Why it matters
- Shared LoRA serving architecture dramatically reduces the cost of deploying multiple fine-tuned models vs. full model serving.
- No MLOps required — upload data and train without managing GPUs, CUDA environments, or serving infrastructure.
- Built on Ludwig, Uber's open-source ML framework — strong technical foundation with configurable training.
- Serverless auto-scaling means zero cost when your fine-tuned model isn't receiving requests.
Key capabilities
- Managed fine-tuning: Upload JSONL training data; select base model; Predibase handles GPU training.
- LoRA/QLoRA support: Parameter-efficient fine-tuning for Llama 3, Mistral, Mixtral, Gemma, Phi, and more.
- Shared LoRA serving: Multiple fine-tuned adapters share a base model — cost-efficient multi-model serving.
- Serverless API: Auto-scaling endpoints that scale to zero when idle.
- Ludwig integration: Advanced training configuration via Ludwig's declarative ML config format.
- Evaluation: Built-in evaluation metrics on held-out test sets during training.
- Model comparison: Compare fine-tuned vs. base model performance side-by-side.
- SDK: Python and REST API for programmatic training and inference.
Technical notes
- Framework: Ludwig (open source; Uber-originated)
- Base models: Llama 3, Mistral, Mixtral, Gemma, Phi-3, and others
- Fine-tuning: LoRA, QLoRA parameter-efficient training
- Serving: Serverless; shared-base LoRA adapter architecture
- Data format: JSONL instruction-response pairs
- Pricing: Free tier; Starter ~$99/mo; Enterprise custom
- Company: Predibase; San Francisco; founded 2021 by Ludwig ML creators; raised $19.5M
Ideal for
- Teams who need domain-specific fine-tuned models but lack ML infrastructure expertise.
- Organizations deploying multiple specialized LLM adapters (one per use case/department) cost-efficiently.
- Product teams who want fine-tuning as a managed service without building training pipelines.
Not ideal for
- Teams with existing GPU infrastructure who want full control — Unsloth + RunPod is cheaper.
- Very large model fine-tuning (70B+ full fine-tune) — Predibase focuses on LoRA/QLoRA.
- Real-time low-latency requirements — serverless cold starts add latency for infrequent usage patterns.