Predibase
Generative AI infrastructure software
Generative AI software
Large language model operationalization (LLMOps) software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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What is Predibase
Predibase is an LLMOps platform focused on building, fine-tuning, evaluating, and deploying large language models for enterprise applications. It targets ML engineers and data science teams that need to adapt open-source or proprietary foundation models to domain-specific tasks and serve them reliably in production. The product emphasizes model customization workflows (including fine-tuning) and operational tooling for inference, monitoring, and iteration across the model lifecycle.
End-to-end LLM lifecycle tooling
Predibase covers key stages of operationalizing LLMs, including model adaptation, evaluation, deployment, and ongoing iteration. This reduces the need to stitch together separate tools for training, experiment tracking, and serving. It is positioned for teams that want a single workflow from prototype to production rather than only a chatbot builder or a general analytics platform.
Fine-tuning and customization focus
The platform is oriented around adapting foundation models to specific enterprise tasks via fine-tuning and related customization workflows. This is useful for organizations that need higher task accuracy or domain alignment than prompt-only approaches typically provide. It also supports repeatable experimentation so teams can compare variants and promote a chosen model to deployment.
Production deployment and serving
Predibase includes capabilities to deploy models for inference and operate them as services, which is central to LLMOps. This helps teams move beyond notebooks into controlled environments with versioning and rollout practices. It is designed for engineering-led use cases where reliability, latency, and maintainability matter.
Less suited for non-technical teams
Predibase is primarily built for ML and engineering users rather than business users who want no-code assistants. Organizations seeking turnkey conversational UX design, contact-center workflows, or end-user productivity features may need additional application-layer software. Adoption typically requires ML/DevOps skills to integrate with data sources and production systems.
Not a full data platform
While it supports model operations, it is not positioned as a comprehensive data integration, governance, and BI environment. Teams may still rely on separate systems for data preparation, cataloging, access controls, and broader analytics workflows. This can increase architectural complexity in enterprises with strict data management requirements.
Model and infrastructure trade-offs
LLMOps outcomes depend on the chosen base models, available compute, and deployment constraints (cloud, region, or on-prem requirements). If an organization has strict infrastructure standards or needs deep control over low-level serving stacks, additional engineering work may be required. Cost and performance can vary materially based on fine-tuning strategy and inference scale.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Free | $0 (Free Plan) | Up to 1 user; best-in-class fine-tuning with A100 GPUs; 1 private serverless deployment (no rate limits); autoscaling and scale-to-0; serve unlimited adapters on a single GPU with LoRAX; free shared serverless inference (rate-limited) for testing; access to all base models; file-upload data connection; 2 concurrent training jobs; in-app chat, email, and Discord support; includes $25 in free credits (expire after 30 days). |
| Enterprise (SaaS) | Custom / Contact sales | Everything in Free Tier plus additional seats, volume discounts on serving compute, guaranteed instances & replicas, additional private serverless deployments, uptime SLAs, connectors (Snowflake, Databricks, S3, BigQuery), more concurrent training jobs, dedicated Slack channel and consulting hours. |
| Enterprise (VPC) | Custom / Contact sales | Enterprise Virtual Private Cloud deployment into your cloud (AWS/Azure/GCP); use your own cloud commitments and GPUs; enterprise security/compliance; same production features as Enterprise SaaS but deployed in your VPC. |
Usage-based pricing (official page lists the following):
Private serverless inference (billed by the second; base price per hour shown):
- 1 L4 (24 GB): $2.14 / hr.
- 1 A10G (24 GB): $2.60 / hr.
- 1 L40S (48 GB): $3.20 / hr.
- 1 A100 (80 GB): $4.80 / hr.
- 1 H100 (80 GB): Enterprise-only (price via sales).
- 1 H200 (141 GB): Enterprise-only (price via sales).
Shared serverless inference:
- Free to use for experimentation up to 1M tokens per day and 10M tokens per month (rate-limited free shared inference for testing). Models supported are listed on the official models page.
Batch / token pricing example:
- $30 per 1M input tokens; $60 per 1M output tokens (listed on pricing page as batch high-throughput example).
Fine-tuning pricing (per 1M tokens) — official listed rates:
- Up to 16B (SFT, Continued Pretraining, LoRA/Turbo): $0.50 per 1M tokens.
- 16.1–80B (SFT, Continued Pretraining, LoRA/Turbo): $3.00 per 1M tokens.
- Up to 16B (Turbo LoRA): $1.00 per 1M tokens.
- 16.1–80B (Turbo LoRA): $6.00 per 1M tokens.
- Up to 16B (RFT GRPO (LoRA)): $10.00 per 1M tokens.
- 16.1–32B (RFT GRPO (LoRA)): $20.00 per 1M tokens.
Notes & caveats:
- Enterprise-grade GPUs (H100/H200 and multi-H100/H200) are marked enterprise-only; contact sales for pricing and availability.
- The site provides cost calculators for inference and fine-tuning (estimators); Predibase notes estimates vary by hardware selection and that enterprise discounts may apply.
- Model availability and exact costs for specific deployments may change; official model list is on predibase.com/models.
Seller details
Predibase, Inc.
Unsure
Private
https://predibase.com/
https://x.com/predibase
https://www.linkedin.com/company/predibase/
