Weights & Biases pricing

About

Weights & Biases (W&B) is the experiment-tracking layer that became an MLOps standard. Founded in 2018 by Lukas Biewald and Chris Van Pelt, its open SDK (wandb) — a few lines you drop into a training script to log metrics, hyperparameters, and artifacts — spread to over 1 million users including OpenAI, Meta, NVIDIA, and Toyota. The platform has since grown four product surfaces: W&B Models (experiment tracking, sweeps, registry, artifacts), W&B Weave (LLM observability, tracing, evaluations, and LLM-as-a-judge scorers for GenAI apps), W&B Serverless Inference (a per-token endpoint for 20+ open-weight models), and the new CoreWeave Sandboxes.

The headline corporate fact: on May 5, 2025, CoreWeave completed its acquisition of W&B for a reported ~$1.7 billion (W&B was last valued at $1.25B in 2023). The deal slots a developer-facing platform on top of CoreWeave’s GPU cloud, creating a path from raw compute to experiment tracking to inference. CoreWeave publicly committed to keeping the platform interoperable — customers can still use any infrastructure provider, deployment option, and foundation model — but the pricing now bundles CoreWeave Sandboxes and a first-party inference endpoint, both signs of the parent’s footprint.

For the most current information, visit Weights & Biases.

Pricing summary : How Weights & Biases’s pricing model works

W&B runs a freemium plan fee plus three usage meters. You pick a tier — Free ($0), Pro (starts at $60/month), or custom Enterprise — and each bundles a quota of seats, storage, and Weave data ingestion. The plan fee is only the floor; the variable bill comes from the meters.

The three things that actually scale your bill:

• Storage (GB) — model artifacts, datasets, and run history. Free includes 5 GB/mo, Pro 100 GB/mo, and overage runs $0.03/GB.
• W&B Weave data ingestion (GB) — the volume of logged LLM traces, evals, and monitoring data for GenAI apps. Free includes 1 GB/mo, Pro 1.5 GB/mo, and overage is $0.10/MB (i.e. ~$100/GB — far pricier than model storage, because it’s the live observability stream).
• W&B Inference (per token) — Serverless calls to 20+ open-weight models, billed per token in arrears (Pro ships a $5/mo credit).

Seats sit alongside these: Free allows up to 5 model seats, Pro up to 10. The legacy W&B Models meter — “tracked hours” (cumulative compute hours of logged training) — still surfaces in the academic license (“unlimited tracked hours”) and the older $50/user tiering, but the current published cloud tiers lead with storage + ingestion + inference.

What makes this different: most observability tools meter one thing. W&B meters four (seats, storage, traces, tokens) because it fused an MLOps tool, an LLM-observability tool, and an inference endpoint into one bill — so modeling your cost means modeling all four levers at once.

Pricing by product

Pro is gated to “early-stage teams fewer than 50 employees” — exceed that and W&B requires a move to Enterprise. Academic research is free forever (all Pro features, unlimited tracked hours, 200 GB storage, 25 GB/mo Weave ingestion, up to 100 seats).

Sales motions across products: self-serve credit-card checkout (Free/Pro), product-led growth via the free wandb SDK, and sales-led (Enterprise custom quotes, annual invoicing, single-tenant/private hosting).

W&B Serverless Inference per-token examples (per 1M tokens): NVIDIA Nemotron 3 Ultra $0.75 in / $0.15 cached / $2.75 out; GPT-OSS 20B $0.05 in / $0.20 out; Qwen3 235B $0.10 in / $0.10 out; DeepSeek V4-Flash $0.01 in / $0.01 out.

Hidden costs : What Weights & Biases users actually pay

The $60 Pro fee is rarely the real bill — the three meters are. The single biggest surprise is Weave data ingestion overage at $0.10/MB. That works out to roughly $100 per GB, and an LLM app that logs verbose traces, large prompts, and eval results can produce several GB a month without anyone noticing — so a team that budgeted “$60 for W&B” can see a four-figure ingestion line.

For a Pro team running real GenAI workloads, the math is roughly:

Other line items to watch: the 50-employee gate means growing teams are pushed off self-serve Pro into custom Enterprise quotes; HIPAA, SSO, audit logs, and BYO-bucket are Enterprise-only, so any regulated use jumps straight to a sales contract; and Inference is a separate per-token meter layered on top of everything — the $5/mo Pro credit barely dents real usage. CoreWeave Sandboxes also carry only a limited-time credit ($10 Free / $25 Pro) before becoming a paid add-on.

Want to estimate your own W&B bill? Use the Weights & Biases pricing calculator to model costs based on usage patterns. For broader context on metered observability spend, see the LLMOps cost-tracking gaps teams hit.

Pricing evolution : Weights & Biases pricing history and changes

Cadence

Tracked range: 2018–present. Snapshot image attached for the 2026 capture; earlier tiers reconstructed from G2/ZenML teardowns and archived pricing pages.

Notable changes

• 2018 — Launched the wandb SDK; free logging plus paid team/enterprise seats and a “compute hours” meter.
• 2022 — Meter terminology shifted to “tracked hours”; Starter/Pro sold at ~$50/user/mo, tiered by cumulative tracked hours (to $100/$150), $1/hr overage, $0.03/GB storage.
• 2024 — W&B Weave shipped, introducing Weave data ingestion (GB) as a new value metric for GenAI observability.
• 2025-05-05 — CoreWeave completed its ~$1.7B acquisition, folding W&B into its GPU cloud with an interoperability pledge.
• 2026 — Tiers restructured to Free / Pro ($60/mo) / Enterprise, metered on storage ($0.03/GB), Weave ingestion ($0.10/MB), and per-token Serverless Inference, plus CoreWeave Sandboxes credits.

What’s unique : Weights & Biases’s distinctive pricing mechanics

1. Four meters under one bill. Seats, storage (GB), Weave ingestion (GB), and Inference (tokens) all bill independently. That reflects W&B’s history as three products (Models, Weave, Inference) stitched together — and it means buyers must forecast four distinct levers, not one usage number.

2. The asymmetric storage-vs-ingestion price. Model storage overage is a cheap $0.03/GB; Weave trace ingestion is $0.10/MB — roughly 3,000x more per byte. W&B prices the live observability stream (hot, queryable LLM traces) far above cold model artifacts, a deliberate signal of where the marginal cost and the value sit.

3. The 50-employee Pro tripwire. Pro is explicitly restricted to teams under 50 employees; outgrow it and you must move to Enterprise. It’s a rare case of a vendor coding its self-serve-to-sales handoff directly into the pricing page rather than relying on usage thresholds.

Strengths & weaknesses

Billing UX : Weights & Biases billing controls and transparency

• Billing controls — Pro is billed monthly or annually upfront; model seats are prorated when added mid-term, with no credit for removed seats. Weave ingestion, Inference, and storage are billed monthly in arrears on actual usage. Enterprise is invoiced annually upfront.
• Usage visibility — The platform surfaces storage and ingestion consumption against quota; the published pricing table and FAQ explain how each meter is calculated. Per-model Inference rates are listed on a dedicated inference page.
• Payment options — Self-serve credit-card checkout and a 30-day Pro free trial; Enterprise moves to annual invoicing with contract terms, single-tenant hosting, and security addenda (HIPAA BAA, CMEK).

Strategic wins : Why Weights & Biases’s pricing decisions worked

1. Free SDK as the category land-grab

The free wandb logging SDK embedded W&B into ML training workflows at OpenAI, Meta, and thousands of labs before any sales motion. Owning the developer’s first import wandb made W&B the default — distribution CoreWeave ultimately paid a reported ~$1.7B to acquire. See usage-based pricing fundamentals.

2. Free-forever academic seeding

Switching off the paid meter (tracked hours) entirely for academics — plus 200 GB and 100 seats — trains the next generation of ML practitioners on W&B, who then bring it into industry. It’s a long-cycle funnel disguised as generosity. Related: how AI companies structure pricing.

3. Pricing the hot stream above the cold store

Charging $0.10/MB for live Weave traces but $0.03/GB for model artifacts aligns price with the genuinely expensive thing (queryable, retained observability data). It’s a clean example of choosing the right usage metric per product surface rather than one blunt unit.

Areas to improve : Gaps in Weights & Biases’s pricing approach

1. Four-meter forecasting

Seats + storage + ingestion + tokens is a lot to model. Teams routinely undershoot the Weave ingestion line because $0.10/MB doesn’t read as expensive until it’s a multi-GB monthly bill. Better in-product forecasting would reduce bill shock.

2. Governance gated to Enterprise

SSO, HIPAA, audit logs, and BYO-bucket all require a custom Enterprise contract. A security-conscious 20-person team that needs SSO has no self-serve path — it jumps from $60 Pro straight to sales.

3. The 50-employee cliff

Coding a headcount cap into Pro is clean for W&B but abrupt for customers: a team that crosses 50 employees is forced into renegotiation regardless of actual usage, which can feel punitive mid-project.

Key takeaways

1. A free SDK can be the whole moat. W&B’s import wandb default drove the adoption that made it the MLOps standard — and the reported ~$1.7B CoreWeave exit.
2. Meter each product surface on its own value unit. Storage GB, Weave trace ingestion, and per-token Inference each price what that surface actually costs — but four meters demand strong forecasting UX.
3. Price the hot stream above the cold store. $0.10/MB for live traces vs $0.03/GB for artifacts is a deliberate, defensible asymmetry.
4. Free-for-academics is a multi-year funnel. Switching off the paid meter for research seeds the practitioners who later buy.
5. Acquisition reshaped the bundle, not the philosophy. CoreWeave added Sandboxes and first-party Inference, but the freemium-plus-meters structure held.

UBP implications

1. When you sell multiple products, meter each on its own unit — but invest in a unified forecasting view, or buyers will model the plan fee and miss the meters that dominate the bill.
2. Differentiate hot vs cold data in your price. Live, queryable observability streams justify a far higher per-byte rate than cold artifact storage; charging one flat rate leaves money or trust on the table.
3. A free, uncrippled developer entry point compounds. W&B’s free SDK and free-forever academic tier built the distribution that made the company acquirable — proof that the top of the funnel is a pricing decision, not just marketing.

Sources

• Weights & Biases pricing page (accessed 2026-06-16)
• W&B Serverless Inference pricing (accessed 2026-06-16)
• CoreWeave: completes acquisition of Weights & Biases (May 5, 2025) (accessed 2026-06-16)
• TechCrunch: CoreWeave acquires AI developer platform Weights & Biases (accessed 2026-06-16)
• ZenML: WandB pricing guide (accessed 2026-06-16)
• G2: Weights & Biases pricing (accessed 2026-06-16)

Bottom line

Weights & Biases prices like the multi-product platform it became: a free, sticky experiment-tracking SDK at the top, a $60/mo Pro tier for small teams, and custom Enterprise for everyone regulated or over 50 employees — all sitting on three usage meters (storage at $0.03/GB, Weave trace ingestion at $0.10/MB, and per-token Serverless Inference). The free personal and free-forever academic tiers are a deliberate land-grab that built the 1M-user distribution CoreWeave paid a reported ~$1.7 billion for in May 2025. The real friction is forecasting: the plan fee is the floor, and Weave ingestion overage is where unprepared GenAI teams get a four-figure surprise.

Want to compare Weights & Biases against other AI infrastructure companies? Browse the pricing blueprint.

Pricing timeline : Major events on a vertical axis

Each milestone below corresponds to a public pricing change, product launch, or material adjustment. Major events use a filled marker; minor adjustments use a faded one.

Restructured tiers: storage + ingestion + per-token Inference

Jan 2026

Post-acquisition pricing settled on Free / Pro ($60/mo) / Enterprise, metered on storage ($0.03/GB overage), Weave ingestion ($0.10/MB overage), and per-token Serverless Inference, plus CoreWeave Sandboxes credits ($10/$25 per mo).

CoreWeave completes ~$1.7B acquisition

May 2025

CoreWeave closed its acquisition of W&B for a reported ~$1.7 billion (W&B last valued at $1.25B in 2023), folding the developer platform into CoreWeave's GPU cloud while pledging continued interoperability across infra providers and models.

W&B Weave launches for LLM observability

Jan 2024

W&B added Weave — tracing, evaluations, LLM-as-a-judge scorers, and production monitoring for GenAI apps — introducing a new value metric: Weave data ingestion (GB of logged traces), distinct from Models tracked hours.

'Compute hours' meter renamed to 'tracked hours'

Jan 2022

W&B Models pricing shifted terminology from cumulative 'compute hours' to 'tracked hours'; the Starter/Pro tier was sold at roughly $50/user/mo, tiered by cumulative tracked hours (to $100/$150) with $1/hr overage and $0.03/GB storage.

Launched as wandb experiment tracking

Jan 2018

Lukas Biewald and Chris Van Pelt founded W&B; the free experiment-tracking SDK (wandb) became the de-facto standard for logging ML training runs, monetized via team/enterprise seats and a 'compute hours' meter.