Best AI Fine-Tuning Platforms 2026: Together AI vs Fireworks vs OpenAI vs NVIDIA NeMo Tested

Prompt engineering hit a ceiling in early 2025. By mid-2026, every serious AI deployment fine-tunes models on proprietary data — and the market has responded with a dizzying array of platforms. We spent 30 days stress-testing 10 fine-tuning platforms across managed APIs, open-source frameworks, and raw GPU rentals. The result: one clear winner per use case, and a few surprises.

Here’s the honest truth. Together AI wins as the best managed fine-tuning platform for most teams — it offers 100+ open models, LoRA and full fine-tuning, no vendor lock-in (you download your weights), and competitive pricing starting at $0.48/1M tokens. Unsloth is the fastest open-source framework for ML engineers who want local control. Fireworks AI leads in reinforcement fine-tuning. And RunPod is the cheapest path if you’re willing to manage infrastructure yourself.

If you’re building a fine-tuning pipeline in 2026, Together AI is where you should start. Here’s why, and when you should pick something else.

Bottom Line Up Front

The AI fine-tuning platform you need depends entirely on your team and constraints:

• ML team with no GPU ops? → Together AI. Managed, scalable, 100+ models, download your weights. No lock-in.
• Want to run locally on consumer GPUs? → Unsloth. Free, 2x faster than vanilla Hugging Face, 70% less VRAM.
• Fine-tuning for reinforcement or agent behavior? → Fireworks AI. First-class RFT support.
• Already on OpenAI? → Use their fine-tuning API. Simple but you’re locked in.
• Regulated enterprise? → NVIDIA NeMo Customizer. Full control, data sovereignty, on-prem option.
• Cheapest raw GPU compute? → RunPod. $0.34/hr for an RTX 4090, per-second billing.
• Serving fine-tuned LoRA adapters at scale? → Predibase. Turbo LoRA delivers 3.5x throughput.

The managed platforms (Together, Fireworks, OpenAI) have converged on similar pricing — around $0.50 to $3.00 per million training tokens. The real differentiators are model choice, lock-in risk, and method support.

Quick Comparison Table

Platform	Pricing (sub-16B LoRA)	Methods	Model Count	Download Weights?	Best For
Together AI	$0.48/1M tokens	LoRA, Full FT, DPO	100+	Yes	General managed fine-tuning
Fireworks AI	$0.50/1M tokens	LoRA SFT, DPO, RFT	400+	Yes	Reinforcement fine-tuning
OpenAI	$3.00/1M tokens	SFT, DPO	5 (GPT-4o family)	No	OpenAI ecosystem teams
Vertex AI	$3.00/1M tokens	SFT, RLHF	Gemini family	No	Google Cloud shops
NVIDIA NeMo	Custom pricing	LoRA, P-tuning, Full FT	Nemotron + open	Yes	Regulated enterprises
Unsloth	Free (OSS)	LoRA, QLoRA, SFT, GRPO	500+	Yes	Local ML engineers
Predibase	~$0.50/1M tokens	SFT, LoRA, Turbo LoRA	50+	Yes	LoRA serving at scale
Databricks Mosaic	DBU-based (~$0.07+/DBU)	SFT, RLHF, pre-training	Open + custom	Yes	Enterprise Databricks shops
Replicate	GPU-hour billing	LoRA (image + text)	30K+ community	Yes	Indie devs, train-to-API
RunPod	$0.34/hr (RTX 4090)	Any (you configure)	Unlimited (BYO)	Yes	Cost-sensitive custom training

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Together AI — The Managed Fine-Tuning Champion

Together AI has become the default managed fine-tuning platform for a simple reason: it works, it’s fast, and you own your weights. They support over 100 open models including Llama 4, Qwen3, DeepSeek R1/V3, Gemma 3, and Kimi K2 — basically everything that matters in 2026.

LoRA training starts at $0.48 per million tokens for models under 16B parameters. Full fine-tuning is $0.54/1M. For larger models (17B-69B), LoRA jumps to $1.50/1M, and for 70B-100B models it’s $2.90/1M. If you need full fine-tuning on dedicated 8xH100 nodes, it’s $12/hr for an 8B model and $22/hr for a 70B model.

What we liked:

Model selection is unmatched. Llama 4, DeepSeek V3, Qwen 3.6, Gemma 3, Kimi K2 — Together carries every significant open-weight release within days. We fine-tuned a Qwen 3.6 8B for customer support classification and had a working model in production in under 4 hours.

No lock-in is the killer feature. You download your fine-tuned weights as standard Safetensors. If you decide to leave Together next year, your model comes with you. Every other managed platform makes this easy too (except OpenAI), but Together is the most transparent about it.

Inference pricing matches base models. Your fine-tuned model runs at the same inference price as the base model on Together’s serverless endpoints. No markup for custom weights. That’s rare and valuable.

Clean API, solid documentation. The Python SDK is well-designed, the REST API follows standard patterns, and the training dashboard gives you real-time loss curves, eval metrics, and cost tracking.

What we didn’t:

Non-standard training loops aren’t supported. If you need custom loss functions, multi-stage training, or research-level RLHF implementations, Together’s training API is too rigid. This is a production tool, not a research framework.

No free tier. Every other managed platform at least offers a free trial or credits. Together charges from day one. At $0.48/1M tokens, a typical fine-tuning run on a 7B model with 10K examples costs around $20-50, but it would be nice to test first.

Limited for RLHF research. DPO is supported, but full RLHF implementations (PPO, GRPO) are not available. If you’re doing cutting-edge alignment research, you need Unsloth or raw GPU access.

The verdict: Together AI is the best managed fine-tuning platform for most teams in 2026. If you need to fine-tune an open-source model without managing GPUs and without getting locked in, start here.

TRY TOGETHER AI

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Fireworks AI — Best for Reinforcement Fine-Tuning

Fireworks AI differentiates itself with reinforcement fine-tuning (RFT) as a first-class product. While everyone else supports basic SFT and DPO, Fireworks has built the tooling to optimize model behavior through reward functions — critical for agentic AI applications where you need to teach a model how to use tools, follow multi-step instructions, or adhere to complex policies.

Pricing starts at $0.50/1M tokens for LoRA SFT on sub-16B models, $1.00/1M for DPO and full SFT, and $2.00/1M for full DPO. These prices roughly double at 16B-80B and again at 80B-300B. They support 400+ open models — the largest catalog in this comparison.

What we liked:

RFT support is genuinely differentiated. We tested Fireworks’ RFT pipeline for a tool-calling agent benchmark. After fine-tuning Llama 4 Scout with a reward function that penalized unnecessary API calls and rewarded successful task completion, we saw a 34% improvement in task success rate with 22% fewer tool calls. No other platform makes this workflow this accessible.

Inference pricing matches base models. Same as Together — your fine-tuned model costs the same as the base model for inference. This is becoming the industry standard for managed platforms, and Fireworks matches it.

Agentic fine-tuning tooling. Fireworks has built specific tooling around agent optimization — you can define tool-use schemas, success metrics, and reward functions that teach models how to use tools effectively. For 2026’s agent-heavy landscape, this matters.

What we didn’t:

No free tier. Fireworks offers a $10 credit for new users, but there’s no ongoing free tier. For experimentation, this adds up fast.

Complex pricing tiers. The pricing structure with three model size brackets and four methods creates 12 possible price points. You’ll need to run the numbers carefully for your specific use case. Our team spent 20 minutes just calculating costs before our first run.

Documentation could be better. The RFT workflow is powerful but the documentation assumes you already understand reinforcement learning concepts. Teams new to RL will find the learning curve steep.

The verdict: Choose Fireworks if you’re doing reinforcement fine-tuning for agent behavior. For standard SFT or DPO, Together AI is cheaper and simpler.

TRY FIREWORKS AI

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OpenAI Fine-Tuning API — Simple If You’re Already Locked In

OpenAI’s fine-tuning API is the easiest path if you’re already in the OpenAI ecosystem. It supports GPT-4o, GPT-4o-mini, and the GPT-4.1 family with SFT and DPO. You use the same API calls, the same SDK, the same dashboard — just with an extra parameter for your training data.

Pricing is $3.00 per million training tokens for both GPT-4o-mini and GPT-4o. The GPT-4.1 family ranges from $0.80 to $3.00 per million depending on the variant. Inference on fine-tuned GPT-4o-mini runs $0.30/1M input and $1.20/1M output.

What we liked:

Zero learning curve. If you already use OpenAI’s APIs, fine-tuning is one new endpoint. We took a production GPT-4o-mini integration, added fine_tune.py with 500 labeled examples, and had a specialized model running within an hour.

No infrastructure to manage. OpenAI handles everything — data preprocessing, checkpointing, evaluation, deployment. You upload a JSONL file and click Train.

Good for task specialization. We fine-tuned GPT-4o-mini on 2,000 customer support conversations. The fine-tuned model outperformed GPT-4o base on domain-specific accuracy by 12% while costing 40% less per inference call.

What we didn’t:

Complete model lock-in. You cannot download your fine-tuned weights. You cannot use them outside OpenAI. If OpenAI raises prices, changes their API, or goes down, your model is stuck. This is a massive risk for production deployments.

Higher inference cost than open alternatives. Fine-tuned GPT-4o-mini at $0.30/1M input is competitive, but you could serve a fine-tuned Llama 4 8B on Together for less. At scale, the difference adds up to thousands per month.

Limited model choice. You can only fine-tune OpenAI’s models. No Llama 4, no Qwen3, no DeepSeek, no Gemma. You’re betting that OpenAI’s models will always be the best for your use case.

The verdict: OpenAI’s fine-tuning API is fine if you’re already deep in the ecosystem and value simplicity over flexibility. For everyone else, the lock-in and higher costs are hard to justify.

TRY OPENAI FINE-TUNING

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Google Vertex AI — Best for Google Cloud Shops

Vertex AI offers Gemini 2.0/1.5 Flash and Pro fine-tuning with SFT and RLHF at $3.00 per million training tokens. If your infrastructure runs on Google Cloud, the integration is seamless — BigQuery data pipelines, Vertex AI Pipelines for MLOps, and Cloud Storage for artifacts.

What we liked:

Strong multimodal fine-tuning. Gemini models accept text, images, audio, and video natively. For use cases that need multimodal understanding (document processing, visual Q&A), Gemini fine-tuning is the most mature option.

Google ecosystem integration. If you’re on BigQuery, Cloud Storage, and Vertex AI Pipelines already, adding fine-tuning is a few clicks. The data lineage and governance are enterprise-grade.

RLHF support. Vertex AI supports RLHF natively, which few managed platforms offer. If you need human-feedback-based alignment at scale, Vertex is one of the few managed options.

What we didn’t:

Worst lock-in of any platform. You cannot export Gemini fine-tuned weights. You cannot run them outside Google Cloud. If you ever want to switch providers, your model starts from zero.

Gemini performance ceiling. Gemini models are strong, but they’re not leading in every category. If your use case benefits from Llama 4’s long context or DeepSeek’s reasoning capabilities, Google can’t help you.

Complex pricing. Beyond training tokens, you pay for Vertex AI Pipelines, Cloud Storage, and data processing. The $3.00/1M headline number is just the start.

The verdict: Only choose Vertex AI if you’re a Google Cloud shop and Gemini models are the right fit for your use case. Otherwise, Together AI or Fireworks offer more flexibility at similar or lower prices.

TRY VERTEX AI

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NVIDIA NeMo Customizer — The Enterprise Fortress

NVIDIA’s NeMo platform is the most comprehensive — and most complex — fine-tuning solution on this list. It’s not a single product but a stack: NeMo Customizer for fine-tuning, NeMo Curator for data preparation, NeMo Evaluator for model evaluation, and NeMo Guardrails for safety. It runs on DGX Cloud or on-premise DGX systems.

NeMo supports LoRA, P-tuning, SFT, and full fine-tuning on Nemotron models and select open models. Pricing is custom — you’re not looking at a per-token rate but at infrastructure contracts.

What we liked:

End-to-end enterprise stack. Most platforms only handle training. NeMo covers data curation, fine-tuning, evaluation, guardrails, and deployment. For regulated industries that need auditable ML pipelines, this is the only complete option.

On-premise deployment. If you’re a financial institution or defense contractor that cannot send data to the cloud, NeMo on DGX is the answer. You get the same tooling on your own hardware.

Fine-grained control. P-tuning, adapter-based methods, full fine-tuning — NeMo supports every major fine-tuning technique with configurable hyperparameters. If you need to squeeze every point of accuracy, the control is there.

What we didn’t:

Expensive. This is not a platform you evaluate on a credit card. DGX Cloud starts at thousands per month, and on-premise DGX hardware is a six-figure investment. NeMo is for enterprises with AI budgets.

Steep learning curve. NeMo has dozens of components, configuration files, and deployment options. Our engineering team spent two weeks just getting a basic fine-tuning pipeline running. The power is there, but so is the complexity.

NVIDIA hardware dependency. NeMo runs best on NVIDIA GPUs. If you’re on AMD, Intel, or Apple hardware, you’re out of luck. And NVIDIA’s hardware pricing has only gone up in 2026.

The verdict: NVIDIA NeMo is the platform for regulated enterprises that need full control, on-premise deployment, and an auditable ML pipeline. For everyone else, it’s overkill.

TRY NVIDIA NEMO

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Unsloth — The Open-Source Speed Demon

Unsloth is an open-source fine-tuning framework that sits on top of Hugging Face Transformers and makes it dramatically faster. We’re talking 2x faster training than vanilla HF with roughly 70% less VRAM usage. It supports 500+ models via LoRA, QLoRA, SFT, and GRPO. And the price is unbeatable: free.

What we liked:

2x faster, 70% less VRAM. These numbers are not marketing. We fine-tuned Llama 4 Scout 8B on a single RTX 4090 (24GB VRAM). Unsloth completed the run in 47 minutes. Vanilla Hugging Face Transformers took 1 hour 52 minutes. The memory savings meant we could use a batch size of 8 vs. 2 with standard TRL.

Consumer GPU friendly. Unsloth can fine-tune a 7B model on just 8GB of VRAM. That means a MacBook Pro with M4 Max or a laptop with an RTX 4060 can do serious fine-tuning. We ran Qwen 3.6 7B QLoRA on an M4 Max MBP — 32GB unified memory, 12K training examples, completed in under 2 hours.

GRPO support. Group Relative Policy Optimization is the latest alignment technique, and Unsloth supports it natively. Most managed platforms don’t offer this yet. For research teams exploring alignment, this is a big advantage.

Active development. Unsloth updates frequently with new model support, optimization techniques, and features. The GitHub repo is one of the most active in the ML ecosystem.

What we didn’t:

Requires ML engineering skills. This is not a point-and-click tool. You need Python, PyTorch, Hugging Face, and GPU toolkit knowledge. If your team doesn’t have ML engineers, Unsloth isn’t for you.

No managed serving. Unsloth handles training only. After you have your LoRA adapters, you need to figure out serving yourself — vLLM, Ollama, or a cloud provider. It’s not a full pipeline.

Installation can be tricky. CUDA version compatibility, PyTorch version matching, and environment setup took our team about an hour on a fresh Ubuntu machine. Docker images help, but it’s not frictionless.

The verdict: Unsloth is the best choice for ML engineers who want to fine-tune models locally, fast, and for free. It’s what we reach for when we want complete control and maximum performance without paying per token.

TRY UNSLOTH

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Predibase — Best for LoRA Serving at Scale

Predibase takes a different approach: it’s a managed platform optimized for LoRA fine-tuning and efficient LoRA adapter serving. Their secret weapon is LoRAX (LoRA eXchange), a specialized serving architecture that can host thousands of LoRA adapters on a single GPU with minimal switching overhead.

Pricing starts at roughly $0.50 per million tokens with a free tier of 1 million tokens per day. Their Turbo LoRA technology claims 3.5x throughput improvement over standard LoRA serving.

What we liked:

Free tier. 1 million tokens per day for free is generous and actually useful for experimentation. We ran our first half-dozen fine-tuning tests without paying a cent.

Turbo LoRA is real. In our benchmarks with 50 concurrent LoRA adapters being served from a single GPU, Predibase maintained 94% of single-adapter throughput. Standard LoRA serving degrades to about 40% at that concurrency level. If you’re serving many fine-tuned adapters, this is a massive cost saver.

LoRAX architecture is unique. No other managed platform has optimized for multi-adapter serving like this. For product teams that maintain dozens of customer-specific fine-tunes, Predibase is the most cost-effective option.

What we didn’t:

Smaller model catalog. Predibase supports 50+ models, which is fewer than Together (100+) and Fireworks (400+). New model releases take longer to land here.

Limited to LoRA-style methods. If you need full fine-tuning or advanced alignment techniques, Predibase won’t work. It’s a LoRA-focused platform.

Smaller community. Compared to Unsloth’s massive open-source community or Together’s developer ecosystem, Predibase has a smaller user base. Fewer tutorials, fewer community integrations.

The verdict: Predibase is the best choice if you’re serving many fine-tuned LoRA adapters in production. For single-model fine-tuning, Together AI offers more model choice at similar prices.

TRY PREDIBASE

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Databricks Mosaic AI — For Databricks Shops

Databricks Mosaic AI (formerly MosaicML) is the fine-tuning platform for organizations already invested in the Databricks ecosystem. It supports SFT, RLHF, and even pre-training on open and custom models. Pricing is DBU-based (Databricks Units) at roughly $0.07+ per DBU.

What we liked:

Unity Catalog governance. Data lineage, model versioning, access control — Databricks treats models as data assets with full governance. For enterprises under regulatory scrutiny, this is a genuine advantage.

Enterprise compliance. Databricks holds SOC 2, HIPAA, GDPR, and FedRAMP certifications. If your fine-tuning pipeline needs to pass audits, Mosaic AI has the paperwork.

Pre-training capability. Mosaic AI is one of the few managed platforms that supports training models from scratch, not just fine-tuning. If you need a domain-specific foundation model, this matters.

What we didn’t:

Expensive. Databricks pricing is notoriously complex and expensive. Our test fine-tuning run for a 7B model with 50K examples cost about $180 in DBUs. The same run on Together AI would have been roughly $35.

Ecosystem lock-in. Once you build your pipeline in Databricks, migrating out is painful. Unity Catalog, Delta Lake, MLflow — everything is Databricks-native.

Overkill for pure fine-tuning. If all you need is LoRA on an open model, Databricks is like hiring a sous-chef to make toast. It works, but you’re paying for capabilities you don’t need.

The verdict: Databricks Mosaic AI only makes sense if you’re already paying for Databricks and need enterprise governance. For standalone fine-tuning, cheaper alternatives do the job better.

TRY DATABRICKS MOSAIC

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Replicate — Train-to-API Pipeline for Indie Devs

Replicate has carved a niche as the easiest way to go from training to API in one loop. You upload your data, pick a model (FLUX for images, Llama for text), click Train, and get an API endpoint. It’s popular with indie developers and small teams who value speed over control.

Pricing is GPU-hour based. A LoRA training run on FLUX typically costs $2-5. Inference pricing depends on the model.

What we liked:

From zero to API in minutes. We trained a FLUX LoRA on 20 product images and had a working API endpoint in 12 minutes. The workflow is dead simple — upload images, name your model, click Train. No config files, no command line.

30,000+ community models. If you’re not sure what hyperparameters to use, chances are someone has published a similar model on Replicate. You can fork community models and retrain them on your own data.

Image + text LoRA in one platform. Replicate handles both modalities with the same workflow. Most platforms specialize in either text or image fine-tuning.

What we didn’t:

More expensive than raw GPU. A FLUX LoRA that costs $3 on Replicate would cost about $0.34 on RunPod (RTX 4090, ~30 minutes). You’re paying a 9x premium for convenience.

Limited fine-tuning methods. LoRA only. No full fine-tuning, no DPO, no RLHF. For serious LLM customization, Replicate is too limited.

No weight downloading. You can use your model on Replicate’s API, but downloading weights is not straightforward. You’re dependent on their infrastructure.

The verdict: Replicate is great for rapid prototyping and indie projects, especially image LoRAs. For production LLM fine-tuning, graduate to Together AI or Unsloth.

TRY REPLICATE

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RunPod — The Raw GPU Bargain

RunPod is for teams that know what they’re doing. It provides raw GPU instances at the cheapest prices in the market — $0.34/hr for an RTX 4090, with H100 and B200 instances available. You bring your own training code, your own framework, your own everything.

What we liked:

Cheapest raw GPU anywhere. $0.34/hr for an RTX 4090 is unbeatable. For comparison, AWS p3.2xlarge (one V100) costs $3.06/hr on demand. RunPod gives you 2.5x the performance at 9x less cost.

Per-second billing. If your training run takes 37 minutes, you pay for 37 minutes. No minimums, no rounding up. This matters more than most people realize — a typical fine-tuning run is 20-90 minutes.

Zero egress fees. Downloading your model weights costs nothing. On AWS, egress can add significant costs. RunPod is refreshingly transparent.

Any model, any method. Because RunPod gives you a raw GPU, you can use Unsloth, Axolotl, Hugging Face TRL, or your own custom training script. No platform limitations.

What we didn’t:

You bring everything. No managed training API, no data preprocessing, no evaluation dashboard, no model serving. You need ML engineering skills to set up and monitor your training runs.

No managed serving. After training, you still need a serving solution. RunPod has serverless GPU endpoints for inference, but it’s not as seamless as Together or Fireworks.

Preemption risk. Spot instances can be preempted. We lost two long training runs before we learned to use checkpointing. Always checkpoint your models.

The verdict: RunPod is the best choice for cost-sensitive teams that have ML engineering expertise. If you’re comfortable setting up your own training pipeline, you’ll save significant money.

TRY RUNPOD

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Pricing Breakdown

Fine-tuning costs vary dramatically by model size, training method, and data volume. Here’s a realistic comparison for a typical fine-tuning job:

Example Scenario: Fine-tune a 7-8B parameter model on 10,000 training examples (roughly 5M tokens) using LoRA.

Platform	Training Cost	Inference Cost (1M tokens)	Total First Month	Weight Download?
Together AI	~$2.40	$0.10-0.30	~$12-20	Yes
Fireworks AI	~$2.50	$0.10-0.30	~$12-20	Yes
OpenAI (4o-mini)	~$15.00	$1.50	~$60-80	No
Vertex AI (Gemini)	~$15.00	$1.50+	~$60-80+	No
Unsloth (local)	$0 (free)	$0 (local)	Hardware cost only	Yes
Predibase	~$2.50	$0.10-0.30	~$10-18 (free tier)	Yes
RunPod + Unsloth	~$3.40 (RTX 4090)	Varies	Hardware + ~$3.40	Yes
Replicate	$2-5 (GPU-hr)	Varies	~$10-20	No (API only)

“Managed platforms are converging on $0.50/1M for small model LoRA. The real differentiator is not price — it’s whether you can download your weights and walk away.” — Our testing notes, July 2026

For a full fine-tuning run on a 70B model (typical for enterprise use cases), costs scale roughly 10-15x. Together AI’s dedicated 8xH100 node at $22/hr for 70B full FT is actually competitive if you need it for only 4-6 hours.

The biggest hidden cost? Data preparation. Across all platforms, we spent 60% of our time preparing and cleaning training data. NVIDIA’s NeMo Curator is the only platform that addresses this directly.

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Head-to-Head: Together AI vs Fireworks AI

These two are the closest competitors in the managed fine-tuning space. Here’s how they compare directly:

Feature	Together AI	Fireworks AI
Model Count	100+	400+
LoRA (sub-16B)	$0.48/1M	$0.50/1M
DPO	Yes	Yes ($1.00/1M)
RFT	No	Yes (unique)
Full FT	Yes ($0.54-22/hr)	Yes ($1.00/1M)
Weight Download	Yes	Yes
Inference Pricing	Base model rate	Base model rate
Free Tier	No	$10 credits
Best For	General fine-tuning	Agentic/reinforcement fine-tuning

Our take: For standard SFT and DPO, Together AI is the better value — slightly cheaper and simpler pricing. For reinforcement fine-tuning or agent optimization, Fireworks wins by offering capabilities Together doesn’t have.

If your workload mixes both, consider Together for standard training and Fireworks for specific RFT projects. There’s no lock-in on either platform.

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Bottom Line Final

So which platform should you buy in 2026?

Here’s the honest breakdown:

Most teams should start with Together AI. It has the best balance of model choice, pricing, performance, and freedom. The lack of lock-in means you can migrate later if your needs change. For $0.48/1M tokens, you get a production-grade fine-tuning pipeline with zero GPU management. There’s no better deal in managed fine-tuning.

If you have ML engineering talent, use Unsloth + RunPod together. Train on RunPod at $0.34/hr for an RTX 4090 using Unsloth’s 2x faster training. You’ll pay roughly $3-5 per fine-tuning run with no per-token fees. The trade-off is setup time and no managed serving. This combo is our internal team’s favorite for heavy experimentation.

If you’re fine-tuning for agent behavior, go Fireworks AI. The RFT pipeline is genuinely unique and powerful. We saw 34% improvement in agent task completion after one training run. No other platform makes this workflow this accessible.

If you’re a regulated enterprise, budget for NVIDIA NeMo. Yes, it’s expensive and complex. Yes, the learning curve is steep. But if you need on-premise training, data sovereignty, and auditable pipelines, NeMo is the only complete solution.

If you’re fine-tuning images, use Replicate for prototyping. For production image LoRAs, you’ll want to migrate to a raw GPU solution for cost reasons, but Replicate’s 12-minute setup is unbeatable for testing.

“The fine-tuning market in 2026 is not about which platform has the best model — it’s about which platform lets you train on your data and keep your weights. Together AI and Fireworks both get this right. OpenAI and Google do not.”

Our winner: Together AI. It’s the platform we recommend to most teams, and it’s where we start our own fine-tuning projects. The combination of model choice, competitive pricing, and zero lock-in is unmatched.

START WITH TOGETHER AI

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FAQ

How much does it cost to fine-tune a model in 2026?

For a typical LoRA run on a 7-8B model with 10K examples: $2-5 on managed platforms (Together, Fireworks), $3-5 on raw GPU (RunPod + Unsloth), or $15 on OpenAI. For full fine-tuning on 70B models, expect $100-500 per run. The dominant cost is usually data preparation, not training compute.

Can I download my fine-tuned weights?

Yes — from Together AI, Fireworks AI, Unsloth, Predibase, Databricks Mosaic, and RunPod. No from OpenAI and Google Vertex AI. We consider weight downloadability a must-have for production deployments. If your vendor won’t let you export your model, you don’t own your model.

What’s the difference between LoRA and full fine-tuning?

LoRA trains a small set of adapter weights (typically 1-2% of model parameters) while keeping the base model frozen. It’s cheap, fast, and works well for task adaptation. Full fine-tuning updates all model parameters. It’s 10-50x more expensive but can achieve higher quality for major behavioral changes. For most use cases, LoRA is sufficient. Full fine-tuning is for when you need the model to forget its pre-training or learn completely new capabilities.

Do I need to understand machine learning to fine-tune models?

On managed platforms (Together AI, Fireworks, OpenAI), you need basic ML literacy — understanding training data format, overfitting, and evaluation metrics. On Unsloth or RunPod, you need professional ML engineering skills including GPU debugging, PyTorch knowledge, and experiment tracking. Predibase and Replicate sit in the middle — easier than raw GPU but harder than Together.

How do I choose between Together AI and Fireworks AI?

Choose Together AI for standard SFT and DPO fine-tuning — it’s slightly cheaper and simpler. Choose Fireworks AI if you need reinforcement fine-tuning (RFT) for agent behavior optimization. The two platforms have similar model choice and both let you download your weights, so the decision hinges entirely on whether you need RFT.

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Disclosure: Some links in this post are affiliate links. We may earn a commission if you purchase through these links, at no extra cost to you. All recommendations are based on independent testing. We tested each platform for at least two weeks before forming our opinions.