Logos vs OpenAI vs Together vs Replicate: Fine-Tuning Buyer's Guide

The short answer

There are four meaningful choices if you want to fine-tune a large language model in 2026 without managing GPU infrastructure yourself:

OpenAI Fine-Tuning — fine-tune a GPT model, keep it behind the OpenAI API.
Together AI (and Fireworks AI) — fine-tune an open-weight model, run hosted inference, optionally export the weights.
Replicate — fine-tune an open model, run inference through Replicate's API, export the weights.
Logos — fine-tune an open-weight model, receive the weights, run inference yourself.

All four are valid. They solve different problems. This post walks through each on the dimensions that actually matter for production buyers: weight ownership, pricing model, model catalog, inference flexibility, evaluation quality, and data policy.

The feature matrix

	Logos	OpenAI	Together AI	Replicate	Hugging Face AutoTrain
You own the weights	Yes	No	Yes	Yes (export)	Yes
Hosted inference included	No (by design)	Yes (required)	Yes (optional)	Yes (default)	Optional
Model catalog	45 open-weight across 16 families	GPT-4.1 family (4.1, 4.1-mini, 4.1-nano) + GPT-3.5 Turbo	Any HF-Hub open model + native frontier MoE (Kimi-K2, GLM-4.7, DeepSeek-V3, Qwen3-235B)	Many open models	Many open models
Pricing model	Flat one-time fee	Per-token training + per-token inference	Per-token training + optional per-token inference	Per-compute-second	Per-training-hour
Starting price	$349	Pay-per-use	Pay-per-use	Pay-per-use	Pay-per-use
Full-parameter FT	Atelier + Custom	No	Select models	Limited	Limited
LoRA + QLoRA	Yes	No (managed)	Yes	Yes	Yes
Evaluation report included	Yes (HTML with before/after)	No	No	No	No
Function-calling / tool-use add-on	Yes ($499)	Built-in to GPT	No dedicated add-on	No	No
Quantized variant delivery	Yes ($349)	No	Partial	Partial	Via separate tools
Data retention	Deleted on completion	Not used for training (per policy)	Varies by plan	Retained	Varies

Each column hides nuance. Let's walk through them.

OpenAI Fine-Tuning — when it's the right answer

OpenAI's fine-tuning API is the simplest path if three things are all true:

A GPT model is a good fit for your task (strong general-purpose instruction following, built-in tool-use).
You're comfortable with all your inference running through OpenAI's API at per-token rates.
You don't need to run the model on-prem, at the edge, or on air-gapped infrastructure.

Fine-tuning a GPT-4.1-mini for a specific classification task, shipping it behind your existing OpenAI integration, and never thinking about GPUs again is a legitimate product decision. Pay-per-use works out well for low-volume deployments.

Where it stops being the right answer:

Your inference volume makes per-token fees dominate your infrastructure budget.
You need the model to run locally or in an air-gapped environment.
You want the option to swap providers later (you can't — the weights are OpenAI's).
You want a non-GPT base (Llama, Qwen, Mistral, Phi) for licensing, cost, or capability reasons.
OpenAI deprecates your fine-tune's base model and you have to re-fine-tune on the new base.

If any of these apply, OpenAI is not the answer. Consider Logos, Together, or Replicate.

Together AI — when it's the right answer

Together AI and Fireworks AI are the closest competitors to Logos on model catalog. Both fine-tune open-weight models (Llama, Mistral, Qwen, Gemma) and both let you export the weights. The difference is what they're optimized for.

Together is the right answer if:

You want hosted inference from day one with zero ops overhead. Upload data, click "fine-tune," get an endpoint, ship.
You expect steady per-token inference costs to be smaller than self-hosted infrastructure costs at your scale.
You want to pay only for what you use — training and inference both billed per-token.

Where Logos wins versus Together:

Flat-fee pricing. A $1,499 Studio project has no ongoing meter. Together's per-token training pricing scales with dataset size; on a 100K-example dataset with a 32B base, the bill can exceed Studio.
Weight delivery is the product, not one option among many. Together ships first-class checkpoint-download tooling today (merged / adapter / default variants, ZSTD tarballs, optional HF-Hub push), but their revenue center is hosted inference. With Logos, weights are the only thing you get — there is no inference business pulling at the product roadmap.
Evaluation report is included. Together requires you to bring your own evaluation pipeline. Logos ships before/after metrics as HTML by default.
Specific frontier MoE availability. Logos's catalog includes GLM-5.1, Kimi K2.5, Qwen 3.5 397B-A17B, and MiniMax-M1 (456B) as named catalog entries with fixed surcharge pricing. Together supports most frontier models via HF-Hub integration but version-specific availability varies — confirm whichever specific variant you need before committing.

Together and Logos solve adjacent but different problems. Many teams use both — Logos for weight-owned production deployments, Together for prototypes that need a shareable endpoint the same day.

Replicate — when it's the right answer

Replicate's fine-tuning flow is built around their "cog" packaging system. Train a model, and it lives in Replicate's registry; you invoke it through their API like any other Replicate model.

Replicate is the right answer if:

You're already running your ML stack through Replicate and fine-tuning is one more model in that stack.
You want the cog abstraction — a standardized container, versioned endpoints, their CI/CD.
You're comfortable with Replicate's per-compute-second pricing model.

Where Logos wins versus Replicate:

Pricing predictability. Per-second billing can surprise on long training runs. Logos is a flat fee.
Inference flexibility. You're not bound to Replicate's runtime. Logos weights drop into vLLM, llama.cpp, Ollama, MLX, TensorRT-LLM directly.
Specialist add-ons. Replicate is infrastructure-shaped; Logos is deliverable-shaped. The Custom Evaluation Suite, DPO Preference Tuning, and Function Calling add-ons don't have direct Replicate equivalents.

Hugging Face AutoTrain — briefly

AutoTrain is the lowest-friction way to fine-tune without writing code. Upload data, pick a model from the Hub, click go. You get weights back that drop into the HF ecosystem cleanly.

AutoTrain wins on:

Simplicity. Zero code, guided UI.
HF ecosystem integration. Weights land in your HF account, deployable via HF inference endpoints or downloadable.

Logos wins on:

Curation. Logos's 30+ models are vetted for production training; AutoTrain lets you pick anything on the Hub, including models that will not train cleanly.
Deliverables. Evaluation report, inference snippets, add-ons — AutoTrain is a training runner, Logos is a project.
Advisory. The Atelier tier's engineer consultation doesn't exist on AutoTrain.

If you've already hand-picked the base model and know your training config, AutoTrain is often the right call. If you want someone to pick the right model for the job and ship it with evaluation, Logos.

Decision tree

Answer these in order:

Do you need to own the weights? If no → OpenAI is simplest. If yes → continue.
Do you need hosted inference from day one? If yes → Together or Replicate. If no → continue.
Do you want predictable flat-fee pricing and an evaluation report? If yes → Logos. If you want per-token metering → Together.
Is your data regulated (HIPAA / SOC 2 / PCI / ITAR)? If yes → neither Logos nor Together; you need Erkos (DDG's on-prem secure fine-tuning service, built on the IronClaw security framework).

What Logos deliberately does not do

A buyer's guide should be honest about non-features. Logos does not:

Host inference. We do not run an inference API and we will not. This is a design choice, not a gap. Run your model on vLLM, llama.cpp, Ollama, Together, Fireworks, RunPod, your own cloud, or your laptop.
Offer ongoing support contracts. Each engagement is scoped. Atelier includes engineer consultation during the delivery window; ongoing engineering retainers are out of scope for Logos (they're in scope for Erkos).
Fine-tune GPT-4o or Claude. Those weights aren't available. If you need a closed-weight fine-tune, use the vendor's API.
Commit to a fixed turnaround on the order page. Training time depends on dataset size, base model, and current capacity — we estimate at intake after seeing your data, not before. Shipping a 10K-example LoRA on a 7B is hours; shipping a full-parameter 70B with DPO on 100K examples is days.

Frequently asked questions

Why choose Logos over OpenAI fine-tuning?

Two reasons that matter for most buyers: you own the weights, and you avoid per-token inference fees. OpenAI fine-tunes a GPT variant that only runs through their API at ongoing per-token rates. Logos fine-tunes an open-weight model, delivers the .safetensors, and you run inference anywhere. If your inference volume is meaningful, the flat-fee-vs-metered economics favor Logos.

Is Together AI better than Logos?

They're optimized for different problems. Together is best when you want hosted inference from day one with zero ops overhead. Logos is best when you want weight ownership and flat-fee pricing. Many teams use both — Logos to own the weights for production, Together for prototype endpoints that need to be live the same day.

When should I use Replicate instead?

If your stack already runs through Replicate's cog abstraction and you want the fine-tuned model in Replicate's registry, Replicate's per-compute-second pricing is a natural fit. If you want flat-fee pricing and the weights delivered as raw .safetensors, Logos is the match.

Can I move my fine-tuned model between providers?

With Logos, yes — you have the raw .safetensors and the LoRA adapter. Drop into vLLM, llama.cpp, Ollama, Together, Fireworks, RunPod, MLX, TensorRT-LLM, or any inference stack that accepts standard open-weight formats. OpenAI fine-tunes do not leave OpenAI.

What if I need regulated-industry handling?

For HIPAA / SOC 2 / PCI / ITAR / air-gapped deployments, neither Logos nor Together nor Replicate is the right service. Use Erkos — DDG's on-prem secure fine-tuning service, built on the IronClaw security framework. Erkos runs inside your infrastructure; training data and weights never leave your environment.

Does Logos fine-tune GPT-4o or Claude?

No — closed-weight models from OpenAI and Anthropic cannot be fine-tuned outside the vendor's own API. Logos fine-tunes open-weight models (Llama, Qwen, Mistral, Mixtral, Gemma, Phi-4, DeepSeek, Nemotron, GLM, Kimi). If you need a fine-tuned GPT or Claude, use the respective vendor's API.

Summary of when Logos is the right choice

Logos is the right answer when:

You want weight ownership — weights delivered as .safetensors plus LoRA adapter
You want flat-fee pricing that doesn't scale with token throughput
You want an evaluation report bundled with training
You want to run inference anywhere — local, your own cloud, any provider
You want a curated catalog that includes frontier MoE (GLM-5.1, Kimi K2.5, Qwen 397B)
You want predictable deliverables — weights, eval, config, inference snippets, all on a signed download URL

If any of those is the deciding factor for your project, the Logos tiers page shows pricing and model selector. Starter at $349 is the fastest way to validate the service; Studio at $1,499 is the typical production tier; Atelier at $3,499 covers 70B class and up with engineer consultation.

Next post in this silo: a deep-dive ranking all 30+ base models in the Logos catalog by production readiness, training cost, and use-case fit.