Scaleway Generative APIs
1M tokens for new accounts; it does not recur.
Models mentioned
glm-5.2deepseek-v4-flash-0731gpt-oss-120bmistral-small-3.2-24b-instruct-2506pixtral-12b-2409qwen3.6-35b-a3bqwen3-embedding-8bbge-multilingual-gemma2Equivalent paid value
How this was valued: The 1M shared text-token trial is assigned to the highest-priced captured eligible model, GLM 5.2, and its costlier output rate for a deterministic best-case upper bound, then the separately documented 60 transcription minutes are added. Text-model rows show alternative maxima for the same shared pool and are not additive.
Limits and terms
- Free Tokens
- 1,000,000
- Transcription Minutes
- 60
- Recurrence
- not explicitly documented
- Caveat
- Recorded as an account-level trial allowance, not a recurring free tier.
What happens to your prompts?
Scaleway excludes ordinary prompt collection and model training, hosts models on its own EU infrastructure, and limits incident retention.
- Plan Scope
- Scaleway Generative APIs hosted in Europe; batch processing and abuse investigations have explicit exceptions to the default zero-data-retention policy.
- Prompt Retention
- Prompts are not ordinarily collected, read, reused, or analyzed. For harmful misuse or failures, full HTTP request content may be stored and accessed for up to two weeks. Batch inputs are stored during processing for up to 24 hours.
- Response Retention
- Outputs are not ordinarily collected, read, reused, or analyzed; incident-related request content and batch-state exceptions apply.
- Ordinary Logging
- Anonymized request metadata, status codes, timestamps, input and output token counts, and non-content parameters are retained up to six months for performance and improvement.
- Model Training
- Scaleway states customer data is not used to train, retrain, or improve base models.
- Product Improvement
- Aggregated and anonymized metadata is used to monitor and improve the API; prompt and output content is excluded absent the documented incident exception.
- Human Or Operator Access
- Full request content may be accessed temporarily to reproduce and fix harmful misuse, unexpected errors, or security vulnerabilities.
- Subprocessors And Routing
- Scaleway says it hosts the models on its own European infrastructure without interaction with third-party model services; ordinary support and payment subprocessors remain possible.
- Deletion Controls
- Incident content expires within two weeks, batch input within 24 hours, and anonymized usage data within six months; account-data rights follow the general privacy policy and DPA.
- Caveat
- The service calls its default policy zero data retention, but explicit incident, batch, and metadata retention remain.
Governing documents
- AI Service Terms
- https://www-uploads.scaleway.com/Conditions_Particulieres_Services_IA_61a1a5f301.pdf
- Privacy Policy
- https://www.scaleway.com/en/privacy-policy/
- Data Processing Agreement
- https://www-uploads.scaleway.com/DPA_2024_ENG_b0abb5cc26.pdf
Eligibility
- Account Required
- Yes
- Valid Payment Method Required For Base Limits
- Yes
Primary sources
Before you build with Scaleway Generative APIs
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 8 model IDs. 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. 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. A payment method is required. A zero-cost allowance can still be useful, but protect the account with provider-side budgets or alerts before sending production traffic. 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 “Private”: Scaleway excludes ordinary prompt collection and model training, hosts models on its own EU infrastructure, and limits incident 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 10 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.