Sail Research
$5 in credits refresh on a published schedule.
https://api.sailresearch.com/v1Models mentioned
moonshotai/Kimi-K3zai-org/GLM-5.3zai-org/GLM-5.3-Flashdeepseek-ai/DeepSeek-V4.1-Flashdeepseek-ai/DeepSeek-V4-Pro-0813deepseek-ai/DeepSeek-V4-Flash-0731moonshotai/Kimi-K2.6google/gemma-4-31B-itnvidia/Gemma-4-31B-IT-NVFP4google/gemma-4-12B-itopenai/gpt-oss-120bQwen/Qwen3.6-35B-A3BEquivalent paid value
How this was valued: Face value of Sail's recurring shared model credit; per-model values are maxima against the same balance and are not additive.
Limits and terms
- Included Credit USD Per Month
- 5
- Reset
- monthly
- Rate Limits
- No strict public limits; service priority depends on completion window.
What happens to your prompts?
The controlling incorporated DPA requires ephemeral inference processing for Sail/subprocessors and prohibits training/improvement without written consent; stateful Sailboxes are separate.
- Plan Scope
- Sail inference APIs including self-service customers; stateful Sailboxes have separate storage.
- Prompt Retention
- Current controlling DPA requires ephemeral in-memory processing only for the minimum request duration, by Sail and subprocessors.
- Response Retention
- Same DPA commitment; inference outputs are not persistently stored after request delivery.
- Ordinary Logging
- Operational metadata supports scheduling, billing and security; inference content is excluded from persistent storage.
- Model Training
- DPA prohibits training, fine-tuning or model improvement without prior written consent and extends this to model-provider subprocessors.
- Product Improvement
- Customer content is excluded from model improvement absent written consent; operational metrics may support the service.
- Human Or Operator Access
- Authorized processing personnel are confidentiality-bound; ephemeral processing limits retained-content access.
- Subprocessors And Routing
- Contracts impose substantially equivalent protections; Sail remains responsible for subprocessors.
- Deletion Controls
- Inference content is not persisted; Sailboxes/checkpoints and customer-managed storage require separate controls.
- Conflict
- Terms section 3.3 still permits response storage that cannot be disabled. The incorporated DPA expressly takes precedence and forbids persistent inference storage, superseding the older 48-hour S3 lifecycle.
Governing documents
- Terms Of Service
- https://www.sailresearch.com/terms
- Privacy Policy
- https://www.sailresearch.com/privacy
- Data Processing Agreement
- https://docs.sailresearch.com/dpa
Eligibility
- Account Required
- Yes
- Payment Method Required For Monthly Refresh
- Yes
- Allowed Use
- Internal business use only; self-service terms exclude consumer use.
Primary sources
Before you build with Sail Research
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 12 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. The recorded base endpoint is https://api.sailresearch.com/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. 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”: The controlling incorporated DPA requires ephemeral inference processing for Sail/subprocessors and prohibits training/improvement without written consent; stateful Sailboxes are separate. 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 9 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.