AI Tool
Predibase pricing, features, company info, and alternatives
A factual product page for Predibase as a developer platform for fine-tuning and serving open-source LLMs.
Last updated April 2026 · Pricing and features verified against official documentation
Pricing
Current public pricing tiers on file for Predibase, last verified Apr 25, 2026.
Free Plan
$0 / signup
Includes $25 in free credits, access for up to 1 user, 1 private serverless deployment, 2 concurrent training jobs, and free shared serverless inference for testing. Credits expire after 30 days.
Private Serverless Inference
From $2.14/hour / hour
Billed by the second. Public base prices are listed for L4, A10G, L40S, and A100 GPUs; H100 and H200 are enterprise-only.
Fine-tuning (SFT / Continued Pretraining)
$0.50 / 1M training tokens
Up to 16B models start at $0.50 per 1M training tokens; 16.1 to 80B models are priced at $3.00 per 1M training tokens.
Fine-tuning (Turbo LoRA)
$1.00 / 1M training tokens
Up to 16B models start at $1.00 per 1M training tokens; 16.1 to 80B models are priced at $6.00 per 1M training tokens.
Fine-tuning (RFT GRPO)
$10.00 / 1M training tokens
Up to 16B models start at $10.00 per 1M training tokens; 16.1 to 32B models are priced at $20.00 per 1M training tokens.
Enterprise Plan
Custom
Adds additional seats, guaranteed uptime SLAs, dedicated Slack support, consulting hours, and volume discounts on serving compute.
Virtual Private Cloud
Custom
Deploy fine-tuning and serving in your own cloud with enterprise security and compliance.
What You Can Do With It
The main capabilities that shape how people use Predibase today.
Fine-tuning supports LoRA, Turbo LoRA, Turbo, and custom base-model workflows through the SDK and UI.
Shared endpoints are available for experimentation, while private deployments are recommended for production workloads.
VPC deployments keep the dataplane in your cloud and support direct ingress through AWS PrivateLink-style routing.
The Python SDK and OpenAI-compatible API let teams create deployments, prompt models, and manage inference from code.
Best For
Who Predibase is most clearly built for.
ML and platform teams that want to fine-tune and serve open-source models in one product.
Enterprise buyers that need private cloud deployment, direct ingress, and SOC 2 Type II controls.
Developers who want a free public starting point before moving into private production infrastructure.
Model Notes
Current model information surfaced publicly for Predibase.
Latest model
Qwen 3
Model
Llama 4
Model
DeepSeek R1
Company
Leadership and company context for Predibase, Inc..
CEO
Piero Molino
Founders
Piero Molino, Travis Addair, Devvret Rishi, Chris Ré
Platforms
Where you can use Predibase today.
Web app
Python SDK
REST API
VPC
Integrations
Notable connected tools and ecosystem hooks for Predibase.
Hugging Face
Amazon S3
Snowflake
Databricks
BigQuery
Privacy Notes
Publicly stated data-handling notes that matter when evaluating Predibase.
Predibase says customer data is not used to train other models unless explicit permission is granted.
The privacy policy says Predibase processes text, images, and structured data on behalf of customers and may use third-party storage and hosting partners.
By default, Predibase does not log prompts or responses for deployments; request logging is opt-in.
Compliance
Public compliance or enterprise-governance signals we found for Predibase.
SOC 2 Type II
Access
How to integrate or build around Predibase.
Public API
Yes
Docs
Available
Alternatives
Other tools worth considering alongside Predibase.
Developer platform for running, fine-tuning, and deploying open models.
Cloud API for running public and private AI models, training custom models, and deploying them on managed infrastructure.
SambaNova's hosted inference cloud and OpenAI-compatible API for large open-source models.
Open AI platform for models, datasets, apps, deployment, and collaboration.
Product Snapshot
Predibase is a developer platform for fine-tuning and serving open-source LLMs. Its public materials focus on LoRA-based tuning, private deployments, shared endpoints, and cloud or VPC-based inference.
What You Can Do With It
- Fine-tune supported open-source base models through the UI or Python SDK.
- Serve shared endpoints for testing or private deployments for production workloads.
- Deploy into your own VPC and use direct ingress for tighter network control.
- Prompt deployments through the Python SDK or an OpenAI-compatible API path.
Why It Stands Out
Predibase combines fine-tuning, serving, and private-cloud deployment in one platform, which makes it useful when the model lifecycle needs to stay in one operational stack.
Tradeoffs To Know
- Public pricing is split across free credits, usage-based inference, fine-tuning rates, and enterprise or VPC quotes.
- Model availability changes quickly, so the public catalog should be checked before committing to a deployment plan.
- The strongest privacy and networking controls sit on the VPC-oriented paths rather than the shared public tiers.
Sources
- predibase.com/pricing
- predibase.com/privacy-policy
- predibase.com/blog/introducing-predibase-the-enterprise-declarative-machine-learning-platform
- predibase.com/blog/how-to-deploy-and-serve-qwen-3-in-your-private-cloud-vpc
- predibase.com/blog/deploy-llama-4-in-virtual-private-cloud-or-saas
- predibase.com/models
- docs.predibase.com/fine-tuning/models
- predibase.com/platform
- docs.predibase.com/inference/deployments/private
- docs.predibase.com/inference/querying-models/text-generation
- docs.predibase.com/inference/deployments/shared
- docs.predibase.com/sdk-reference/installation
- docs.predibase.com/fine-tuning/datasets
- docs.predibase.com/inference/fine-tuned
- docs.predibase.com/admin/vpc/privacy
- docs.predibase.com/resources/faq
- predibase.com/predibase-virtual-private-cloud
- predibase.com/blog/how-to-deploy-llama-4-in-virtual-private-cloud-or-saas
- docs.predibase.com/sdk-reference/deployments