How to use
Featherless.ai.

1. 01

   Start at https://featherless.ai/ and confirm the domain before entering account or payment information.

2. 02

   Availability: Featherless.ai is accessed through its browser dashboard and OpenAI-compatible HTTPS API, with catalog and model-detail endpoints for discovery.

3. 03

   An account and plan appropriate to interactive or application use, a server-side API key, acceptance of gated model terms, model-specific licenses and safety controls, evaluation data, and output review.

4. 04

   Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.

5. 05

   Run one low-risk test task before connecting sensitive files, repositories, or workspace data.

• Use official download pages.
• Review permissions during setup.
• Keep installers and applications updated.

1. 01

   Define the task, audience, content boundaries, languages, context, tool needs, output schema, quality, latency, license, privacy, and cost criteria.

2. 02

   Query the current model catalog and shortlist only models available on the plan whose license, gated status, context, completion limit, capabilities, and content flags fit.

3. 03

   Run a frozen evaluation set using server-side credentials, identical prompts and parameters, captured model IDs, errors, latency, usage, and human-scored outputs.

4. 04

   Choose and pin a model with a tested fallback, enforce input and output limits, monitor delisting or state changes, and reevaluate before switching models.

• State the outcome before the background.
• Provide the real source material.
• Review the result before expanding the task.

1. 01

   Restate the task, the acceptance criteria, and the failure cost before evaluating any response.

2. 02

   Test with a fixed evaluation set rather than ad-hoc prompts, so changes are measurable across model versions.

3. 03

   Check determinism and drift: record model version, parameters, and date, because providers update models underneath you.

4. 04

   Probe the failure modes deliberately — ambiguous inputs, adversarial phrasing, and out-of-scope requests.

5. 05

   Validate structure as well as content: confirm schemas, types, and required fields survive real inputs.

• Pin and record the model version with every result.
• Evaluate against a fixed set, not one-off prompts.
• Validate output structure before it reaches downstream code.

1. 01

   Classify the data in each request before it is sent to a hosted model.

2. 02

   Check the provider's retention and training terms for your specific plan, not the marketing page.

3. 03

   Store API keys in a secret manager, scope them per project, and rotate them on a schedule.

4. 04

   Strip or tokenise personal and regulated data before it reaches the request body.

5. 05

   Set rate limits, spend caps, and monitoring so a runaway loop is bounded and visible.

• Featherless provides infrastructure but does not license third-party models to you. Gated access, acceptable use, commercial rights, attribution, content risks, context, and availability remain model-specific. Individual plans are described for interactive experimentation rather than arbitrary production integration. Keep keys server-side, verify plan and model terms, handle cold or unavailable models, moderate user input and output, and never assume catalog breadth guarantees fitness.
• Follow your organisation's approved-use policy.
• Never treat fluent output as authorization to act.

• Featherless overview ↗
• API overview ↗
• Models API ↗
• Privacy and logging ↗

Working on something like this? See how we handle product engineering, or book a call.