Open-model serverless inference
How to use
Featherless.ai.
Exploring many Hugging Face language models without provisioning GPUs, comparing creative, role-playing, coding, and general chat models, and prototyping OpenAI-compatible integrations.
Featherless.ai is a Serverless inference platform offering OpenAI-compatible access to a broad catalog of open-weight language models. Exploring many Hugging Face language models without provisioning GPUs, comparing creative, role-playing, coding, and general chat models, and prototyping OpenAI-compatible integrations. This guide covers the whole path in one place: official access, a first session that produces something reviewable, the checks that make output trustworthy, and the permissions worth limiting before you connect real work.
AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.
Access & setup
Find, install, and sign in to Featherless.ai
Get into the official Featherless.ai experience with the right account and a setup you understand.
- 01
Start at https://featherless.ai/ and confirm the domain before entering account or payment information.
- 02
Availability: Featherless.ai is accessed through its browser dashboard and OpenAI-compatible HTTPS API, with catalog and model-detail endpoints for discovery.
- 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.
- 04
Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.
- 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.
First session
Your first useful Featherless.ai session
Learn the interaction loop using a small task with a clear outcome.
- 01
Define the task, audience, content boundaries, languages, context, tool needs, output schema, quality, latency, license, privacy, and cost criteria.
- 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.
- 03
Run a frozen evaluation set using server-side credentials, identical prompts and parameters, captured model IDs, errors, latency, usage, and human-scored outputs.
- 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.
Quality control
Check the quality of Featherless.ai output
Establish that a model's output is reliable enough for the job you are handing it.
- 01
Restate the task, the acceptance criteria, and the failure cost before evaluating any response.
- 02
Test with a fixed evaluation set rather than ad-hoc prompts, so changes are measurable across model versions.
- 03
Check determinism and drift: record model version, parameters, and date, because providers update models underneath you.
- 04
Probe the failure modes deliberately — ambiguous inputs, adversarial phrasing, and out-of-scope requests.
- 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.
Privacy & permissions
Use Featherless.ai safely
Understand what leaves your system on every call, and what the provider keeps.
- 01
Classify the data in each request before it is sent to a hosted model.
- 02
Check the provider's retention and training terms for your specific plan, not the marketing page.
- 03
Store API keys in a secret manager, scope them per project, and rotate them on a schedule.
- 04
Strip or tokenise personal and regulated data before it reaches the request body.
- 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.
Core workflows
Step-by-step ways to use Featherless.ai for the work it does best.
Each workflow below has its own outcome, steps and review checkpoints.
Filter the large catalog by plan, license, capability, context, and evidence before exposing a model to users.
↓ 02 WorkflowIntegrate Featherless.ai without leaking model or user riskUse the OpenAI-compatible API with server-side secrets, tenant controls, moderation, bounded generation, and resilient model-state handling.
↓Workflow 01
Select an open-weight model with Featherless.ai
Filter the large catalog by plan, license, capability, context, and evidence before exposing a model to users.
Outcome: Choose a legally usable, available model whose behavior is measured on the actual application task.
- 01
Define task, audience, languages, safety boundary, tools, schema, context, quality, latency, traffic, plan, commercial use, and cost criteria.
- 02
Query model details and filter current-plan availability, active state, license, gated access, capabilities, modalities, content flags, context, and completion limits.
- 03
Accept required upstream terms through the authorized Hugging Face account, then record exact model ID, metadata, license version, and plan suitability.
- 04
Run a frozen benchmark with identical prompts, capture errors and latency, and score task accuracy, safety, style, refusals, schema, long-context behavior, and cost.
- 05
Pin the selected model and fallback, enforce input and output limits, monitor availability and delisting, and reevaluate before changing model or plan.
What good use looks like
- Filter by license before quality.
- Record gated-model acceptance.
- Monitor model availability continuously.
Workflow 02
Integrate Featherless.ai without leaking model or user risk
Use the OpenAI-compatible API with server-side secrets, tenant controls, moderation, bounded generation, and resilient model-state handling.
Outcome: Operate a stable application integration that respects provider, plan, model, and user-content rules.
- 01
Confirm the production plan permits the application, document model terms and content policy, and define retention, moderation, rate, concurrency, cost, and incident controls.
- 02
Keep keys server-side, isolate tenants, set application attribution headers, validate inputs, remove secrets and personal data, and prevent user-selected arbitrary model IDs.
- 03
Call a pinned allowlisted model with explicit token, timeout, retry, temperature, schema, and budget limits, using idempotency records for consequential workflows.
- 04
Handle gated, unavailable, cold, delisted, rate-limited, malformed, and unsafe results without silently switching to an unevaluated model or publishing output.
- 05
Log non-sensitive metadata, monitor spend and errors, rotate keys, retest fallbacks, audit license changes, and keep every consequential action behind authorization and human review.
What good use looks like
- Allowlist model IDs server-side.
- Never silently switch models.
- Keep actions behind independent authorization.
Official references
Check the current product documentation.
Features, plan limits, availability, and data controls change. These official pages are the starting points used for this guide.