Predibase
Managed fine-tuning and inference platform specializing in LoRA (Low-Rank Adaptation) fine-tuning of open-source LLMs. Predibase allows teams to fine-tune Llama, Mistral, Gemma, and other models using the LoRA/QLoRA technique with minimal GPU cost. Uses serverless LoRA serving — multiple fine-tuned adapters share the same base model weights, enabling cost-effective serving of many task-specific fine-tuned models without separate GPU allocations. Built on Ludwig (open-source ML training framework).
Score Breakdown
⚙ Agent Friendliness
🔒 Security
SOC2 certified. HTTPS enforced. Training data stored in Predibase's secure cloud — evaluate before uploading sensitive datasets. API key with no scope granularity. Built on Ludwig (Apache 2.0 open source) for training transparency.
⚡ Reliability
Best When
You have a specific, well-defined agent task with training data and want to fine-tune an open-source model for production use with serverless serving of multiple task-specific variants.
Avoid When
Your task requires frontier model capabilities or you don't have quality training data — fine-tuning without good data produces worse results than few-shot prompting.
Use Cases
- • Fine-tune open-source LLMs on your agent's specific task (SQL generation, code review, classification) using LoRA with small training datasets
- • Serve multiple fine-tuned agent variants cost-effectively using Predibase's serverless LoRA adapter architecture
- • Reduce fine-tuning cost using QLoRA — fine-tune 70B models on a single A100 via 4-bit quantization
- • Evaluate fine-tuned model quality with Predibase's built-in evaluation and compare against base model and GPT-4 baselines
- • Build specialized agent models for structured output generation where fine-tuned small models outperform prompt-engineered frontier models
Not For
- • Teams without labeled training data — fine-tuning requires at least 100-1000 high-quality examples for meaningful improvement
- • General-purpose agent tasks requiring broad knowledge — fine-tuning specializes models; use frontier models for broad capability
- • Real-time low-latency inference requiring < 200ms — serverless LoRA serving adds cold start overhead for infrequently used adapters
Interface
Authentication
API key for SDK and inference API. OpenAI-compatible inference endpoint uses Bearer token. Keys generated in Predibase dashboard. Single key grants access to all models and fine-tuning jobs.
Pricing
Competitive pricing for fine-tuning and inference vs raw GPU clouds. LoRA adapter serving is very cost-effective for multiple specialized models. Credit card required after free tier.
Agent Metadata
Known Gotchas
- ⚠ LoRA fine-tuning requires understanding of LoRA rank, alpha, and dropout hyperparameters — wrong settings produce poor fine-tuned models
- ⚠ Training data must be in specific JSONL format with 'prompt' and 'completion' fields — data format errors cause silent training failures
- ⚠ Fine-tuning is async and takes 30 minutes to several hours — agents must poll job status before serving from fine-tuned adapter
- ⚠ Serverless LoRA serving has cold start for infrequently used adapters — first request to inactive adapter can take 10-30 seconds
- ⚠ Base model selection is fixed after fine-tuning — can't switch base models without new fine-tuning job
- ⚠ Training data is uploaded to Predibase infrastructure — consider data sensitivity before uploading proprietary datasets
Alternatives
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Scores are editorial opinions as of 2026-03-07.