Logos vs OpenAI, Together, and Replicate: Dated Buyer's Guide

The short answer

The market changed after this guide first appeared. As checked on August 25, 2026:

OpenAI Fine-Tuning — the official pricing page says the platform is winding down and unavailable to new users. Existing users of the fine-tuning platform may create training jobs for the coming months, while fine-tuned models remain available for inference until their base models are deprecated.
Together AI — fine-tune supported open models, use hosted inference, and download merged or adapter checkpoints.
Replicate — train models that expose a training interface; destinations, serving, and export depend on that model and workflow.
Logos — scope an open-weight training project and receive the agreed artifacts without a DDG-hosted endpoint.

This is a dated capability comparison, not a universal ranking. Verify the live provider page, exact model, license, retention terms, region, runtime, and full training-plus-inference cost before purchase.

One positioning update for DDG buyers: Logos is not the place to buy standalone framework setup. Logos trains and delivers the model. If the job is "configure OpenClaw or Hermes Agent around my workflow," start with those projects' documentation and open skill surfaces such as the Hermes skills documentation, or route managed business workflows through Icarus. Hermes Agent is a runtime distinct from the Hermes model family, and OpenClaw is an assistant runtime rather than a model-training stack.

The feature matrix

	Logos	OpenAI	Together AI	Replicate	Hugging Face AutoTrain
Downloadable artifact	As scoped; rights follow license/terms	No	Merged or adapter checkpoints	Trainable-model-specific	Yes for completed local/hosted run artifacts
Hosted inference included	No (by design)	Yes (required)	Yes (optional)	Yes (default)	Optional
Model catalog	45 listed entries; feasibility reviewed	Winding down; unavailable to new users, with limited job creation for eligible existing users	Supported list changes	Trainable-model-specific	Hub model + task compatibility
Pricing model	Scoped tier fee plus disclosed model/add-on charges	Per-token training + per-token inference	Per-token training + optional per-token inference	Per-compute-second	Per-training-hour
Starting price	From $349 after intake (Aug. 24, 2026)	Transition access and usage pricing for eligible existing users	Check current calculator	Compute-dependent	Compute-dependent
Full-parameter FT	Custom after feasibility review	No downloadable weights	Select models	Model-specific	Configuration-specific
LoRA + QLoRA	Yes	No (managed)	Yes	Yes	Yes
Evaluation report included	As scoped; Starter lists a PDF baseline comparison and Atelier a custom HTML report	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	Must be stated in accepted order	Fine-tuning state until deleted; abuse monitoring generally 30 days; not ZDR-eligible	Check current plan and policy	Check model and account terms	Depends on selected backend/storage

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

OpenAI Fine-Tuning — wind-down status

OpenAI is not available as a new fine-tuning choice: its official pricing page says the platform is winding down and unavailable to new users. It also says existing users of the fine-tuning platform may create training jobs for the coming months. Fine-tuned models remain available for inference until their base models are deprecated. OpenAI does not publish a more exact end date on that pricing notice, so buyers should treat job creation as temporary transition access rather than ongoing availability.

OpenAI's data controls documentation says API data is not used to train models unless the organization opts in. Fine-tuning jobs nevertheless retain application state until deletion, are generally subject to 30-day abuse-monitoring retention, and are not eligible for Zero Data Retention. Those details are more precise than “not used for training.”

Together AI — when it's the right answer

Together AI is the closest comparison here because it combines open-model training, hosted inference, and documented checkpoint export. Fireworks should be evaluated separately against its current model and export documentation rather than assumed identical.

Together is the right answer if:

You want managed hosted inference and are comfortable with provider onboarding, model support, quotas, and operating terms.
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:

Scoped project pricing. Logos can quote training deliverables at intake. Compare that quote with Together's current calculator; no unsourced 100K-example example proves which is cheaper.
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 scoped. Logos can include before/after task metrics. Confirm Together's current evaluation features rather than assuming none exist.
Specific frontier-model intake. A Logos catalog entry is an invitation to feasibility review, not guaranteed capacity. Together's supported models also change; confirm the exact model and method with either provider.

Together and Logos solve adjacent problems. Together offers checkpoint export as a first-class feature, so the choice should turn on model support, service scope, evaluation, hosting needs, retention, and total cost—not a false rented-versus-owned distinction.

Replicate — when it's the right answer

Replicate exposes training through models that implement a training interface, with the result stored as a destination model and version. Cog is Replicate's packaging and deployment system; it is not itself a universal LLM fine-tuning service. See Replicate's training-destination documentation.

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 the current pricing and artifact behavior of the specific trainable model.

Where Logos wins versus Replicate:

Scoped pricing. Logos quotes an accepted project; Replicate pricing depends on the selected trainer and hardware. Compare actual estimates.
Targeted artifact delivery. Logos can scope the format and conversion you need. .safetensors does not directly load into every runtime; llama.cpp generally requires GGUF, and every architecture must be supported.
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

Hugging Face AutoTrain supports configuration-driven training across documented tasks and backends. Its current configuration documentation should be checked before promising a particular hosted “upload and click” flow.

AutoTrain wins on:

Reusable configuration. Training can be described in a project configuration.
HF ecosystem integration. Artifact location and deployability depend on the selected backend, repository, task, and model.

Logos wins on:

Curation. Logos currently lists 45 intake candidates and evaluates feasibility; catalog presence is not a blanket production guarantee.
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:

Are you an eligible existing OpenAI fine-tuning user managing the wind-down? If yes → confirm current access before creating a transition-period job and plan around the relevant base-model deprecation. New users should continue.
Do you need hosted inference from day one? If yes → Together or Replicate. If no → continue.
Do you want a scoped offline deliverable and evaluation plan? If yes → request Logos intake. If you want managed inference, compare current hosted providers.
Does the data trigger legal, contractual, or assurance obligations? If yes → obtain security and legal review. HIPAA, PCI DSS, ITAR, and SOC 2 are not interchangeable, and on-prem software alone does not establish compliance.

For non-regulated buyers that already have a model and need the skill or setup layer, use the open-source framework ecosystem directly for small technical workflows. Use Icarus when that workflow needs customer channels, approvals, reporting, or managed operations.

What Logos deliberately does not do

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

Host inference. DDG does not run a Logos inference API. Deployment requires a runtime and format compatible with the exact architecture; conversion may be required.
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 closed weights. Logos cannot train GPT or Claude weights. Do not assume Anthropic offers public Claude fine-tuning through its own API; verify current vendor or cloud-platform availability for the exact model.
Commit to a fixed turnaround on the order page. Training time depends on tokens, model, sequence length, method, evaluation scope, and current capacity. Large jobs require a measured pilot.

Frequently asked questions

Why choose Logos over OpenAI fine-tuning?

For new buyers, the immediate distinction is that OpenAI's fine-tuning platform is unavailable to new users, while Logos scopes open-weight artifact delivery. Eligible existing OpenAI fine-tuning users retain limited transition-period job creation, but that is not a new-user option. Downloadable artifacts can avoid mandatory vendor inference, while total cost still includes serving hardware, operations, conversion, monitoring, and support. Customer rights remain subject to model and data licenses.

Is Together AI better than Logos?

Together combines managed inference with checkpoint export. Logos focuses on a scoped training and evaluation deliverable without hosting. Compare the exact model, export variant, price, retention, evaluation, and deployment workload; neither is universally better.

When should I use Replicate instead?

If a Replicate model exposes the trainer and destination behavior you need, keeping the resulting version in Replicate can be convenient. Choose Logos when its intake accepts your requested offline artifact, conversion, and evaluation scope.

Can I move my fine-tuned model between providers?

Sometimes. Portability depends on the delivered artifact, base-model license, architecture, precision, and destination support. vLLM may accept compatible .safetensors; llama.cpp generally needs GGUF; hosted providers enforce upload allowlists. The accepted order should name and validate the target runtime.

What if I need regulated-industry handling?

Regulated or highly sensitive work requires a written data-flow and controls review. HIPAA, PCI DSS, ITAR, and SOC 2 impose different obligations; SOC 2 is an attestation, not a data class. On-prem deployment and IronClaw do not create certification by themselves. Require evidence, responsibilities, retention, subprocessors, and legal sign-off.

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

No. Logos cannot train closed GPT or Claude weights. OpenAI is winding down its fine-tuning platform, and Anthropic does not document a general public Claude fine-tuning API; limited fine-tuning has existed through specific cloud offerings and models. Verify current availability directly with the vendor before designing around it.

Summary of when Logos is the right choice

Logos is the right answer when:

You want a downloadable artifact defined in the order, with rights subject to the base-model license and terms
You want a scoped fixed fee rather than inference charges that scale with token throughput
You want an evaluation report bundled with training
You have a named compatible inference target and accept any required conversion or packaging
You want a curated catalog that includes frontier MoE (GLM-5.1, Kimi K2.5, Qwen 397B)
You want the accepted deliverables and their formats documented before training, with a time-limited signed download URL where applicable

The Logos tiers page shows the current intake prices and model selector. As of August 24, 2026, listed tiers start at $349, $1,499, and $3,499, but model surcharges and Custom quotes apply. Checkout remains intake-first while DDG confirms access, license, data readiness, capacity, method, evaluation, and delivery scope.

Related next reads: the Logos model-ranking guide, the Logos overview, and Icarus demos for managed business workflows.