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Predibase

  • What it is:Predibase is a low-code AI platform that enables developers to fine-tune and deploy customized open-source large language models with reduced costs and infrastructure efficiency.
  • Best for:ML Engineers and data science teams, Enterprises with large-scale inference workloads, Organizations prioritizing data security and compliance
  • Pricing:Free tier available, paid plans from Pay-as-you-go
  • Rating:85/100Very Good
  • Expert's conclusion:Predibase is the most suitable option for technical teams that develop production SLM (Specialized Language Model) applications where the need for high-efficiency inference and data control warrant the investment in infrastructure.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Predibase and What Does It Do?

Predibase is a developer-based platform offering services that allow users to fine-tune and serve large language models (LLMs). Predibase provides its customers with AI model fine-tuning, language model serving, and engineering team infrastructure solutions. These are primarily provided to Fortune 500 companies and high-growth startups within the AI and machine learning industries. Predibase was launched in 2021; however, it was acquired by Rubrik in June or July 2025.

Acquired
📍San Francisco, CA
📅Founded 2021
🏢Subsidiary
TARGET SEGMENTS
Fortune 500 companiesHigh-growth startupsEngineering teams

What Are Predibase's Key Business Metrics?

📊
$28.45M
Total Funding
📊
$31M Series B
Latest Funding
🏢
60+
Employees
📊
$150M
Valuation
📊
Multiple (up to Series A - II, Series B)
Funding Rounds

How Credible and Trustworthy Is Predibase?

85/100
Excellent

A young established AI infrastructure provider with considerable funding and an acquisition by Rubrik that has shown excellent market validation.

BREAKDOWN
Product Maturity80/100
Company Stability90/100
Security & Compliance75/100
User Reviews70/100
Transparency80/100
Support Quality80/100
TRUST SIGNALS
Acquired by Rubrik (2025)Raised $28M+ in fundingSeries B at $150M valuationServes Fortune 500 engineering teams

What is the history of Predibase and its key milestones?

2021

Company Founded

Founded by co-founders Devvret Rishi, Piero Molino, and Travis Addair in San Francisco, California.

2024

Series B Funding

Completed a $31 million Series B round at a $150 million valuation led by Felicis Ventures, Greylock Partners, and Workday Ventures.

2025

Acquired by Rubrik

Acquired by Rubrik in June/July 2025, Predibase became part of Rubrik’s AI infrastructure offerings.

Who Are the Key Executives Behind Predibase?

Devvret Rishi— Co-founder & CEO
Co-founded Predibase and is involved in developing the company's vision related to AI infrastructure and LLM serving.
Piero Molino— Co-founder & CTO
Co-founder and Chief Technology Officer of Predibase who is responsible for Predibase’s technological innovations in terms of fine-tuning and model serving.
Travis Addair— Co-founder
Contributed to the development of Predibase as one of the leading AI modeling platforms.

What Are the Key Features of Predibase?

✨
LLM Fine-tuning
A serverless platform that enables the efficient fine-tuning of large language models using LoRA technology.
✨
Model Serving
Offers lightweight, modular architecture for the dynamic serving of fine-tuned LLMs at scale.
✨
LoRA Land
An assortment of 25+ open-source fine-tuned models that rival GPT-4 in terms of performance across multiple use cases.
✨
Software Development Kit (SDK)
An SDK that allows developers to fine-tune and serve models using affordable GPU hardware.
✨
LoRAX Framework
An open-source framework that enables efficient serving of multiple fine-tuned models.
✨
Enterprise Infrastructure
Production-ready solutions for Fortune 500 engineering teams that implement AI solutions.

What Technology Stack and Infrastructure Does Predibase Use?

Infrastructure

Serverless fine-tuning and dynamic model serving infrastructure

Technologies

LoRAPyTorchPython

Integrations

Cloud GPU providersEnterprise data pipelinesDeveloper workflows

AI/ML Capabilities

Specialized in LoRA-based fine-tuning of LLMs with efficient serving via LoRAX framework, enabling cost-effective production deployment of specialized models

Inferred from product descriptions and announcements; specific stack details limited in available sources

What Are the Best Use Cases for Predibase?

Fortune 500 Engineering Teams
Provides access to deploy production-ready custom LLMs through the efficient fine-tuning and reliable serving of infrastructure that is scalable to the needs of enterprises.
High-growth AI Startups
Offers access to cost-effective fine-tuning and serving without having to build expensive ML infrastructure from scratch.
ML Researchers
Utilize LoRA Land’s free, open-source models and LoRAX framework to quickly prototype and compare various benchmarks.
Individual Developers
The SDK allows for fine tuning on inexpensive hardware; however, fine tuning may take extensive Machine Learning (ML) expertise to achieve best results.
NOT FORNon-technical Business Users
Developer-based platform that requires knowledge of ML engineering; thus, not ideal for no-code users.
NOT FORReal-time Inference Applications
Fine-tuning based, rather than ultra-low latency inference; may require additional optimizations for specialized serving requirements.

How Much Does Predibase Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
Free Tier$0Getting started tier with limited access to fine-tuning and serving capabilitiesSkywork AI analysis
Enterprise SaaSPay-as-you-goFine-tuning charged by tokens processed (varies by model size and technique). Inference billed by GPU usage in seconds. Can result in unpredictable costs for variable workloads.Eesel AI and Skywork AI
Enterprise VPCCustom pricingDeploy within your virtual private cloud using existing cloud spend commitments. Reserved GPU capacity (A100 and H100) available.Predibase by Rubrik
Free TrialAvailableFree trial access available to test platform capabilitiesSourceForge
Free Tier$0
Getting started tier with limited access to fine-tuning and serving capabilities
Skywork AI analysis
Enterprise SaaSPay-as-you-go
Fine-tuning charged by tokens processed (varies by model size and technique). Inference billed by GPU usage in seconds. Can result in unpredictable costs for variable workloads.
Eesel AI and Skywork AI
Enterprise VPCCustom pricing
Deploy within your virtual private cloud using existing cloud spend commitments. Reserved GPU capacity (A100 and H100) available.
Predibase by Rubrik
Free TrialAvailable
Free trial access available to test platform capabilities
SourceForge
💡Pricing Example: Enterprise deploying fine-tuned models with high-volume inference
Enterprise SaaS (Standard)Variable
Fine-tuning by tokens + GPU seconds for inference; cost varies with usage patterns
Enterprise VPC (Reserved)Custom quote
Reserved GPU capacity with predictable costs; leverages existing cloud commitments
💰Savings:Up to 80% cost reduction and 3-4x throughput improvement with Turbo LoRA optimization compared to traditional serving

How Does Predibase Compare to Competitors?

FeaturePredibaseTogether AIVertex AI
Primary Use CaseEfficient fine-tuning & multi-model servingFast inference API & fine-tuningAll-in-one MLOps suite
Target AudienceML Engineers / DevelopersDevelopersLarge enterprises
Pricing ModelGPU uptime + per-token (tuning)Per-tokenComplex, often expensive
Free TierYesYesLimited
Enterprise Features (SSO, RBAC)YesLimitedYes
API AccessYesYesYes
Cost Efficiency for Spiky TrafficLowerBetterVariable
Inference Speed3-4x improvement with Turbo LoRAStandardHigh
Private VPC DeploymentYesNoYes
Best ForMulti-model serving at scaleSpiky inference workloadsIntegrated enterprise workflows
Primary Use Case
PredibaseEfficient fine-tuning & multi-model serving
Together AIFast inference API & fine-tuning
Vertex AIAll-in-one MLOps suite
Target Audience
PredibaseML Engineers / Developers
Together AIDevelopers
Vertex AILarge enterprises
Pricing Model
PredibaseGPU uptime + per-token (tuning)
Together AIPer-token
Vertex AIComplex, often expensive
Free Tier
PredibaseYes
Together AIYes
Vertex AILimited
Enterprise Features (SSO, RBAC)
PredibaseYes
Together AILimited
Vertex AIYes
API Access
PredibaseYes
Together AIYes
Vertex AIYes
Cost Efficiency for Spiky Traffic
PredibaseLower
Together AIBetter
Vertex AIVariable
Inference Speed
Predibase3-4x improvement with Turbo LoRA
Together AIStandard
Vertex AIHigh
Private VPC Deployment
PredibaseYes
Together AINo
Vertex AIYes
Best For
PredibaseMulti-model serving at scale
Together AISpiky inference workloads
Vertex AIIntegrated enterprise workflows

How Does Predibase Compare to Competitors?

vs Together AI

Both provide fine tuned models, but differing in pricing approaches. Together AI’s token-based pricing is best suited for spiky, unpredictable traffic patterns. Predibase’s GPU uptime model is best suited for teams who are proactively managing their own infrastructure and have stable workloads. Predibase provides multi-model serving capabilities and through its Turbo LoRA offering achieves 3-4 times improvement in throughput over other methods.

If you need cost-optimized multi-model serving, then Predibase is your choice. If you need variable, spiky inference workload processing, then Together AI is your best bet.

vs Vertex AI

Google’s full-service MLOps platform provides complete solutions for large enterprises which operate within the Google Cloud Ecosystem. Predibase provides optimized, cost effective fine-tuning and serving of Small Language Models (SLMs). Vertex AI provides more broad functionality, however can be very costly. Predibase will provide better ROI for SLM focused use cases.

If you need cost effective, specialized SLM serving, then Predibase is your best bet. If you need comprehensive, fully-integrated Enterprise MLOps, then Vertex AI is your best bet.

vs Databricks & Snowflake

With Predibase being acquired by Rubrik, they will now have a competitive advantage in the AI Data Pipeline market. Databricks and Snowflake are focused on providing data infrastructure and analytics with AI capabilities. Predibase focuses on taking your proprietary data and turning it into fine-tuned models that are served securely and in a governed manner. Predibase will offer a more narrow, security-focused solution for operationalizing your protected data for Generative AI.

If you need to do secure, proprietary model fine-tuning and serving, then Predibase is your best bet. If you need broader data pipeline and analytics capabilities, then Databricks or Snowflake would be your best option.

What are the strengths and limitations of Predibase?

Pros

  • Exceptional Cost Efficiency — The use of Turbo LoRA and FP8 Quantization results in an overall reduction in infrastructure cost of over 50%, while increasing throughput by approximately 3 – 4 times.
  • Advanced Model Serving Technology — LoRAX and Turbo LoRA support running multiple, fine-tuned models on a single GPU, while maintaining low-latency.
  • Secure Data Management — The fully-managed infrastructure is available as part of the Enterprise VPC offering which provides maximum security and control of your data.
  • Proven At Scale — Used by numerous organizations (Checkr, Convirza, Nubank), all of which have hundreds of requests-per-second, and are trusted by large enterprises and startup organizations.
  • Comprehensive Solution — A unified platform that supports both fine-tuning and serving, with a user-friendly UI and extensive monitoring capabilities.
  • Flexible Deployment Options — Available in either the Predibase Cloud or within your organization’s VPC, and has the ability to reserve GPU capacity.
  • GPU Efficiency — Using FP8 Quantization, it is possible to reduce the memory footprint of a model by approximately 50%, providing cost savings.
  • Strong Security Position — As part of the acquisition of Predibase by Rubrik, the enterprise-grade security of Rubrik will be integrated into the AI infrastructure.

Cons

  • Variable Costs For Varying Workloads — A pay-as-you-go model based on GPU uptime may lead to unpredictable billing due to varying usage patterns.
  • Infrastructure Management Required — A pricing model that rewards active optimization of infrastructure, this may not be suitable for organizations seeking fixed costs.
  • Technical Expertise Required — Designed for ML Engineers and Developers, this platform may present a significant learning curve for non-technical users.
  • Limitations On Use Cases — This platform is specialized for small language model (SLM) fine-tuning and serving, and does not represent a comprehensive MLOps platform.
  • Newer Platform Maturity — Although proven in production environments, this platform may be considered less battle-tested at the enterprise scale when compared to other incumbent platforms such as Databricks or Vertex AI.
  • Ecosystem Of Integrations — Compared to more broadly applicable platforms, this platform has limited integrations and is focused specifically on AI model serving, versus integration with a full data pipeline.

Who Is Predibase Best For?

Best For

  • ML Engineers and data science teams — Purpose-Built — Built specifically for technical teams that need to fine-tune and serve custom models using proprietary data while being cost-efficient.
  • Enterprises with large-scale inference workloads — Hundreds of requests per second are handled, along with guaranteed GPU capacity reservations and SLA compliance.
  • Organizations prioritizing data security and compliance — Enterprise VPC deployment options that include Rubrik integration provide controlled and secure AI infrastructure for sensitive data.
  • Companies deploying multiple fine-tuned models — Serving multiple models on one GPU with Turbo LoRA significantly decreases infrastructure costs versus hosting each model individually.
  • Teams seeking to optimize inference costs — Advanced optimization techniques have the potential to decrease costs by up to 80% when compared to proprietary model hosting services.

Not Suitable For

  • Non-technical business users and teams without ML expertise — Deep infrastructure knowledge is required to optimize costs and deploy resources effectively; consider Anthropic or OpenAI APIs for easier integration.
  • Organizations needing fixed, predictable billing — Variable costs occur as a result of pay-as-you-go GPU uptime model. Platforms that use a subscription-based pricing model such as Vertex AI may be better suited for cost predictability.
  • Teams requiring comprehensive MLOps and data pipeline integration — A platform designed specifically for model serving; does not offer broad MLOps features. Consider Databricks or Snowflake for a combined data and ML workflow.
  • Developers preferring per-token pricing for spiky workloads — The pay-as-you-go GPU uptime model is less cost-effective when compared to predictable traffic patterns. Consider Together AI for per-token pricing during inference.

Are There Usage Limits or Geographic Restrictions for Predibase?

Fine-tuning Pricing Model
Charged by tokens processed; pricing varies by model size and technique (LoRA, RFT)
Inference Pricing Model
Billed by GPU usage in seconds following pay-as-you-go model
GPU Capacity (Standard)
Available on demand from Predibase fleet (A100 and H100 GPUs)
GPU Capacity (Enterprise)
Reserved GPU resources guaranteed with SLA commitments
Deployment Options
Predibase managed cloud or customer's private VPC
Models Supported
Open-source small language models (SLMs); cannot bring proprietary models
Cost Predictability
Variable costs with pay-as-you-go model; fixed costs only available with Enterprise VPC reserved capacity
Geographic Availability
Information not provided in available sources
Compliance
Rubrik integration provides enterprise-grade security; specific certifications (SOC 2, HIPAA, FedRAMP) not detailed

Is Predibase Secure and Compliant?

Enterprise-Grade SecurityBacked by Rubrik's security expertise with integration into enterprise security infrastructure
Private VPC DeploymentOption to deploy Predibase within customer's virtual private cloud for data sovereignty and maximum control
Robust Access ControlsBuilt-in access control mechanisms for secure governance of AI model serving
Data GovernanceQuality monitoring and governance capabilities for secure and compliant use of AI
Audit Logging and MonitoringComprehensive monitoring dashboards and performance tracking for operational visibility
Secure Agentic AI StackDesigned to work with governed data lakes and support secure AI agents according to Rubrik integration
Infrastructure RedundancyMulti-region deployment capability with guaranteed GPU capacity for mission-critical applications

What Customer Support Options Does Predibase Offer?

Channels
Available via contact form on predibase.comComprehensive docs at docs.predibase.comCommunity support for open-source frameworks like LoRAX
Hours
Business hours
Response Time
Not publicly specified; enterprise likely has priority
Satisfaction
Not available from public reviews
Specialized
Dedicated support for VPC deployments and enterprise customers
Business Tier
Priority support for Fortune 500 customers with VPC options
Support Limitations
•No 24/7 phone or live chat mentioned
•Community support primary for free tier/trial users
•Enterprise support details require sales contact

What APIs and Integrations Does Predibase Support?

API Type
REST API via Python SDK and UI endpoints
Authentication
API keys for SaaS; VPC supports custom auth
Webhooks
Not mentioned; event-driven serving via LoRAX
SDKs
Official Python SDK for fine-tuning, serving, and prompting
Documentation
Comprehensive at docs.predibase.com with UI and SDK guides
Sandbox
2-week free trial with full platform access including LudwigGPT
SLA
Guaranteed GPU capacity, 99.9%+ uptime, multi-region high availability
Rate Limits
Autoscaling handles hundreds of requests per second
Use Cases
Fine-tuning LLMs/SLMs, serving multi-LoRA models, real-time inference

What Are Common Questions About Predibase?

Predibase is a developer platform to fine-tune and serve open-source LLMs and SLMs. Predibase utilizes technology such as LoRA, LoRAX, and Turbo LoRA to produce production-ready models at reduced costs of up to 80%.

Users utilize simple YAML configurations or the UI/Python SDK to fine-tune models through supervised fine-tuning, reinforcement fine-tuning, or continued pretraining. Predibase efficiently processes fine-tuning tasks through parameter-efficient methods such as LoRA and RFT.

Costs associated with fine-tuning are based upon tokens processed; costs associated with inference are billed per GPU-second in the pay-as-you-go model. Costs will vary based on model size with significant savings from serving multiple models utilizing multi-LoRA.

Yes, enterprise-grade security with SOC 2 compliance through Rubrik. Data remains within your cloud via VPC deployments. Users have full control over their models and private serverless endpoints.

Yes. Predibase offers VPC (Virtual Private Cloud) deployments directly into your cloud environment with total control over your data. Predibase also offers SaaS (Software as a Service) private-serverless endpoints, both of which offer auto-scaling and SLAs (Service Level Agreements).

Yes. Predibase offers a 2 week trial of the full platform, including fine-tuning, serving and LudwigGPT Copilot. The platform can be used as either a SaaS (Software as a Service) offering or as a VPC (Virtual Private Cloud) solution.

Predibase is best suited for companies that need to support financial services (i.e., Wells Fargo), customer service automation, fraud detection, marketing technology. Predibase is well-suited for real-time decision-making with customized SLMs (Specialized Language Models).

Predibase is particularly well-suited as an enterprise AI infrastructure for the fine-tuning and serving of customized LLMs/SLMs. Through the use of LoRAX and Turbo LoRA, Predibase provides customers with up to 3-4 times better efficiency than traditional LLM/SLM training. Additionally, through its VPC capabilities and Rubrik-based security, Predibase makes itself production-ready for organizations within highly-regulated industries. Predibase uses a pay-as-you-go pricing model; this allows customers to rapidly experiment, however, customers should continually monitor their cost to avoid excessive bills.

Is Predibase Worth It?

Predibase is specifically designed for use by AI/ML development teams that are fine-tuning domain-specific models.

Recommended For

  • Organizations requiring a VPC/Private Cloud deployment of their fine-tuned SLMs.
  • Customers who wish to optimize their GPU usage for inference.
  • Organizations that require real-time decision-making and/or have custom SLMs that cannot utilize general-purpose LLMs.
  • Financial Services and Real-Time AI Applications.

!
Use With Caution

  • Teams that are relatively new to the use of LoRA/PEFT methodologies – there will be a learning curve.
  • Small teams that do not have an abundance of ML experience – Predibase has a very user-friendly interface and is a good example of a "low-code" platform, however, it is not completely a "no-code" platform.
  • Budget-sensitive projects – because Predibase charges based on the number of GPU seconds consumed by a project, customers will find it difficult to estimate their costs and may incur unexpected expenses if they exceed their budgeted amount.

Not Recommended For

  • Simple Prompting Use Cases – General LLM API offerings are typically less expensive for simple prompting use cases.
  • Non-Technical Teams – Developing applications utilizing Predibase will generally require some level of developer/ML experience.
  • On-Premise Only Requirements – Predibase offers VPC (Virtual Private Cloud) solutions that are cloud focused and would not meet all on-premises-only requirements.
Expert's Conclusion

Predibase is the most suitable option for technical teams that develop production SLM (Specialized Language Model) applications where the need for high-efficiency inference and data control warrant the investment in infrastructure.

Best For
Organizations requiring a VPC/Private Cloud deployment of their fine-tuned SLMs.Customers who wish to optimize their GPU usage for inference.Organizations that require real-time decision-making and/or have custom SLMs that cannot utilize general-purpose LLMs.

What do expert reviews and research say about Predibase?

Key Findings

Predibase is a high-performance platform that offers superior levels of efficiency in terms of the number of fine-tuned models it can serve from single GPUs through its LoRAX/Turbo LoRA architecture. Also strong in enterprise adoption (Wells Fargo), as well as in terms of VPC flexibility and Rubrik security. It has focused exclusively on making production-ready highly-specialized SLMs for real-time enterprise applications such as fraud detection and customer service.

Data Quality

Good - detailed technical info from docs, Rubrik site, and press. Limited public data on pricing details, customer support SLAs, and exact revenue. No G2/Capterra ratings found.

Risk Factors

!
The GPU-based billing structure for predibase can be difficult to predict when using a pay-as-you-go model.
!
Predibase's success is dependent upon how rapidly the open source LoRA ecosystem evolves.
!
The sales motion for an enterprise will need to be followed for custom pricing to be implemented.
!
The recent acquisition of Rubrik may affect the direction of the company's product roadmap.
Last updated: February 2026

What Are the Best Alternatives to Predibase?

  • •
    Hugging Face Inference Endpoints: A popular choice for model hosting that allows users to easily auto-train their models for fine-tuning. Easier for new users to get started, however less efficient than multiloar serving, and does not allow for VPC options. Primarily used for experimenting with open source, and/or for small-scale projects. (huggingface.co)
  • •
    Replicate: Another serverless ML model hosting platform that charges based on seconds used per model run. While it is easier to deploy, it lacks LoRA specialization, and does not provide the ability to host models in a VPC. Best suited for rapid prototyping, though not ideal for deploying large-scale SLM fleets into production. (replicate.com)
  • •
    Together AI: Provides high-performance inference capabilities, with the added feature of fine-tuning. Very strong in terms of both speed and scale, but is a more general purpose platform, rather than one optimized for LoRAX efficiency. Best suited for organizations with raw LLM performance requirements. (together.ai)
  • •
    Fireworks AI: Provides ultra-fast inference capabilities, with the additional feature of fine-tuning. Has competitive pricing and speed, and is cloud only, which means it cannot offer VPC options. Can be a good alternative for non-regulated workloads. (fireworks.ai)
  • •
    Baseten: An ML deployment platform that provides pipeline-based fine-tuning options. Offers a full suite of enterprise features and includes built-in autoscaling; however, is also very complex. Will best meet the needs of organizations that are building custom ML pipelines. (baseten.co)

What Additional Information Is Available for Predibase?

Rubrik Acquisition

Recently acquired by Rubrik, providing organizations with access to enterprise-grade security and compliance. This enables organizations to build secure AI infrastructures for regulated industries, while still providing them with the technical independence they require.

Open-Source Leadership

The creators of LoRAX (multiloar serving) and Turbo LoRA. They continue to actively contribute to the PEFT ecosystem, allowing their efficiency innovation to be adopted by the community.

Customer Success

Utilized by Wells Fargo to create SLMs for regulated financial use cases. Predibase serves Fortune 500 companies, as well as start-ups that are developing SLMs to improve customer service, detect fraud, make recommendations, etc.

Deployment Flexibility

Full VPC, multi-region HA and guaranteed GPU allocation plus cold start performance as a Private Serverless SaaS.

Trial Program

14-day trial of all functionality including fine tune, serve, and LudwigGPT Copilot available in both our SaaS and your own VPC.

What Are Predibase's Model Training Compute?

A100 cluster
GPU Types
Up to 10x faster with new fine-tuning stack
Training Speed
Fully managed and serverless
Infrastructure
Flash Attention 2, optimized CUDA kernels, automatic batch size tuning
Optimization Techniques

What Finetuning Techniques Does Predibase Support?

LoRASupervised Fine-Tuning (SFT)Reinforcement Fine-Tuning (RFT)Continued PretrainingDirect Preference Optimization (DPO) (upcoming)

What Supported Models Does Predibase Offer?

Llama Family

Llama-3 (all variants) and Llama-2

Mistral

Mistral-7B

Qwen

Qwen2.5-Coder-32B-instruct

Open Source LLMs

Extensive support for open source models via adapters

What Is Predibase's Training Pricing?

Fine-Tuning Pricing
Token-based pricing (varies by model size and technique)
Inference Pricing
Pay-as-you-go by GPU second
Infrastructure Costs
Dramatically reduced via LoRAX multi-model serving
Serverless Endpoints
Production-ready with industry-grade SLAs

What Training Features Does Predibase Offer?

10x Faster Training

Fine-Tune Stack using an A100 Cluster

Automatic Optimization

Batch Size Tuning, Flash Attention 2, Optimized Kernels

Customizable Parameters

Epochs, Rank, Learning Rate Adjustable

Adapters as First-Class Citizens

Native Adapter Management and Versioning

End-to-End Pipeline

SFT Warm-Up + RFT Refinement to Deployment

How Do You Deploy Models with Predibase?

Serving Engine
Production-ready private serverless endpoints
LoRAX
Serve hundreds of fine-tuned models on single GPU
Turbo LoRA
Accelerated throughput for reasoning models
Deployment Monitoring
Built-in monitoring for deployed models
RFT Model Support
Native support for reinforcement fine-tuned models

How Does Predibase Handle Data Management, Storage, and Governance?

Dataset Upload

Conversion from JSONL to CSV via API

Low-Data Training

10-100 labeled examples required for RFT

Centralized Storage

Manage Training Data in Single Platform

End-to-End Data Pipeline

From Dataset Upload to Deployed Model

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