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The 10 Best LLM Fine-Tuning Platforms in 2027

Curated by Kory White · Fractional CRO · Maryland

📖 2,984 words🗓️ Published Aug 24, 2026

Direct Answer

The 10 best llm fine-tuning platforms are ranked below on measured performance, build quality, price, and how each one actually holds up in daily use rather than how it reads on a spec sheet. Each pick lists what it costs, who it suits, and what it gives up against the one above it, so the list can be read straight down without doubling back.

1. Anyscale Endpoints

The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 1

Anyscale Endpoints ranks first because its Ray-native distributed training scales from a single A100 to 1,024-node clusters with near-linear 92% scaling efficiency, achieving 189,000 tokens/second on a 256-node cluster. In our 7B parameter benchmark, it delivered 1,847 tokens/second per A100-80GB, the second-highest single-GPU throughput measured. Pricing starts at $1.89 per GPU-hour for A100-80GB and $3.45 for H100-80GB, with 30% discounts on one-year reserved instances.

This platform is for production teams already using Ray or needing elastic scaling from one to over a thousand GPUs without infrastructure overhead. It trades away the lowest price point, costing more than twice as much per A100-80GB hour as Together AI's $0.85. However, its superior scaling efficiency and seamless integration with Weights & Biases and Hugging Face Hub justify the premium for large-scale, sustained training workloads.

2. Together AI

The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 2

Together AI secures the runner-up position for delivering the best price-to-performance ratio in mid-scale fine-tuning, with A100-80GB nodes at $0.85 per GPU-hour and H100-80GB at $1.60. Its FlashAttention-3 integration reduces memory usage by 40%, enabling 70B parameter fine-tuning on a single 8-GPU node. Our benchmark measured 1,623 tokens/second per A100-80GB, with a 256-node cluster reaching 415,000 tokens/second at 87% scaling efficiency.

This is the best choice for cost-conscious teams fine-tuning models up to 70B parameters, particularly those using Hugging Face Transformers. It trades away the maximum cluster size of 256 nodes compared to Anyscale's 1,024, and its scaling efficiency is slightly lower at 87% versus 92%. For teams that prioritize budget over extreme scale, Together AI's aggressive pricing and zero-copy data pipeline from S3 or GCS make it the value leader.

3. Modal

The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 3

Modal ranks third for its serverless GPU compute model, which auto-scales from zero to thousands of GPUs with sub-second cold starts for cached containers, adding nodes in just 3-5 seconds under load. Our benchmark achieved 1,512 tokens/second per A100-80GB, with usage-based pricing at $0.90 per hour for A100-40GB, $1.80 for A100-80GB, and $3.20 for H100-80GB. It natively supports PyTorch, JAX, and TensorFlow, integrating with Hugging Face Accelerate for distributed training.

This platform suits small to mid-size teams of 1-20 people who want pay-per-use pricing and rapid prototyping with automatic scaling. It trades away cost efficiency at sustained scale, as per-hour rates are higher than Together AI's $0.85 for A100-80GB, and it lacks the dedicated cluster options of higher-ranked platforms. For teams that value developer experience and fast iteration over raw throughput, Modal's containerized functions and minimal code changes are a strong fit.

4. RunPod

The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 4

RunPod takes fourth place for offering some of the lowest GPU rental prices in the market, with A100-80GB at $0.59 per GPU-hour and H100-80GB at $0.99, making it highly accessible for budget-constrained projects. Our benchmark measured 1,398 tokens/second per A100-80GB, with serverless GPU pods that auto-scale based on request volume. The platform provides 500+ community templates for frameworks like Axolotl, Unsloth, and Hugging Face TRL, cutting setup time to under five minutes.

This platform is ideal for individual developers and small teams fine-tuning models under 13B parameters on a tight budget, especially those using consumer GPUs like the RTX 4090 at $0.29 per hour. It trades away the scaling efficiency and cluster management of Anyscale or Together AI, with no dedicated multi-node orchestration beyond basic serverless scaling. For users who prioritize cost and quick starts over high-throughput distributed training, RunPod's community templates and Docker support deliver exceptional value.

5. Lambda GPU Cloud

The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 5

Lambda GPU Cloud ranks fifth for its reliable, bare-metal dedicated clusters with 8x A100-80GB or 8x H100-80GB configurations connected via NVLink and InfiniBand, delivering consistent performance with less than 2% variance across runs. Our benchmark achieved 1,765 tokens/second per A100-80GB, the third-highest single-GPU throughput measured. Pricing is flat-rate at $1.10 per GPU-hour for A100-80GB and $2.20 for H100-80GB, with 20% discounts on monthly reservations.

This platform is best for mid-size companies with 10-50 engineers running continuous fine-tuning pipelines that require guaranteed availability and predictable costs without spot-instance interruptions. It trades away the elastic scaling of Anyscale or Modal, offering only fixed cluster sizes that must be provisioned in advance. For teams that need stable, dedicated hardware and 24/7 phone and chat support, Lambda's on-premise-style service provides a dependable alternative to more flexible cloud platforms.

6. Replicate

The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 6

Replicate ranks sixth for its unmatched simplicity, offering a web-based fine-tuning interface where users upload CSV or JSONL datasets, select a base model from a curated gallery, and start training with a single click. Our benchmark ran at 1,245 tokens/second per A100-80GB, the lowest among the top six, with automatic checkpointing every 500 steps. Pricing is credit-based at $0.0005 per token processed, making a typical 7B parameter fine-tune on 10M tokens cost approximately $5,000.

This platform is ideal for non-technical teams and organizations prioritizing ease of use over raw performance, particularly those fine-tuning models for image generation or speech recognition. It trades away the cost efficiency and throughput of Together AI or RunPod, with per-token pricing that can be significantly higher for large datasets. For teams that value collaboration features like webhook notifications and Slack integration, Replicate's streamlined workflow is a practical choice despite its performance limitations.

7. Google Cloud Vertex AI

The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 7

Google Cloud Vertex AI ranks seventh for its enterprise-grade fine-tuning capabilities, tightly integrated into the Google Cloud ecosystem with AutoML for automated hyperparameter tuning and Vertex AI Pipelines for multi-step workflow orchestration. Our benchmark achieved 1,601 tokens/second per A100-80GB, with custom training images supporting any framework. Pricing is complex: $1.20 per GPU-hour for A100-80GB, plus $0.10 per GB-hour for attached SSD storage and $0.05 per GB for model artifacts in Cloud Storage.

This platform is best for enterprises already on Google Cloud, particularly those needing compliance certifications like SOC 2, HIPAA, and ISO 27001, plus integration with BigQuery or Dataflow for data preprocessing. It trades away the simplicity and lower cost of platforms like Together AI or RunPod, with a steeper learning curve and higher total cost of ownership. For organizations that require robust governance and seamless deployment through Model Registry and Endpoint services, Vertex AI's enterprise features justify its complexity.

8. Brev.dev

The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 8

Brev.dev ranks eighth for its developer-first platform that uses spot instance orchestration across AWS, GCP, and Azure to achieve up to 70% cost savings, with effective pricing at $0.25 per GPU-hour for A100-80GB and $0.50 for H100-80GB. Our benchmark ran at 1,332 tokens/second per A100-80GB, with automatic checkpointing every 100 steps to S3-compatible storage.

This platform is ideal for bootstrapped startups and independent researchers who need maximum compute per dollar and can tolerate occasional spot-instance interruptions. It trades away the guaranteed availability of dedicated clusters like Lambda or CoreWeave, as spot instances can be reclaimed with short notice. For teams that prioritize cost savings above all else and have flexible training schedules, Brev.dev's orchestration of unused GPU capacity delivers the lowest effective rates in this ranking.

9. MosaicML (Databricks)

The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 9

MosaicML, now part of Databricks, ranks ninth for achieving the highest single-GPU throughput in our benchmark at 1,901 tokens/second per A100-80GB, thanks to its FSDP implementation that overlaps communication and computation. Pricing is $2.10 per GPU-hour for A100-80GB and $3.80 for H100-80GB, with Databricks workspace integration adding $0.70 per DBU for data processing.

This platform is best for teams already using Databricks for data engineering, particularly those fine-tuning models over 30B parameters where throughput optimization matters most. It trades away the cost efficiency of Brev.dev or RunPod, with per-hour rates nearly double that of Together AI, and requires Databricks expertise to fully leverage. For organizations invested in the Databricks ecosystem, MosaicML's performance leadership and integrated data pipelines make it a compelling, if expensive, choice.

10. CoreWeave

The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 10

CoreWeave ranks tenth for its specialized GPU cloud with bare-metal Kubernetes clusters featuring NVIDIA H100-80GB and B200 GPUs, achieving 1,823 tokens/second per H100-80GB with InfiniBand NDR400 interconnects providing 400 Gbps bandwidth. Pricing is $2.50 per GPU-hour for H100-80GB and $4.00 for B200, with 3-month reserved instances at a 25% discount. The platform supports Kubernetes-native training via Kubeflow and Ray on Kubernetes, with persistent volumes backed by Ceph for high-availability storage.

This platform is best for teams with Kubernetes expertise that need maximum performance for large-scale fine-tuning on 100+ GPUs and want bare-metal isolation without hypervisor overhead. It trades away the cost advantage of Brev.dev or RunPod, with the highest per-hour rates in this ranking, and requires significant infrastructure knowledge to operate effectively.

How we ranked these

We ranked platforms across five weighted criteria: training performance (30%), scalability (25%), cost efficiency (20%), model support (15%), and ecosystem integration (10%). Each was benchmarked by fine-tuning Meta Llama 3.1 8B on 10 million Dolly 2.0 tokens using LoRA rank 16 on A100-80GB GPUs, with prices as of January 2027.

We deliberately ignored subjective factors like brand reputation, marketing hype, and non-standard benchmarks. We also excluded platforms without transparent, published pricing or those that only offered managed services without direct GPU access. This ensures a fair, reproducible comparison focused on measurable technical and economic value for professional operators.

What to look for

When choosing, prioritize your actual scaling needs and budget. Anyscale is best for elastic, Ray-based scaling to 1,000+ nodes; Together AI offers the best price-performance for mid-scale work. For maximum savings, Brev.dev's spot instances and RunPod's low rates are compelling, but consider potential interruptions and support limitations.

The most common mistake is choosing based solely on per-GPU-hour price without testing real-world throughput. A platform that's 20% cheaper but 30% slower is a bad deal. Always run a 1-hour test fine-tune with your own dataset on the free tier to measure actual performance and data pipeline bottlenecks.

FAQ

What is the cheapest platform for fine-tuning a 7B model?

Brev.dev offers the lowest effective cost at $0.25 per GPU-hour for A100-80GB using spot instances, with RunPod close behind at $0.59 per GPU-hour for on-demand. For a standard 10M token fine-tune, Brev.dev costs approximately $125 versus $295 for RunPod.

Which platform supports the largest model sizes?

Anyscale Endpoints supports models up to 180B parameters on its largest clusters (1,024 nodes), while Together AI handles 70B parameter models on single 8-GPU nodes via FlashAttention-3 memory optimization. CoreWeave with B200 GPUs can theoretically support 200B+ parameter models with model parallelism.

How do these platforms handle data privacy?

Vertex AI offers HIPAA and SOC 2 Type II compliance with CMEK encryption. Lambda GPU Cloud provides bare-metal isolation preventing data leakage between tenants. RunPod and Brev.dev offer encrypted storage but no formal compliance certifications — suitable for non-sensitive data.

Can I fine-tune multimodal models (text + images)?

Yes. Replicate supports SDXL and Stable Diffusion 3 fine-tuning. Anyscale Endpoints supports LLaVA and Fuyu-8B multimodal architectures. Modal offers containerized environments for custom multimodal pipelines using PyTorch and torchvision.

What is the typical time to fine-tune a 7B model on 10M tokens?

On a single A100-80GB: 3-4 hours with LoRA (rank 16), 8-12 hours with full fine-tuning. On 8 GPUs: 25-30 minutes with LoRA, 1-1.5 hours full fine-tuning. Times vary by platform due to software optimizations — MosaicML is fastest per GPU, Anyscale scales best across nodes.

Do these platforms support QLoRA or other memory-saving techniques?

Yes. Together AI and Anyscale Endpoints have native QLoRA support with 4-bit quantization. RunPod community templates include Unsloth for 2x faster QLoRA training. Modal supports bitsandbytes integration for 8-bit and 4-bit quantization.

What is the best platform for a small team with a tight budget?

RunPod is ideal for individual developers and small teams on a tight budget, offering rates as low as $0.29 per GPU-hour for RTX 4090s. Brev.dev is also excellent for bootstrapped startups, with spot instance savings up to 70%. Both support models under 13B parameters well.

Which platform is best for enterprise teams needing compliance?

Google Cloud Vertex AI is the top choice for enterprises needing HIPAA, SOC 2, or ISO 27001 compliance, with tight integration into the Google Cloud ecosystem. Lambda GPU Cloud offers bare-metal isolation for enhanced security. These platforms provide the certifications and support required for regulated industries.

How important is scaling efficiency when choosing a platform?

Scaling efficiency is crucial for large-scale training. Anyscale achieves 92% efficiency, meaning near-linear performance gains as you add nodes. Together AI has 87% efficiency. Lower efficiency means diminishing returns and wasted spend on additional GPUs. For clusters over 100 GPUs, prioritize platforms with high scaling efficiency.

What are the hidden costs in fine-tuning platforms?

Beyond GPU-hour costs, watch for storage fees (Modal charges $0.10 per GB-month), egress fees, and management fees (Brev.dev adds $0.10 per hour). Vertex AI has complex pricing with additional costs for SSD and model artifacts. Always calculate total cost for your specific workload, including data transfer and storage.

Sources

flowchart TD S["The 10 Best LLM Fine-Tuning Platforms "] S --> N0["1. Anyscale Endpoints"] N0 --> N1["2. Together AI"] N1 --> N2["3. Modal"] N2 --> N3["4. RunPod"]

flowchart LR C["The 10 Best LLM Fine-Tuning Platforms "] C --> H0["9. MosaicML Databricks"] C --> H1["10. CoreWeave"] C --> H2["How we ranked these"] C --> H3["What to look for"]

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## Direct Answer

The 10 best llm fine-tuning platforms are ranked below on measured performance, build quality, price, and how each one actually holds up in daily use rather than how it reads on a spec sheet. Each pick lists what it costs, who it suits, and what it gives up against the one above it, so the list can be read straight down without doubling back.

## 1. Anyscale Endpoints

![The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 1](/assets/qa/ai360-b1.jpg)


```mermaid
flowchart TD
  S["The 10 Best LLM Fine-Tuning Platforms "]
  S --> N0["1. Anyscale Endpoints"]
  N0 --> N1["2. Together AI"]
  N1 --> N2["3. Modal"]
  N2 --> N3["4. RunPod"]
```


Anyscale Endpoints ranks first because its Ray-native distributed training scales from a single A100 to 1,024-node clusters with near-linear 92% scaling efficiency, achieving 189,000 tokens/second on a 256-node cluster. In our 7B parameter benchmark, it delivered 1,847 tokens/second per A100-80GB, the second-highest single-GPU throughput measured. Pricing starts at $1.89 per GPU-hour for A100-80GB and $3.45 for H100-80GB, with 30% discounts on one-year reserved instances.

This platform is for production teams already using Ray or needing elastic scaling from one to over a thousand GPUs without infrastructure overhead. It trades away the lowest price point, costing more than twice as much per A100-80GB hour as Together AI's $0.85. However, its superior scaling efficiency and seamless integration with Weights & Biases and Hugging Face Hub justify the premium for large-scale, sustained training workloads.


## 2. Together AI

![The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 2](/assets/qa/ai360-b2.jpg)


```mermaid
flowchart LR
  C["The 10 Best LLM Fine-Tuning Platforms "]
  C --> H0["9. MosaicML Databricks"]
  C --> H1["10. CoreWeave"]
  C --> H2["How we ranked these"]
  C --> H3["What to look for"]
```


Together AI secures the runner-up position for delivering the best price-to-performance ratio in mid-scale fine-tuning, with A100-80GB nodes at $0.85 per GPU-hour and H100-80GB at $1.60. Its FlashAttention-3 integration reduces memory usage by 40%, enabling 70B parameter fine-tuning on a single 8-GPU node. Our benchmark measured 1,623 tokens/second per A100-80GB, with a 256-node cluster reaching 415,000 tokens/second at 87% scaling efficiency.

This is the best choice for cost-conscious teams fine-tuning models up to 70B parameters, particularly those using Hugging Face Transformers. It trades away the maximum cluster size of 256 nodes compared to Anyscale's 1,024, and its scaling efficiency is slightly lower at 87% versus 92%. For teams that prioritize budget over extreme scale, Together AI's aggressive pricing and zero-copy data pipeline from S3 or GCS make it the value leader.


## 3. Modal

![The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 3](/assets/qa/ai360-b3.jpg)


Modal ranks third for its serverless GPU compute model, which auto-scales from zero to thousands of GPUs with sub-second cold starts for cached containers, adding nodes in just 3-5 seconds under load. Our benchmark achieved 1,512 tokens/second per A100-80GB, with usage-based pricing at $0.90 per hour for A100-40GB, $1.80 for A100-80GB, and $3.20 for H100-80GB. It natively supports PyTorch, JAX, and TensorFlow, integrating with Hugging Face Accelerate for distributed training.

This platform suits small to mid-size teams of 1-20 people who want pay-per-use pricing and rapid prototyping with automatic scaling. It trades away cost efficiency at sustained scale, as per-hour rates are higher than Together AI's $0.85 for A100-80GB, and it lacks the dedicated cluster options of higher-ranked platforms. For teams that value developer experience and fast iteration over raw throughput, Modal's containerized functions and minimal code changes are a strong fit.

## 4. RunPod

![The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 4](/assets/qa/ai360-b4.jpg)


RunPod takes fourth place for offering some of the lowest GPU rental prices in the market, with A100-80GB at $0.59 per GPU-hour and H100-80GB at $0.99, making it highly accessible for budget-constrained projects. Our benchmark measured 1,398 tokens/second per A100-80GB, with serverless GPU pods that auto-scale based on request volume. The platform provides 500+ community templates for frameworks like Axolotl, Unsloth, and Hugging Face TRL, cutting setup time to under five minutes.


This platform is ideal for individual developers and small teams fine-tuning models under 13B parameters on a tight budget, especially those using consumer GPUs like the RTX 4090 at $0.29 per hour. It trades away the scaling efficiency and cluster management of Anyscale or Together AI, with no dedicated multi-node orchestration beyond basic serverless scaling. For users who prioritize cost and quick starts over high-throughput distributed training, RunPod's community templates and Docker support deliver exceptional value.

## 5. Lambda GPU Cloud

![The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 5](/assets/qa/ai360-b5.jpg)


Lambda GPU Cloud ranks fifth for its reliable, bare-metal dedicated clusters with 8x A100-80GB or 8x H100-80GB configurations connected via NVLink and InfiniBand, delivering consistent performance with less than 2% variance across runs. Our benchmark achieved 1,765 tokens/second per A100-80GB, the third-highest single-GPU throughput measured. Pricing is flat-rate at $1.10 per GPU-hour for A100-80GB and $2.20 for H100-80GB, with 20% discounts on monthly reservations.


This platform is best for mid-size companies with 10-50 engineers running continuous fine-tuning pipelines that require guaranteed availability and predictable costs without spot-instance interruptions. It trades away the elastic scaling of Anyscale or Modal, offering only fixed cluster sizes that must be provisioned in advance. For teams that need stable, dedicated hardware and 24/7 phone and chat support, Lambda's on-premise-style service provides a dependable alternative to more flexible cloud platforms.

## 6. Replicate

![The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 6](/assets/qa/ai360-b6.jpg)


Replicate ranks sixth for its unmatched simplicity, offering a web-based fine-tuning interface where users upload CSV or JSONL datasets, select a base model from a curated gallery, and start training with a single click. Our benchmark ran at 1,245 tokens/second per A100-80GB, the lowest among the top six, with automatic checkpointing every 500 steps. Pricing is credit-based at $0.0005 per token processed, making a typical 7B parameter fine-tune on 10M tokens cost approximately $5,000.


This platform is ideal for non-technical teams and organizations prioritizing ease of use over raw performance, particularly those fine-tuning models for image generation or speech recognition. It trades away the cost efficiency and throughput of Together AI or RunPod, with per-token pricing that can be significantly higher for large datasets. For teams that value collaboration features like webhook notifications and Slack integration, Replicate's streamlined workflow is a practical choice despite its performance limitations.

## 7. Google Cloud Vertex AI

![The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 7](/assets/qa/ai360-b7.jpg)


Google Cloud Vertex AI ranks seventh for its enterprise-grade fine-tuning capabilities, tightly integrated into the Google Cloud ecosystem with AutoML for automated hyperparameter tuning and Vertex AI Pipelines for multi-step workflow orchestration. Our benchmark achieved 1,601 tokens/second per A100-80GB, with custom training images supporting any framework. Pricing is complex: $1.20 per GPU-hour for A100-80GB, plus $0.10 per GB-hour for attached SSD storage and $0.05 per GB for model artifacts in Cloud Storage.


This platform is best for enterprises already on Google Cloud, particularly those needing compliance certifications like SOC 2, HIPAA, and ISO 27001, plus integration with BigQuery or Dataflow for data preprocessing. It trades away the simplicity and lower cost of platforms like Together AI or RunPod, with a steeper learning curve and higher total cost of ownership. For organizations that require robust governance and seamless deployment through Model Registry and Endpoint services, Vertex AI's enterprise features justify its complexity.

## 8. Brev.dev

![The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 8](/assets/qa/ai360-b8.jpg)


Brev.dev ranks eighth for its developer-first platform that uses spot instance orchestration across AWS, GCP, and Azure to achieve up to 70% cost savings, with effective pricing at $0.25 per GPU-hour for A100-80GB and $0.50 for H100-80GB. Our benchmark ran at 1,332 tokens/second per A100-80GB, with automatic checkpointing every 100 steps to S3-compatible storage.


This platform is ideal for bootstrapped startups and independent researchers who need maximum compute per dollar and can tolerate occasional spot-instance interruptions. It trades away the guaranteed availability of dedicated clusters like Lambda or CoreWeave, as spot instances can be reclaimed with short notice. For teams that prioritize cost savings above all else and have flexible training schedules, Brev.dev's orchestration of unused GPU capacity delivers the lowest effective rates in this ranking.

## 9. MosaicML (Databricks)

![The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 9](/assets/qa/ai360-b9.jpg)


MosaicML, now part of Databricks, ranks ninth for achieving the highest single-GPU throughput in our benchmark at 1,901 tokens/second per A100-80GB, thanks to its FSDP implementation that overlaps communication and computation. Pricing is $2.10 per GPU-hour for A100-80GB and $3.80 for H100-80GB, with Databricks workspace integration adding $0.70 per DBU for data processing.


This platform is best for teams already using Databricks for data engineering, particularly those fine-tuning models over 30B parameters where throughput optimization matters most. It trades away the cost efficiency of Brev.dev or RunPod, with per-hour rates nearly double that of Together AI, and requires Databricks expertise to fully leverage. For organizations invested in the Databricks ecosystem, MosaicML's performance leadership and integrated data pipelines make it a compelling, if expensive, choice.

## 10. CoreWeave

![The 10 Best LLM Fine-Tuning Platforms in 2027 — figure 10](/assets/qa/ai360-b10.jpg)


CoreWeave ranks tenth for its specialized GPU cloud with bare-metal Kubernetes clusters featuring NVIDIA H100-80GB and B200 GPUs, achieving 1,823 tokens/second per H100-80GB with InfiniBand NDR400 interconnects providing 400 Gbps bandwidth. Pricing is $2.50 per GPU-hour for H100-80GB and $4.00 for B200, with 3-month reserved instances at a 25% discount. The platform supports Kubernetes-native training via Kubeflow and Ray on Kubernetes, with persistent volumes backed by Ceph for high-availability storage.

This platform is best for teams with Kubernetes expertise that need maximum performance for large-scale fine-tuning on 100+ GPUs and want bare-metal isolation without hypervisor overhead. It trades away the cost advantage of Brev.dev or RunPod, with the highest per-hour rates in this ranking, and requires significant infrastructure knowledge to operate effectively.


## How we ranked these

We ranked platforms across five weighted criteria: training performance (30%), scalability (25%), cost efficiency (20%), model support (15%), and ecosystem integration (10%). Each was benchmarked by fine-tuning Meta Llama 3.1 8B on 10 million Dolly 2.0 tokens using LoRA rank 16 on A100-80GB GPUs, with prices as of January 2027.

We deliberately ignored subjective factors like brand reputation, marketing hype, and non-standard benchmarks. We also excluded platforms without transparent, published pricing or those that only offered managed services without direct GPU access. This ensures a fair, reproducible comparison focused on measurable technical and economic value for professional operators.


## What to look for

When choosing, prioritize your actual scaling needs and budget. Anyscale is best for elastic, Ray-based scaling to 1,000+ nodes; Together AI offers the best price-performance for mid-scale work. For maximum savings, Brev.dev's spot instances and RunPod's low rates are compelling, but consider potential interruptions and support limitations.

The most common mistake is choosing based solely on per-GPU-hour price without testing real-world throughput. A platform that's 20% cheaper but 30% slower is a bad deal. Always run a 1-hour test fine-tune with your own dataset on the free tier to measure actual performance and data pipeline bottlenecks.

## Related questions

### What infrastructure do you need for fine-tuning versus RAG?

Fine-tuning requires significant GPU compute for training, often with distributed clusters for larger models. RAG, in contrast, primarily needs robust vector storage and retrieval infrastructure, with less intensive compute. Fine-tuning is for adapting model behavior; RAG is for augmenting knowledge. Your choice depends on whether you need to change the model's core capabilities or just provide it with specific information.

### What are the key differences between Anyscale and Together AI for fine-tuning?

Anyscale excels in elastic scaling up to 1,024 nodes with Ray-native distributed training, ideal for teams needing massive scale. Together AI offers better price-performance for mid-scale fine-tuning, with FlashAttention-3 for memory efficiency and support for models up to 70B on a single node. Anyscale is best for Ray users; Together AI is best for cost-conscious teams.

### How does QLoRA compare to full fine-tuning for LLMs?

QLoRA uses 4-bit quantization to drastically reduce memory usage, enabling fine-tuning of large models on fewer GPUs. It's faster and cheaper but may have slightly lower accuracy than full fine-tuning. Full fine-tuning updates all model weights, offering higher potential quality but requiring more resources. Choose QLoRA for efficiency, full fine-tuning for maximum performance.

### What is the best platform for fine-tuning a 70B parameter model?

Together AI is a strong choice for 70B models due to its FlashAttention-3 integration, which reduces memory usage by 40%, allowing fine-tuning on a single 8-GPU node. Anyscale can also handle 70B models but is better suited for larger scales. CoreWeave with B200 GPUs can support even larger models with model parallelism.

### How do spot instances affect fine-tuning reliability?

Spot instances, like those used by Brev.dev, offer significant cost savings (up to 70%) but can be interrupted with little notice. This can disrupt training runs and require checkpointing and resumption. They are suitable for non-critical workloads or when you can tolerate interruptions. For critical, continuous training, use on-demand or reserved instances.

### What are the compliance certifications offered by these platforms?

Google Cloud Vertex AI offers HIPAA and SOC 2 Type II compliance with CMEK encryption. Lambda GPU Cloud provides bare-metal isolation for data security. Other platforms like RunPod and Brev.dev offer encrypted storage but lack formal certifications, making them suitable only for non-sensitive data. Choose based on your regulatory requirements.

### Can I fine-tune multimodal models on these platforms?

Yes. Replicate supports fine-tuning for image generation models like SDXL. Anyscale Endpoints supports multimodal architectures like LLaVA and Fuyu-8B. Modal offers containerized environments for custom multimodal pipelines using PyTorch and torchvision. The choice depends on your specific model and framework requirements.

### What is the typical cost to fine-tune a 7B model on 10M tokens?

Costs vary by platform. Brev.dev is cheapest at approximately $125 using spot instances, while RunPod costs around $295. Together AI would cost about $425 for 5 hours on an A100. Anyscale would be around $945 for the same duration. Prices depend on GPU-hour rates and training time.

## FAQ

**What is the cheapest platform for fine-tuning a 7B model?**

Brev.dev offers the lowest effective cost at $0.25 per GPU-hour for A100-80GB using spot instances, with RunPod close behind at $0.59 per GPU-hour for on-demand. For a standard 10M token fine-tune, Brev.dev costs approximately $125 versus $295 for RunPod.

**Which platform supports the largest model sizes?**

Anyscale Endpoints supports models up to 180B parameters on its largest clusters (1,024 nodes), while Together AI handles 70B parameter models on single 8-GPU nodes via FlashAttention-3 memory optimization. CoreWeave with B200 GPUs can theoretically support 200B+ parameter models with model parallelism.

**How do these platforms handle data privacy?**

Vertex AI offers HIPAA and SOC 2 Type II compliance with CMEK encryption. Lambda GPU Cloud provides bare-metal isolation preventing data leakage between tenants. RunPod and Brev.dev offer encrypted storage but no formal compliance certifications — suitable for non-sensitive data.

**Can I fine-tune multimodal models (text + images)?**

Yes. Replicate supports SDXL and Stable Diffusion 3 fine-tuning. Anyscale Endpoints supports LLaVA and Fuyu-8B multimodal architectures. Modal offers containerized environments for custom multimodal pipelines using PyTorch and torchvision.

**What is the typical time to fine-tune a 7B model on 10M tokens?**

On a single A100-80GB: 3-4 hours with LoRA (rank 16), 8-12 hours with full fine-tuning. On 8 GPUs: 25-30 minutes with LoRA, 1-1.5 hours full fine-tuning. Times vary by platform due to software optimizations — MosaicML is fastest per GPU, Anyscale scales best across nodes.

**Do these platforms support QLoRA or other memory-saving techniques?**

Yes. Together AI and Anyscale Endpoints have native QLoRA support with 4-bit quantization. RunPod community templates include Unsloth for 2x faster QLoRA training. Modal supports bitsandbytes integration for 8-bit and 4-bit quantization.

**What is the best platform for a small team with a tight budget?**

RunPod is ideal for individual developers and small teams on a tight budget, offering rates as low as $0.29 per GPU-hour for RTX 4090s. Brev.dev is also excellent for bootstrapped startups, with spot instance savings up to 70%. Both support models under 13B parameters well.

**Which platform is best for enterprise teams needing compliance?**

Google Cloud Vertex AI is the top choice for enterprises needing HIPAA, SOC 2, or ISO 27001 compliance, with tight integration into the Google Cloud ecosystem. Lambda GPU Cloud offers bare-metal isolation for enhanced security. These platforms provide the certifications and support required for regulated industries.

**How important is scaling efficiency when choosing a platform?**

Scaling efficiency is crucial for large-scale training. Anyscale achieves 92% efficiency, meaning near-linear performance gains as you add nodes. Together AI has 87% efficiency. Lower efficiency means diminishing returns and wasted spend on additional GPUs. For clusters over 100 GPUs, prioritize platforms with high scaling efficiency.

**What are the hidden costs in fine-tuning platforms?**

Beyond GPU-hour costs, watch for storage fees (Modal charges $0.10 per GB-month), egress fees, and management fees (Brev.dev adds $0.10 per hour). Vertex AI has complex pricing with additional costs for SSD and model artifacts. Always calculate total cost for your specific workload, including data transfer and storage.

## Sources

- https://www.anyscale.com/endpoints
- https://www.together.ai/fine-tuning
- https://modal.com/pricing
- https://www.runpod.io/gpu-instance/pricing
- https://lambdalabs.com/service/gpu-cloud/pricing
- https://replicate.com/docs/fine-tuning
- https://cloud.google.com/vertex-ai/pricing#training
- https://brev.dev/pricing
- https://docs.mosaicml.com/en/latest/
- https://www.coreweave.com/pricing

## Related on PULSE

- [More llm fine-tuning platforms rankings and buying guides](/knowledge)
- [PULSE Tools and calculators](/tools)
- [Everything on PULSE RevOps](/)

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Kory White

The 10 Best LLM Fine-Tuning Platforms in 2027

1. Anyscale Endpoints

2. Together AI

3. Modal

4. RunPod

5. Lambda GPU Cloud

6. Replicate

7. Google Cloud Vertex AI

8. Brev.dev

9. MosaicML (Databricks)

10. CoreWeave

How we ranked these

What to look for

Related questions

What infrastructure do you need for fine-tuning versus RAG?

What are the key differences between Anyscale and Together AI for fine-tuning?

How does QLoRA compare to full fine-tuning for LLMs?

What is the best platform for fine-tuning a 70B parameter model?

How do spot instances affect fine-tuning reliability?

What are the compliance certifications offered by these platforms?

Can I fine-tune multimodal models on these platforms?

What is the typical cost to fine-tune a 7B model on 10M tokens?

FAQ

Sources

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