{"slug":"together-ai","name":"Together AI","domain":"together.ai","verdict":"As of 2026-07-15, ChatGPT, Claude, Gemini, Grok collectively rank Together AI first for fine-tuning platform (one of 8 leaderboards it appears on). Source: https://modelsagree.com/product/together-ai (modelsagree.com, CC BY 4.0).","best_rank":1,"categories":8,"brief":{"category":"best-fine-tuning-platform","title":"Best fine-tuning platform","rank":1,"of":10,"top":null,"day":"2026-07-16","why":[{"t":"managed fine-tuning for open models","m":["ChatGPT","Grok","Claude","Gemini"],"q":"Best managed service for tuning open models"},{"t":"LoRA and full fine-tuning","m":["ChatGPT","Claude"],"q":"both LoRA and full fine-tuning"},{"t":"seamless transition from training to hosting","m":["ChatGPT","Claude","Gemini"],"q":"a seamless transition from training to dedicated serverless hosting"},{"t":"weight export limits lock-in","m":["ChatGPT","Claude"],"q":"downloadable merged or adapter weights that limit lock-in"}],"gap":[],"fix":[{"t":"minimal control over training","m":["Claude","Gemini"],"q":"minimal control over the underlying training hyperparameters"},{"t":"less evaluation and data-management guidance","m":["ChatGPT"],"q":"less end-to-end evaluation and data-management guidance than a full ML platform"},{"t":"pricing concerns at scale","m":["Claude","Grok"],"q":"Meaningfully pricier than DIY on rented GPUs at scale"}]},"entries":[{"slug":"best-fine-tuning-platform","title":"Best fine-tuning platform","rank":1,"of":10,"score":12,"appearances":4,"modelRanks":{"ChatGPT":2,"Claude":3,"Gemini":5,"Grok":2},"reason":"Excellent managed workflow for LoRA or full fine-tuning, preference optimization, checkpoint control, integrated inference, and downloadable merged or adapter weights that limit lock-in.","reasons":[{"model":"ChatGPT","reason":"Excellent managed workflow for LoRA or full fine-tuning, preference optimization, checkpoint control, integrated inference, and downloadable merged or adapter weights that limit lock-in."},{"model":"Grok","reason":"Excellent managed fine-tuning API for large open-source models (100B+), Hugging Face Hub integration, reliable multi-node training, and strong cost/performance balance for production custom models"},{"model":"Claude","reason":"Best managed service for tuning open models — broad catalog, both LoRA and full fine-tuning, sane per-token pricing, weight export, and one-click deploy to fast serverless inference closes the tune-to-production loop without any GPU ops"},{"model":"Gemini","reason":"Offers highly scalable, production-grade managed fine-tuning APIs for open-source models with OpenAI-compatible endpoints, enabling a seamless transition from training to dedicated serverless hosting."}],"fixes":[{"model":"ChatGPT","fix":"Offers less end-to-end evaluation and data-management guidance than a full ML platform, so practitioners must supply their own quality loop."},{"model":"Claude","fix":"Meaningfully pricier than DIY on rented GPUs at scale, and you trade away low-level control of the training loop (custom losses, exotic architectures)"},{"model":"Gemini","fix":"Provides minimal control over the underlying training hyperparameters and restricts users to their supported set of base models."},{"model":"Grok","fix":"Broaden proprietary model support and simplify billing/pricing transparency for high-volume users"}],"updated":"2026-07-15","rank_history":{"days":["2026-06-29","2026-06-30","2026-07-08","2026-07-09","2026-07-10","2026-07-12","2026-07-13","2026-07-14","2026-07-15"],"ranks":[1,2,1,1,3,1,2,1,3]},"reasoning_shift":[{"model":"Gemini","from":"2026-07-14","to":"2026-07-15","added":[{"t":"Highly scalable production-grade APIs","q":"highly scalable, production-grade managed fine-tuning APIs"},{"t":"Restricted supported base models","q":"restricts users to their supported set of base models"}],"dropped":[{"t":"Competitive serverless pricing","q":"offering competitive serverless pricing"}]},{"model":"ChatGPT","from":"2026-07-14","to":"2026-07-15","added":[{"t":"Preference optimization","q":"preference optimization"},{"t":"Less evaluation and data-management guidance","q":"Offers less end-to-end evaluation and data-management guidance than a full ML platform"},{"t":"Must supply own quality loop","q":"practitioners must supply their own quality loop"}],"dropped":[{"t":"Model choice and API CLI workflows","q":"model choice, straightforward API and CLI workflows"},{"t":"Integrated experiment tracking","q":"integrated experiment tracking"},{"t":"Tied closely to Together’s infrastructure","q":"Training and serving remain tied closely to Together’s infrastructure"}]},{"model":"Claude","from":"2026-07-14","to":"2026-07-15","added":[{"t":"Broad catalog","q":"broad catalog"},{"t":"Sane per-token pricing","q":"sane per-token pricing"},{"t":"Low-level training control lost","q":"you trade away low-level control of the training loop (custom losses, exotic architectures)"}],"dropped":[{"t":"Upload JSONL","q":"upload JSONL"},{"t":"Run SFT or DPO","q":"run SFT or DPO"},{"t":"Training recipes trail DIY frameworks","q":"Supported model list and training recipes trail the DIY frameworks"}]}],"api":"https://modelsagree.com/api/v1/best/best-fine-tuning-platform.json"},{"slug":"best-serverless-llm-inference-api","title":"Best serverless LLM inference API","rank":2,"of":8,"score":17,"appearances":4,"modelRanks":{"ChatGPT":2,"Claude":1,"Gemini":1,"Grok":3},"reason":"The broadest production-grade catalog of open-weight models (Llama, DeepSeek, Qwen, Kimi, FLUX) behind one OpenAI-compatible API, with consistently strong throughput, high rate limits, and a real growth path from pay-per-token to fine-tuning and dedicated endpoints — the safest default for a practitioner shipping on open models; assumes the typical user wants open-model breadth, not a single frontier model.","reasons":[{"model":"Claude","reason":"The broadest production-grade catalog of open-weight models (Llama, DeepSeek, Qwen, Kimi, FLUX) behind one OpenAI-compatible API, with consistently strong throughput, high rate limits, and a real growth path from pay-per-token to fine-tuning and dedicated endpoints — the safest default for a practitioner shipping on open models; assumes the typical user wants open-model breadth, not a single frontier model."},{"model":"Gemini","reason":"Unmatched breadth in its open-weight model catalog, highly reliable serverless endpoints with OpenAI compatibility, and seamless paths for custom fine-tuning. It serves as the primary benchmark for developer-friendly prototyping."},{"model":"ChatGPT","reason":"Near-tie for first with broad, rapidly updated multimodal model coverage, competitive throughput and pricing, automatic cached-input discounts, batch inference, Serverless LoRA, and easy migration to dedicated deployments."},{"model":"Grok","reason":"broadest open model catalog (200+), excellent fine-tuning + serverless inference, strong performance and reliability for production scale"}],"fixes":[{"model":"ChatGPT","fix":"Its very broad catalog has uneven model-specific performance, so serious workloads require benchmarking rather than trusting platform-wide speed claims."},{"model":"Claude","fix":"Neither the cheapest per token (DeepInfra undercuts it) nor the fastest (Groq/Cerebras beat it on latency), so pure cost- or speed-maximizers should look elsewhere."},{"model":"Gemini","fix":"Higher Time to First Token (TTFT) latency compared to hardware-optimized competitors like Groq, and scaling custom models requires expensive dedicated endpoints."},{"model":"Grok","fix":"simplify billing and reduce complexity for easier high-volume use"}],"updated":"2026-07-15","rank_history":{"days":["2026-06-29","2026-06-30","2026-07-08","2026-07-09","2026-07-10","2026-07-12","2026-07-13","2026-07-14","2026-07-15"],"ranks":[1,3,1,1,2,1,1,2,2]},"reasoning_shift":[{"model":"Gemini","from":"2026-07-14","to":"2026-07-15","added":[{"t":"OpenAI compatibility","q":"OpenAI compatibility"},{"t":"Expensive dedicated endpoints","q":"scaling custom models requires expensive dedicated endpoints"}],"dropped":[{"t":"Enterprise-grade support","q":"enterprise-grade support"},{"t":"Larger model pricing","q":"pricing for larger models is typically slightly higher than budget alternatives"}]},{"model":"Claude","from":"2026-07-13","to":"2026-07-14","added":[{"t":"High rate limits","q":"high rate limits"},{"t":"Not cheapest per token","q":"Neither the cheapest per token (DeepInfra undercuts it)"},{"t":"Latency competitors named","q":"Groq/Cerebras beat it on latency"}],"dropped":[{"t":"No proprietary models","q":"not for teams that want frontier proprietary models (Claude/GPT) served from the same API"}]}],"api":"https://modelsagree.com/api/v1/best/best-serverless-llm-inference-api.json"},{"slug":"best-batch-inference-apis-for-synthetic-data-generation","title":"Best batch inference APIs for synthetic data generation","rank":4,"of":9,"score":7,"appearances":3,"modelRanks":{"ChatGPT":5,"Claude":5,"Grok":1},"reason":"50% cost savings vs realtime on most serverless models with separate high rate limits and up to 30B enqueued tokens per model; broad access to latest open-weight models (Llama, Qwen, DeepSeek, Kimi, etc.); purpose-built for high-volume async jobs including synthetic data generation with reliable sub-24h completion and simple JSONL workflow","reasons":[{"model":"Grok","reason":"50% cost savings vs realtime on most serverless models with separate high rate limits and up to 30B enqueued tokens per model; broad access to latest open-weight models (Llama, Qwen, DeepSeek, Kimi, etc.); purpose-built for high-volume async jobs including synthetic data generation with reliable sub-24h completion and simple JSONL workflow"},{"model":"ChatGPT","reason":"Accessible OpenAI-compatible batch infrastructure for many open models, with a separate rate-limit pool, up to 50,000 requests and 30B queued tokens per model, and fast completion for smaller jobs."},{"model":"Claude","reason":"Best open-model batch API — wide catalog (Llama, Qwen, DeepSeek, Mixtral and more) behind one OpenAI-compatible endpoint with a batch discount, letting you match model to task and license without running infra; strong sweet spot for permissively-licensed, redistributable synthetic datasets."}],"fixes":[{"model":"ChatGPT","fix":"The 50% discount applies only to selected models, while several desirable frontier open models are unavailable for batch processing."},{"model":"Claude","fix":"You inherit open-model quality ceilings and must vet each model's license/behavior yourself; SLA and reliability at extreme scale trail the hyperscalers."},{"model":"Grok","fix":"Not for sub-hour turnaround or workloads that demand the absolute lowest possible per-token rates on the cheapest models"}],"updated":"2026-08-10","rank_history":{"days":["2026-08-03","2026-08-10"],"ranks":[7,1]},"api":"https://modelsagree.com/api/v1/best/best-batch-inference-apis-for-synthetic-data-generation.json"},{"slug":"best-no-code-llm-fine-tuning-platform-for-small-teams","title":"Best no-code LLM fine-tuning platform for small teams","rank":4,"of":10,"score":6,"appearances":3,"modelRanks":{"ChatGPT":3,"Claude":4,"Grok":5},"reason":"Excellent value for tuning open models through a web UI, with broad model choice, LoRA, preference optimization, scalable serving, checkpoints, and downloadable weights","reasons":[{"model":"ChatGPT","reason":"Excellent value for tuning open models through a web UI, with broad model choice, LoRA, preference optimization, scalable serving, checkpoints, and downloadable weights"},{"model":"Claude","reason":"Clean dashboard fine-tuning (LoRA and full fine-tune) over a broad catalog of open-weight models with immediate serverless or dedicated-endpoint deployment on fast inference infrastructure, transparent per-token training pricing, and — unlike OpenAI — downloadable checkpoints, giving small teams open-model ownership without touching a GPU."},{"model":"Grok","reason":"Accessible managed API/UI for LoRA/full fine-tuning on open models with per-token pricing; fast setup, reliable infra, and serving integration; good value for small teams avoiding hardware ops entirely while getting quick results."}],"fixes":[{"model":"ChatGPT","fix":"Data preparation and experiment evaluation are less guided than in OpenPipe or Entry Point AI"},{"model":"Claude","fix":"Thinner product layer than OpenPipe/Predibase — dataset curation, eval loops, and iteration tooling are minimal, so you're assembling your own workflow around the training job."},{"model":"Grok","fix":"Higher per-token costs vs self-hosted for frequent/repeated jobs; data sent to their cloud (less ideal for sensitive/private data)."}],"updated":"2026-07-17","api":"https://modelsagree.com/api/v1/best/best-no-code-llm-fine-tuning-platform-for-small-teams.json"},{"slug":"best-lora-fine-tuning-platforms-for-open-source-llms","title":"Best LoRA fine-tuning platforms for open-source LLMs","rank":5,"of":7,"score":3,"appearances":2,"modelRanks":{"ChatGPT":5,"Claude":4},"reason":"Best managed/serverless option for practitioners who don't want to run infrastructure — upload data, fine-tune LoRA on open models (Llama, Qwen, etc.) via API, and deploy/serve the adapter immediately on the same platform. Predictable pricing and no GPU ops.","reasons":[{"model":"Claude","reason":"Best managed/serverless option for practitioners who don't want to run infrastructure — upload data, fine-tune LoRA on open models (Llama, Qwen, etc.) via API, and deploy/serve the adapter immediately on the same platform. Predictable pricing and no GPU ops."},{"model":"ChatGPT","reason":"Excellent managed default for API-first teams, combining broad modern open-model coverage, LoRA and preference tuning, downloadable adapters or merged weights, experiment tracking, and serverless or dedicated inference"}],"fixes":[{"model":"ChatGPT","fix":"Supported models and training controls remain platform-defined, limiting unusual architectures and deeply customized training"},{"model":"Claude","fix":"Less control and configurability than self-hosted frameworks; you're limited to supported base models and their hyperparameter surface, and data leaves your environment."}],"updated":"2026-08-10","rank_history":{"days":["2026-08-03","2026-08-10"],"ranks":[6,null]},"api":"https://modelsagree.com/api/v1/best/best-lora-fine-tuning-platforms-for-open-source-llms.json"},{"slug":"best-frontier-llm-api-provider","title":"Best frontier LLM API provider","rank":7,"of":7,"score":1,"appearances":1,"modelRanks":{"Gemini":5},"reason":"The premier host for open-weights models offering a unified OpenAI-compatible endpoint, serverless fine-tuning, and superior inference throughput.","reasons":[{"model":"Gemini","reason":"The premier host for open-weights models offering a unified OpenAI-compatible endpoint, serverless fine-tuning, and superior inference throughput."}],"fixes":[{"model":"Gemini","fix":"Open-source models still require significantly more prompt engineering to match the reasoning capabilities of proprietary frontier APIs."}],"updated":"2026-07-13","rank_history":{"days":["2026-06-29","2026-06-30","2026-07-08","2026-07-09","2026-07-10","2026-07-12","2026-07-13"],"ranks":[8,8,null,null,null,null,6]},"api":"https://modelsagree.com/api/v1/best/best-frontier-llm-api-provider.json"},{"slug":"best-gpu-cloud-for-inference","title":"Best GPU cloud for inference","rank":9,"of":9,"score":1,"appearances":1,"modelRanks":{"Claude":5},"reason":"Uniquely spans serverless per-token inference for open-weight models, dedicated endpoints, and raw GPU clusters in one platform, with genuinely strong inference kernels (FlashAttention lineage) driving competitive latency and cost.","reasons":[{"model":"Claude","reason":"Uniquely spans serverless per-token inference for open-weight models, dedicated endpoints, and raw GPU clusters in one platform, with genuinely strong inference kernels (FlashAttention lineage) driving competitive latency and cost."}],"fixes":[{"model":"Claude","fix":"Strongest when you're serving open-weight LLMs through its stack — arbitrary custom-container inference is not its center of gravity."}],"updated":"2026-07-15","reasoning_shift":[{"model":"Claude","from":"2026-07-14","to":"2026-07-15","added":[{"t":"serverless per-token inference","q":"serverless per-token inference for open-weight models"}],"dropped":[{"t":"speculative decoding","q":"speculative decoding"},{"t":"bursty bespoke workloads fit less naturally","q":"bursty bespoke workloads fit less naturally than on Modal or RunPod"},{"t":"cluster rental requires larger commitments","q":"cluster rental skews toward larger commitments"}]}],"api":"https://modelsagree.com/api/v1/best/best-gpu-cloud-for-inference.json"},{"slug":"best-gpu-cloud-for-training","title":"Best GPU cloud for training","rank":10,"of":10,"score":1,"appearances":1,"modelRanks":{"Claude":5},"reason":"GPU Clusters backed by strong research pedigree (FlashAttention lineage), fast interconnects, and training-tuned software stack; attractive when you want cluster rental plus expert-level training support — near-tie with Nebius","reasons":[{"model":"Claude","reason":"GPU Clusters backed by strong research pedigree (FlashAttention lineage), fast interconnects, and training-tuned software stack; attractive when you want cluster rental plus expert-level training support — near-tie with Nebius"}],"fixes":[{"model":"Claude","fix":"Its center of gravity is inference and fine-tuning APIs; pure bare-metal cluster rental is a smaller product with less capacity flexibility than dedicated GPU clouds"}],"updated":"2026-07-15","api":"https://modelsagree.com/api/v1/best/best-gpu-cloud-for-training.json"}],"page":"https://modelsagree.com/product/together-ai","check":"https://modelsagree.com/check?q=Together%20AI","updated":"2026-08-10T18:18:45.051Z","attribution":"modelsagree.com, CC BY 4.0"}