Together AI built its name on language models. It serves a deep catalog of open-weight LLMs, offers fine-tuning (LoRA and full), and rents raw compute through on-demand and reserved GPU clusters. If that is why you are there, nothing on this page replaces it: a per-call generation API is not a substitute for GPU hosting or custom model training, and Together's infrastructure side is a genuinely strong product.
What is easy to get wrong, checked against Together's current docs rather than assumed, is whether it does image and video generation at all. It does. Together exposes an OpenAI-compatible image endpoint (POST /v1/images/generations) serving the FLUX family and an expanded serverless image catalog, plus a newer, separate native async endpoint for video that covers a broad set of third-party video models (Veo, Sora 2, Seedance, Hailuo and more), priced per clip from roughly $0.11 up. Billing across the board is pure consumption: pay per token, per image, or per video, with no subscription tiers or minimums.
So the honest framing is not "Together can't generate media." It is that generation is a newer layer on a platform whose center of gravity is LLM inference and GPU infrastructure, and that image and video live on two different request shapes there (OpenAI-compatible for image, a native endpoint for video). If what you want is a managed, per-call image and video generation API with a single integration surface, here are the best Together AI alternatives in 2026.
The best alternatives
1. HiAPI — one API for image, video, audio, and text
HiAPI is built around exactly the job Together layers on top of its LLM stack: a single API key that calls 60+ models across image, video, audio, and text, billed per successful call. Failed generations aren't charged, there's no minimum spend, and new accounts start with 200 free credits.
The design difference from Together is the integration surface. Text is OpenAI-compatible (swap the base URL and your existing Chat Completions code keeps working), and image, video, and audio all run through one unified async task endpoint (POST /v1/tasks, then poll or use a callback). You don't learn one request shape for image and a different one for video. HiAPI also ships a native remote MCP server (mcp.hiapi.ai/mcp, no local install) plus Agent Skills for Claude Code and Codex, so coding agents can generate media directly. The current lineup spans image models like GPT Image 2, Nano Banana 2/Pro, Seedream 5, FLUX 2, and Qwen Image; video models including Veo 3.1, Kling 3, Seedance 2.5, Hailuo, Wan, and MiniMax; and audio models such as Suno, ElevenLabs, MiniMax Music, and Qwen Audio. Image pricing today runs from about $0.008 per image at the low end to around $0.17 for the premium tier; check the pricing page for current per-model rates, since they change as new models ship.
Be clear about the boundary, though: HiAPI is hosted inference only. It does not offer open-weight downloads, self-hosting, fine-tuning, or GPU clusters. If you use Together for any of those, HiAPI covers the generation half of your workload, not the infrastructure half.
Best for: teams that want one key, one billing model, and one request shape across every generation modality, without hosting anything.
2. Replicate — best for the widest model catalog
Replicate (acquired by Cloudflare in December 2025) still has the deepest catalog of open and community-published models, from mainstream image and video generators to obscure research checkpoints. Pricing is a genuine hybrid: popular official models bill per output, while custom models packaged with its open-source Cog tool bill per second of compute. That hybrid matters here, because Replicate is also the closest of these four to Together's customization side: you can fine-tune supported models and self-deploy your own with Cog, not only call pre-hosted endpoints.
Best for: developers who want to browse and test the broadest possible range of open-source models, and keep the option to fine-tune or deploy their own.
3. fal.ai — best for raw inference speed (and LoRA training)
fal.ai built its reputation on fast inference, and it is usually one of the first places new open-source image and video models appear with optimized, low-latency endpoints. Pricing is pay-as-you-go per output across a large model library. It is also one of the few generation-first platforms with dedicated LoRA training endpoints for open-weight image models, so if you left Together partly for its fine-tuning, fal covers a slice of that without the full infrastructure overhead.
Best for: latency-sensitive workloads where shaving seconds off generation matters, with light custom-training needs on the side.
4. Hugging Face — best if you want to stay in the open-weight ecosystem
If part of what drew you to Together was open-weight models, Hugging Face is the most natural landing spot. It is the hub where open model weights live, and it gives you both ends of the spectrum: download weights to self-host, or call them through Serverless Inference and Dedicated Inference Endpoints without running GPUs yourself. It also supports training and fine-tuning workflows. The trade-off versus a purpose-built generation gateway is that coverage and ergonomics vary by model, and you do more of the assembly yourself.
Best for: teams that want to stay close to open weights and flex between self-hosting and managed inference as needs change.
5. Need GPU clusters or custom training, not a generation API?
If the reason you are on Together is the GPU clusters, dedicated inference, or fine-tuning, be honest with yourself that the four platforms above solve a different problem. A per-call generation API hands you a finished image or video; it does not hand you a GPU to run arbitrary code or train a model from scratch. For that workload, a GPU-compute platform is the real comparison. See our Modal alternatives comparison if on-demand GPU deployment, rather than prompt-in / media-out, is what you actually need.
Quick comparison
| Platform | Best for | Pricing model | Managed image + video | GPU hosting / fine-tuning |
|---|---|---|---|---|
| HiAPI | One key, one request shape across image, video, audio, text | Per-call, pay-as-you-go | Yes, one unified /v1/tasks endpoint | No — hosted inference only |
| Together AI | LLM inference + generation in one account | Per-token / per-image / per-video | Yes (image OpenAI-compatible; video on a separate native endpoint) | Yes — GPU clusters, dedicated inference, fine-tuning |
| Replicate | Widest open/community catalog | Per-output for official models; per-second for custom Cog deployments | Yes | Partial — fine-tune + self-deploy via Cog |
| fal.ai | Fastest raw inference | Per-output, pay-as-you-go | Yes | Partial — dedicated LoRA training endpoints |
| Hugging Face | Staying in the open-weight ecosystem | Free tier + per-use endpoints | Partial — model-dependent | Yes — self-host weights, managed endpoints, training |
This table credits Together AI where it is genuinely strong (OpenAI-compatible image calls, real video generation, and the GPU/fine-tuning stack HiAPI deliberately does not offer). HiAPI's only "no" column is infrastructure, which is a positioning choice, not a hidden weakness. Prices and exact capabilities change, so confirm against each platform's current docs before you commit.
How to choose
- Want one key and one request shape for image, video, audio, and LLM calls, billed per call → HiAPI
- Already running LLM workloads on Together and want to keep generation in the same account → stay on Together AI
- Want the broadest catalog of open-source models, with room to fine-tune or self-deploy → Replicate
- Latency is the deciding factor → fal.ai
- Want to stay in the open-weight ecosystem and flex between self-hosting and managed inference → Hugging Face
- You actually need GPU clusters or to train your own model → a GPU-compute platform, not a per-call generation API
FAQ
Does Together AI support image and video generation, or is it only for LLMs?
Both, as of 2026. Together exposes an OpenAI-compatible image endpoint (/v1/images/generations) serving the FLUX family and a wider serverless image catalog, and a separate, newer native async endpoint for video covering a broad set of third-party video models. It is no longer LLM-only. The nuance is that image and video sit on two different request shapes, and generation is a newer layer on a platform still centered on LLM inference and GPU infrastructure.
Is HiAPI a good alternative to Together AI?
For the generation half, yes: one key, 60+ pre-hosted image, video, audio, and text models, billed per successful call, with a single /v1/tasks shape for all media. For the infrastructure half, no: if you rely on Together's GPU clusters, dedicated inference, or fine-tuning, HiAPI doesn't replace that, because it's hosted inference only and doesn't rent GPUs or train models.
Which is cheaper, HiAPI or Together AI?
They bill differently, so it depends on your usage. Both are pure consumption with no subscription, but HiAPI charges a flat per-call price per model (see the pricing page for current rates), while Together prices per token for text, per image, and per video. Compare on the specific models and volumes you'll actually run, not a headline number, and remember HiAPI doesn't charge for failed generations.
How do I migrate image and video generation from Together to HiAPI?
Sign up and grab an API key, then change your request target. For text, point your OpenAI-compatible client at HiAPI's base URL. For image, video, or audio, submit a job to POST /v1/tasks with the model name and prompt, then poll or use a callback for the result URL. One practical difference: on Together you call image and video through two different endpoints, whereas on HiAPI all media uses the same task endpoint, so you consolidate rather than maintain two shapes.
Do these platforms expose OpenAI-compatible and agent/MCP workflows?
HiAPI and Together AI both offer OpenAI-compatible Chat Completions endpoints for text, and both image stacks are OpenAI-shaped. HiAPI additionally runs a native, generation-capable remote MCP server plus Agent Skills, so coding agents can produce media directly rather than only reading docs. Replicate, fal.ai, and Hugging Face each have their own API surfaces and varying agent support; confirm the current state in their docs.
I came to Together for fine-tuning and GPUs. What should I actually look at?
Not a generation API. If you need to fine-tune or run your own inference code on dedicated GPUs, compare GPU-compute platforms instead; our Modal alternatives comparison is the right starting point. Replicate (Cog self-deploy) and Hugging Face (endpoints plus training) sit between the two worlds if you want some generation convenience with some customization.
Bottom line
Together AI is a strong platform that grew from LLM inference into real image and video generation, and its GPU and fine-tuning stack is not something any per-call API should pretend to replace. But if the part you care about is managed, per-call image and video generation with a single, clean integration surface, HiAPI, Replicate, fal.ai, and Hugging Face are all reasonable choices, each with a genuinely different strength: HiAPI for breadth and one unified API across every modality, Replicate for catalog depth, fal.ai for speed, and Hugging Face for staying in the open-weight ecosystem.
Get a free HiAPI API key and try any of the models above. New accounts start with 200 free credits, and failed generations aren't charged.
