---
title: "Weights & Biases Serverless Inference free inference"
description: "A possible free offer exists, but a decisive term still needs verification."
canonical_url: "https://freeinferencing.com/provider/wandb_serverless_inference/"
md_url: "https://freeinferencing.com/provider/wandb_serverless_inference.md"
last_updated: "2026-09-19"
---

# Weights & Biases Serverless Inference

> A possible free offer exists, but a decisive term still needs verification.

## Classification

- **Directory:** Watchlist
- **Free access:** Limited promotion
- **Status:** Promotional Credit Amount Unpublished
- **Confidence:** High
- **Payment card:** Yes
- **Account:** Required
- **Equivalent paid value:** Not quantifiable
- **API endpoint:** `https://api.inference.wandb.ai/v1`

## Models mentioned

- `deepseek-ai/DeepSeek-V4-Flash-0731`
- `deepseek-ai/DeepSeek-V4-Pro-0813`
- `deepseek-ai/DeepSeek-V3.1`
- `google/gemma-4-31B-it`
- `ibm-granite/granite-4.2-8b`
- `meta-llama/Llama-3.3-70B-Instruct`
- `meta-llama/Llama-3.1-8B-Instruct`
- `MiniMaxAI/MiniMax-M3`
- `moonshotai/Kimi-K2.7-Code`
- `moonshotai/Kimi-K2.6`
- `nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B`
- `nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B`
- `openai/gpt-oss-120b`
- `openai/gpt-oss-20b`
- `Qwen/Qwen3.8-27B`
- `Qwen/Qwen3.6-35B-A3B`
- `zai-org/GLM-5.3-Flash`
- `zai-org/GLM-5.2`

## Limits and terms

```yaml
promotional_credit_amount: not_published
availability: limited_time
recurrence_or_expiry: not_published
concurrency: Applied per project and per user, but no numeric public limit is stated.
default_free_account_spending_cap_usd_per_month: 100
spending_cap_caveat: The cap limits paid usage and is not free credit.
```

## What happens to your prompts?

**Partially private.** Privacy protections are incomplete, conditional, or not fully documented.

Privacy is audited separately from price. Review the current governing terms before sending sensitive or regulated data.

## Data governance and agreements

```yaml
data_governance:
  review_status: not_found
  plan_scope: W&B Serverless Inference promotional credits and CoreWeave-powered
    model routing.
  prompt_retention: not_documented
  response_retention: not_documented
  ordinary_logging: W&B Weave can trace requests when configured, but the default
    Serverless Inference content log and retention policy is not stated on the
    offer pages.
  model_training: not_documented
  product_improvement: not_documented
  human_or_operator_access: not_documented
  subprocessors_and_routing: W&B directs users to CoreWeave terms for geographic
    availability; current pages do not resolve model-route-specific content
    handling.
  deletion_controls: not_documented
  caveat: The plan and model docs establish access, credits, and routing but do
    not provide a complete free-plan inference-data policy.
```

## Eligibility

```yaml
account_required: true
entity_and_project_required: true
geographic_restrictions_apply: true
payment_required_after_promotional_credits: true
```

## API compatibility and modalities

- **Compatibility:** OpenAI Compatible
- **Modalities:** Model inference


## Before you build with Weights & Biases Serverless Inference

### Read the classification narrowly

This record 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 account and region. “Limited promotion” should be interpreted together with the Watchlist directory placement, High confidence, Yes payment-card status, and the plan-specific sources below.

### Resolve the live model route

The snapshot records 18 model IDs. Match the exact ID against the provider's current catalog before using it in code because zero-price routes, aliases, context limits, and feature support can rotate while an older documentation page remains online.

### Match the quota to the workload shape

Translate the allowance into peak requests per minute, input and output tokens, concurrency, retries, and every daily or monthly ceiling that applies to the intended account. The first limit reached by the workload is the practical ceiling. A large token pool can still fail interactive bursts, and a high request limit can still fail long-context work. Include tool calls and retry traffic, test the largest realistic payload, and treat research, experimental, and community access as conditional on their eligibility and fair-use terms.

### Preserve billing and privacy boundaries

The recorded payment-card requirement is Yes, and account access is required. Confirm both in the live signup flow, set provider-side budgets when charges are possible, and observe the balance or usage fields after a complete request. The prompt-handling classification is **Partially private** because Privacy protections are incomplete, conditional, or not fully documented. Re-read the terms for the exact route and plan before sending sensitive, regulated, or proprietary content.

### Follow the evidence, then re-check it

This record links 5 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest and most specific governing document or live catalog when sources disagree. A dated finding can become stale even when the page remains online, so retain the source and snapshot that supported the decision and submit a correction when a provider changes a material term.

### Plan fallback without policy drift

A fallback should preserve modality, context length, streaming, structured output, tools, safety controls, and data terms, not only API syntax. Decide which errors may retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. Rotating aliases and free pools can change behavior without changing the endpoint, so retain the response model field and test at least one substitute route before the primary offer becomes unavailable.

### Monitor the offer as a dependency

Capture the model ID, response model, rate-limit headers, usage fields, latency, HTTP status, and provider request identifier. Watch authorization failure, quota exhaustion, catalog removal, policy revision, and balance movement as separate failure modes. Re-check the live catalog and governing sources on a schedule proportionate to the workload's importance, and keep an owner and exit path for any production dependency on volatile free capacity.

### Test one complete request before scaling

Start with the smallest permitted request using the exact credential, model ID, endpoint, region, and account type intended for deployment. Record the status, headers, usage fields, response model, latency, and dashboard balance movement. Then exercise an invalid model, quota exhaustion, or rate limit so failure is explicit and cannot silently switch to a paid route. Validate streaming, structured output, and tool calls separately because a free model can expose fewer features than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and do not send sensitive data until the observed route matches the reviewed agreement.

## Primary sources

- [Serverless Inference](https://docs.wandb.ai/inference): Official Docs
- [Usage information and limits](https://docs.wandb.ai/inference/usage-limits): Official Docs
- [Available models](https://docs.wandb.ai/inference/models): Official Catalog
- [W&B pricing](https://wandb.ai/site/pricing/): Official Pricing
- [CoreWeave terms linked by W&B](https://docs.coreweave.com/policies/terms-of-service/terms-of-use): Linked Provider Terms

## Sitemap

See the full [semantic sitemap](/sitemap.md) for every page and markdown mirror.