Fine-tune and deploy open-source LLMs on your own infrastructure
Predibase is an LLM fine-tuning and serving platform for teams that need to customize and deploy open-source language models.
AI Panel Score
6 AI reviews
Reviewed
Users interact with Predibase through a web console or API to upload training data, select a base model, configure fine-tuning parameters, and launch training jobs without managing underlying GPU infrastructure. Once trained, models are deployed to a serverless or dedicated endpoint and queried via a REST API compatible with the OpenAI schema, making it straightforward to swap Predibase-hosted models into existing LLM-based applications.
The platform's core technical differentiator is its LoRAX serving engine, an open-source framework Predibase developed that enables hundreds of fine-tuned LoRA adapters to be served concurrently on a single set of GPUs. This multi-adapter serving architecture avoids the cost of provisioning separate GPU instances per fine-tuned model. Predibase also offers prompt optimization tooling and supports quantized model variants to further reduce inference cost and latency.
Predibase targets ML engineering teams and AI product teams at companies that want task-specific model performance without paying for frontier API pricing. Pricing is usage-based, billed by compute consumed during training and inference. It competes with services like Together AI, Fireworks AI, Modal, and cloud-provider fine-tuning offerings from AWS (Bedrock), Google (Vertex AI), and Azure.
The platform runs on Predibase-managed cloud infrastructure with options for dedicated deployments. It exposes a Python SDK and an OpenAI-compatible REST API. The LoRAX serving engine is available as open-source software for teams that prefer self-hosting.
Supports serving embedding models on the platform, enabling downstream applications such as retrieval-augmented generation (RAG), semantic search, text classification, and sentiment analysis.
Fine-tune open-source LLMs using LoRA or the proprietary Turbo LoRA adapter, which improves inference throughput by up to 3.5x for single requests compared to standard LoRA fine-tuning.
Fine-tune LLMs using reward functions via Group Relative Policy Optimization (GRPO), enabling high-accuracy model alignment with as few as 10–100 labeled examples instead of large labeled datasets.
Integrates with Weights & Biases and Comet to track fine-tuning progress, learning curves, and model metrics directly from the Predibase UI during training jobs.
Captures comprehensive logs of all prompts and model responses to enable performance monitoring, model behavior refinement, and a clear audit trail for transparency.
Enterprise customers can reserve dedicated GPU resources from Predibase's fleet of A100 and H100 GPUs to ensure burst capacity is always available for mission-critical applications.
Serve multiple fine-tuned LoRA adapters simultaneously from a single GPU deployment, eliminating the need for separate GPU instances per model and dramatically cutting infrastructure costs.
A purpose-built inference stack powered by Turbo LoRA, LoRAX, and FP8 quantization that serves fine-tuned small language models at 3–4x faster speeds than traditional methods while reducing GPU memory footprint by ~50%.
Deploy fine-tuned models on fully managed serverless infrastructure (SaaS) or within a customer's own Virtual Private Cloud (VPC) on AWS or Microsoft Azure for data sovereignty and compliance.
Provides three access modes — a no-code UI, a low-code Python SDK, and a CLI — allowing teams from novice engineers to expert data scientists to launch and manage fine-tuning jobs without complex ML infrastructure setup.
Supports multiple data connectors including file uploads (CSV, JSONL), Amazon S3, Snowflake, and Databricks for ingesting training datasets directly into the fine-tuning pipeline.
Deploys mission-critical workloads across multiple geographic regions with GPU autoscaling to maintain throughput SLAs and protect against regional outages.
For developers and researchers exploring the platform. Includes $25 in free credits valid for 30 days to test fine-tuning and inference capabilities.
Pay-as-you-go tier for developers and engineering teams building production LLM applications. Activated by adding a credit card. Usage billed by the second for GPU compute. Hardware costs start at $1.82/hour for an A10G 24GB GPU (suitable for models up to 7B parameters). Fine-tuning is billed per token processed.
For enterprises requiring dedicated infrastructure, guaranteed SLAs, and VPC deployment. Pricing requires contacting Predibase sales at sales@predibase.com. Supports deployment within customer's own AWS, GCP, or Azure cloud environment.
Acquired by Rubrik in June 2025 — the fine-tuning play just changed completely.
“Predibase was a credible LoRA fine-tuning platform with real technical differentiation. Rubrik acquired it in June 2025 and pivoted it to AI agent governance, which isn't the same product.”
The LoRAX multi-adapter serving engine was the real story here — hundreds of fine-tuned adapters on a single GPU set, versus paying Together AI or Fireworks AI for separate deployments at $1.82/hour per A10G. That's a legitimate cost wedge for teams running more than two or three fine-tuned variants in production.
But the website meta now reads 'Govern Every Agent. Trust Every Action.' That's not an LLM fine-tuning platform. Rubrik bought the team, repointed the product, and the roadmap isn't yours anymore. Vendor viability isn't the question — Rubrik's stable. Control over the product direction is.
If you need LoRA fine-tuning today, the open-source LoRAX engine still exists for self-hosting. For managed fine-tuning, evaluate Fireworks AI or Modal against your actual workload. Don't standardize on a product mid-pivot.
Turbo LoRA's 3.5x throughput claim is differentiated on paper, but Fireworks AI and Together AI haven't been standing still.
Rubrik is a known enterprise vendor — the acquisition doesn't look sketchy, but betting on a mid-pivot product is a harder board conversation.
OpenAI-compatible REST API plus no-code UI means engineering teams can swap in fine-tuned models without rebuilding integrations.
LoRAX multi-adapter serving genuinely reduces GPU costs versus competitors, but the product roadmap no longer centers on fine-tuning.
Rubrik acquisition in June 2025 means stable parent but product direction has visibly pivoted away from LLM fine-tuning.
Teams already deep in LoRA fine-tuning workflows who need VPC deployment and can accept roadmap uncertainty.
You're evaluating this as a long-term managed fine-tuning platform — the pivot makes that a shaky foundation.
LoRAX multi-adapter serving is genuinely clever infrastructure that cuts real GPU spend.
“Predibase built a technically differentiated fine-tuning and serving stack around LoRAX — open-source, verifiable, and meaningfully cheaper than spinning dedicated GPU instances per adapter. The June 2025 Rubrik acquisition muddies the 3-year roadmap in ways that matter for platform bets.”
The LoRAX engine — serving hundreds of LoRA adapters off a shared GPU pool at $1.82/hour on an A10G — solves a real infrastructure problem most teams hit the moment they need more than two fine-tuned variants in production. Turbo LoRA's claimed 3.5x throughput improvement and the Predibase Inference Engine's ~50% GPU memory reduction are specific enough to be testable claims, not marketing copy. Reinforcement Fine-Tuning via GRPO with 10–100 labeled examples is the kind of alignment tooling that used to require a dedicated research engineer.
The tradeoff: the Rubrik acquisition repositions Predibase toward AI agent governance, not LLM fine-tuning depth. If the roadmap shifts there permanently, the fine-tuning surface freezes while Together AI and Fireworks AI keep iterating. VPC deployment on AWS and Azure clears enterprise data-sovereignty requirements, but no GCP fine-tuning path is documented despite GCP appearing in Enterprise plan copy.
For a team already running Snowflake or Databricks, the native data connectors mean the training pipeline wires up cleanly. W&B and Comet integration for experiment tracking is the right call — no proprietary lock on observability.
LoRAX differentiates from Together AI and Fireworks AI on multi-adapter cost efficiency, but cloud-provider fine-tuning from AWS Bedrock and Vertex AI has distribution advantages Predibase can't match post-acquisition.
No-code UI plus Python SDK plus CLI covers the full team spectrum from data scientists to MLEs; W&B/Comet integration respects existing experiment tracking workflows.
OpenAI-compatible REST API, Snowflake and Databricks connectors, and S3 ingestion mean this slots into a standard ML stack without re-plumbing.
The Rubrik acquisition in June 2025 introduces real strategic uncertainty — fine-tuning roadmap continuity is unconfirmed, which is a meaningful 3-year risk.
LoRAX as open-source infrastructure plus Turbo LoRA and GRPO-based RFT shows genuine ML engineering depth beyond a thin wrapper around Hugging Face.
ML engineering teams that need to run multiple task-specific fine-tuned adapters in production without paying for separate GPU instances per model.
Your team needs a stable 3-year platform commitment — the Rubrik acquisition makes that a harder promise to evaluate right now.
$1.82/hour A10G entry point, but year-3 GPU spend needs a model
“Predibase's LoRAX multi-adapter serving is a real cost lever versus per-model GPU hosting. The acquisition by Rubrik in June 2025 introduces product-direction risk procurement should price in.”
Developer tier starts at $1.82/hour on an A10G. Usage-billed-by-the-second is clean. No seat tax, no SSO surcharge. The $25 free trial credit is honest scoping — 30 days, real infrastructure. Three tiers visible without a sales call. Procurement won't fight the onboarding.
TCO math is the hard part. A team running 1 dedicated A10G continuously: $1.82 × 730 hours × 12 = ~$16K/year. H100 dedicated deployment will run materially higher — no published rate. Add 20-30% for training token costs. Year 3 with model sprawl and adapter growth lands unknown without a usage audit. Compare Together AI or Fireworks AI: both publish inference rates per million tokens, making TCO modeling more predictable.
The Rubrik acquisition is the real contract risk. Product roadmap shifted to agent governance. Fine-tuning depth may erode. Enterprise VPC pricing requires a sales call — standard, but negotiation leverage is unclear post-acquisition. No auto-renewal terms published. Ask before signing anything annual.
Usage-billed-by-the-second with credit card activation is low friction; Enterprise VPC requires sales engagement but SOC-2 compliance is documented.
No auto-renewal or termination terms published; post-acquisition by Rubrik adds roadmap and continuity risk to any multi-year commitment.
Developer tier rates are public ($1.82/hour A10G); H100 and Enterprise VPC pricing require sales contact.
LoRAX multi-adapter serving offers a concrete, measurable cost reduction versus per-model GPU hosting — the ROI story is mechanically defensible.
Per-second billing is predictable at small scale but no published H100 rate or training token rate makes year-3 modeling unreliable.
ML engineering teams running multiple fine-tuned task-specific models who need shared GPU economics without per-model provisioning overhead.
You need locked-in multi-year pricing certainty — the post-acquisition roadmap and unpublished H100 rates make long-term TCO modeling unreliable.
LoRAX multi-adapter serving is genuinely clever; acquisition uncertainty is real
“Predibase's LoRAX engine solves a real GPU cost problem — serving hundreds of LoRA adapters on one GPU fleet instead of provisioning separate instances per model. The Rubrik acquisition in June 2025 shifts the roadmap toward agent governance, which is worth watching if you're building fine-tuning workflows today.”
The technical architecture here isn't marketing fluff. LoRAX multi-adapter serving on shared A100/H100 capacity, Turbo LoRA claiming 3.5x throughput improvement, FP8 quantization cutting GPU memory by ~50% — these are real engineering decisions that show up in your monthly compute bill. At $1.82/hour for an A10G, the pricing is legible. OpenAI-compatible REST API means dropping Predibase into an existing LLM pipeline is a morning's work, not a sprint.
The W&B and Comet integrations for experiment tracking are the right call — no ML team wants a walled-off metrics dashboard. Snowflake and Databricks connectors for training data ingest reduce the CSV-upload-and-pray workflow. RFT via GRPO with 10–100 labeled examples is a legitimately useful addition for alignment work that Together AI and Fireworks AI don't surface as cleanly.
The acquisition flag is the honest concern. The meta description now reads 'Govern Every Agent' — that's Rubrik's product direction, not fine-tuning infrastructure. No changelog, no blog in the scraped evidence. If the fine-tuning roadmap quietly stalls while the parent company pivots, you're mid-workflow on a deprioritized product.
OpenAI-compatible API and Python SDK lower the integration ceiling, but no changelog in public evidence makes it hard to know what's being actively maintained post-acquisition.
Docs confirmed present, but no blog or changelog in evidence suggests the written surface is functional rather than deep — category norm is richer practitioner content from competitors like Modal.
No-code UI plus SDK plus CLI covers the access modes; no free plan beyond $25 trial credits means every experiment after day 30 is billed compute, which adds mental overhead.
RFT via GRPO, Turbo LoRA, FP8 quantization, and VPC deployment on AWS/Azure give advanced users real levers beyond the basic fine-tuning happy path.
W&B/Comet integration, Databricks/Snowflake connectors, and OpenAI schema compatibility fit naturally into standard ML engineering stacks without forcing new habits.
ML engineering teams that need cost-efficient multi-adapter LoRA serving on shared GPU infrastructure without managing their own LoRAX deployment.
Your team needs a fine-tuning platform with a clearly committed long-term roadmap — the acquisition trajectory makes that bet risky today.
LoRAX is genuinely clever engineering; the Rubrik acquisition is a real question mark.
“Predibase solves a real, expensive problem — running multiple fine-tuned models without separate GPU bills. The acquisition pivot toward agent governance makes the product's future direction murky.”
The core idea here is sharp. LoRAX multi-adapter serving lets you run hundreds of fine-tuned adapters off one set of GPUs instead of spinning up separate instances per model. At $1.82/hour for an A10G, that math can get very attractive very fast compared to Together AI or Fireworks AI, especially if you're managing more than two or three task-specific adapters. Turbo LoRA claiming 3.5x throughput improvement for single requests is the kind of number that makes an ML engineer actually read the docs.
The no-code UI plus Python SDK plus CLI stack is sensible. $25 free trial credits to kick the tires is reasonable, not generous. Web-only platform means mobile is basically irrelevant here — this isn't a tool you're checking on your phone, and nobody pretends otherwise.
The Rubrik acquisition in June 2025 is the thing I'd want answered before committing. The meta description now says 'govern every agent action' — that's a different product than fine-tuning LLMs. The changelog shows nothing. Whether LoRAX roadmap continues or gets quietly deprioritized is genuinely unknown, and that uncertainty is real.
No-code UI with W&B and Comet integration suggests care in the ML workflow, but no blog or changelog makes it hard to gauge ongoing polish investment.
LoRA concepts have a learning curve, but the docs-available platform plus OpenAI-compatible REST API means existing LLM app builders can slot this in without rewriting much.
Web-only platform — but this is GPU infrastructure tooling, not a daily driver app, so the low score is context, not really a complaint.
$25 in credits for 30 days plus three access modes (UI, SDK, CLI) means different skill levels can find their entry point without much friction.
Multi-region high availability, autoscaling, scale-to-zero, and SOC-2 on enterprise tier signal serious infrastructure thinking, not a side project.
ML engineering teams running multiple task-specific fine-tuned models who want to stop paying for a separate GPU per adapter.
You need long-term roadmap certainty before committing — the acquisition pivot makes that a legitimate concern right now.
Acquired by Rubrik in June 2025. The fine-tuning product you're reviewing may not exist.
“Predibase got acquired by Rubrik and pivoted to AI agent governance. The LLM fine-tuning platform described in the docs may be discontinued or redirected. Caveat everything below.”
The meta description says 'Govern Every Agent. Trust Every Action.' The product brief says fine-tune Llama and Mistral. That's a different company. Rubrik acquired Predibase in June 2025 and shifted focus to agent ops and governance. Buying into the fine-tuning story right now is buying into a product mid-pivot.
The underlying tech is real. LoRAX is open-source and legitimately clever — hundreds of LoRA adapters on a single GPU is a genuine cost argument against hosting separate models on Together AI or Fireworks AI. $1.82/hour for an A10G isn't outrageous. Turbo LoRA's claimed 3.5x throughput improvement is specific enough to be checkable.
But no changelog, no blog, marketing copy that doesn't match the product page — three tells that something changed recently. Exit portability is actually decent: LoRAX is open-source, the API is OpenAI-compatible, adapters are portable. You're not trapped. You're just buying into uncertainty.
Multi-adapter serving on shared GPUs is a real gap vs. Together AI and Fireworks AI; the cost argument holds if the platform stays active.
LoRAX is open-source, the REST API is OpenAI-compatible, and adapters are portable — migration path is cleaner than most ML platform vendors.
Acquired June 2025, pivoted to agent governance, no changelog, no blog — the fine-tuning roadmap is unconfirmed and the org is mid-transition.
Meta description and product description describe two different companies — the pivot to agent governance post-Rubrik acquisition isn't reconciled anywhere visible.
LoRAX is a real open-source differentiator, but mid-acquisition pivots killed Codeium's original roadmap and numerous MLOps vendors before them — the pattern is familiar.
ML teams who want LoRAX's multi-adapter architecture and can tolerate buying into a vendor mid-acquisition.
You need a stable, actively-developed fine-tuning platform with a clear 12-month roadmap.
Common questions answered by our AI research team
Predibase supports fine-tuning on open-source LLMs including Llama, Mistral, and others.
LoRA-based fine-tuning uses lightweight adapters instead of full model copies, allowing multiple custom models to share the same base model and GPU resources rather than requiring dedicated GPUs per model.
Yes, multiple custom LoRA adapters can run simultaneously on the same base model, enabling efficient multi-tenant serving without separate model deployments.
Yes, Predibase uses a shared infrastructure model for serving fine-tuned LLMs, allowing teams to serve custom models without provisioning isolated infrastructure per model.
Hosting fully separate fine-tuned models requires dedicated GPU resources for each, while Predibase's shared infrastructure runs multiple LoRA adapters on one base model, reducing overall GPU costs.
