Prompt Retention
- The terms permit WaveSpeedAI to store and process Customer Data as necessary to provide outputs and associated services, but no inference-specific maximum retention period is published.
- Response Retention
- Outputs are Customer Data under the same undefined retention; customers are responsible for keeping copies.
- Ordinary Logging
- Service-performance, use, account, security, and transaction information is collected, and aggregated anonymized Resultant Data can be retained and used after the service term.
- Model Training
- WaveSpeedAI's product materials state it does not use customer data for training, while the terms reserve service modifications and third-party-model rules. No selected-model training matrix was found.
- Product Improvement
- Aggregated anonymized Resultant Data can be used to improve services, development, diagnostics, and corrections; the terms do not authorize identifiable content training.
- Human Or Operator Access
- Content can be processed by WaveSpeedAI and necessary service providers for delivery, support, security, and legal compliance; no zero-operator-access commitment is published.
- Subprocessors And Routing
- Open and third-party models are available and their licenses and privacy practices can apply; WaveSpeedAI also uses infrastructure, analytics, payment, support, and model-processing providers.
- Deletion Controls
- not documented
- Caveat
- A no-training statement is available, but the public agreements do not state a prompt/output TTL or self-service deletion mechanism, so retention remains unresolved.
Governing documents
- Terms Of Service
- https://wavespeed.ai/static/terms
- Privacy Policy
- https://wavespeed.ai/static/privacy
Eligibility
- Account Required
- Yes
- Payment Method Required
- No
Primary sources
Before you build with WaveSpeedAI
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. 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”: A no-training statement exists, but prompt/output TTL, deletion, and third-party model handling remain unresolved. 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.