If you’re shipping AI features in 2026, the choice of inference API directly shapes both UX and unit economics. WaveSpeedAI and Together AI are both credible production platforms, but they target meaningfully different workloads. WaveSpeedAI is the speed-optimised platform for generative media (image, video). Together AI is the open-source LLM inference platform that serves Llama, Mixtral, Qwen, DeepSeek, and dozens of other text models at low cost per token.
The decision usually comes down to whether your workload is media generation (Wave’s home turf) or LLM inference (Together’s). For teams doing both, you’ll often use both. This guide walks through the actual comparison.
⚡ Quick Verdict
- →Pick WaveSpeedAI if your workload is generative media (FLUX, Hunyuan Video, Wan, Kling, Veo) and inference speed directly affects your product UX or unit economics.
- →Pick Together AI if your workload is open-source LLM inference (Llama, Mixtral, DeepSeek, Qwen) with OpenAI-compatible APIs and competitive per-token pricing.
📑 Table of Contents
WaveSpeedAI Overview
WaveSpeedAI is engineered around one thesis: generative media inference can be dramatically faster with optimised kernels and a media-focused GPU fleet. The platform hosts FLUX, Hunyuan Video, Wan 2.1, Kling, Veo, SDXL variants, and other top-performing generative models behind a clean API, delivering 2-5× faster generation than general-purpose providers on the same models.
For developers building image editors, video pipelines, or interactive AI media products, the speed advantage materially shapes UX and economics. For broader context, see our roundup of best fast AI inference APIs for developers.
Together AI Overview
Together AI is the leading platform for open-source LLM inference. The catalogue covers Llama (8B, 70B, 405B), Mixtral, Qwen, DeepSeek, Yi, Phi, and dozens of others, served through OpenAI-compatible APIs that make migration from closed-model providers straightforward.
Together AI’s strengths are catalogue breadth (most current open-source LLMs available within days of release), throughput and latency tuned for chat/completion workloads, dedicated endpoints for production, fine-tuning capabilities, and competitive per-token pricing that scales aggressively at volume. The platform is widely used by funded startups and enterprises running open-source LLM workloads in production.
Workload Focus
This is the deciding question. WaveSpeedAI optimises for generative media, the inference patterns (large image/video model parameters, batch sizes, denoising steps) are different from text generation. Custom CUDA kernels and infrastructure decisions targeting these patterns deliver the 2-5× speed advantage.
Together AI optimises for LLM inference, the inference patterns (attention, KV cache, autoregressive decoding) require different optimisation paths. Together’s infrastructure is tuned for high-throughput, low-latency text generation at scale.
For teams doing both media and LLM work, the platforms complement rather than compete. For teams primarily doing one or the other, the focused choice wins.
Pricing Compared
Both use usage-based pricing with no monthly minimums.
WaveSpeedAI charges per generation or per second of GPU time depending on the model. Free credits to start; paid usage starts in the cents-per-generation range for image models, dollars-per-generation for high-end video.
Together AI uses per-token pricing on most LLMs (e.g., Llama 70B around $0.88 per million input tokens, $0.88 per million output tokens). Pricing scales down aggressively at volume. Dedicated endpoints are priced per-hour for production workloads.
The pricing models are different because the workloads are different. Comparing them directly only makes sense if you’re running both, in which case you’d evaluate each separately for its workload.
Performance and Throughput
WaveSpeedAI performance is its biggest selling point on supported models. Generations that take 90+ seconds on general-purpose providers often complete in 20-40 seconds on WaveSpeedAI. For real-time editors or interactive media generation, this matters substantially.
Together AI performance is competitive with other LLM inference providers on tokens per second and time-to-first-token. The platform invests in serving infrastructure tuned for high concurrency, important for chat applications where many users hit the same endpoint simultaneously.
Neither platform tries to beat the other on its home turf because they serve different workloads.
Developer Experience
WaveSpeedAI offers REST and WebSocket APIs with SDKs for Python and JavaScript/TypeScript. The API surface is small (focused on generative media patterns) and integration time is typically an afternoon.
Together AI offers OpenAI-compatible APIs, which means existing code targeting OpenAI’s chat completion API works against Together with a base URL swap. SDKs are mature (Python, JS/TS, Go, REST), documentation is comprehensive, and the platform supports streaming, function calling, and structured outputs aligned with OpenAI conventions.
For LLM-specific developer ergonomics, Together’s OpenAI-compatible API is the smoothest migration story. For generative media, WaveSpeedAI’s focused API is sufficient and integration is straightforward.
Side-by-Side Table
| Feature | WaveSpeedAI | Together AI |
|---|---|---|
| Primary Workload | Generative media (image, video) | Open-source LLM inference |
| Pricing Model | Per generation / GPU second | Per-token (LLMs) |
| Speed Advantage | 2-5× on media models | Competitive LLM throughput |
| Model Catalogue | FLUX, Hunyuan, Wan, Kling, Veo | Llama, Mixtral, Qwen, DeepSeek+ |
| OpenAI-Compatible API | No (focused API) | Yes |
| Streaming | WebSocket | Yes (token streaming) |
| Fine-Tuning | Limited | Yes (full LLM fine-tuning) |
| Dedicated Endpoints | Yes | Yes (per-hour) |
| Best For | Speed-critical media apps | Open-source LLM in production |
Which Should You Choose?
Pick WaveSpeedAI if you are building user-facing apps where image or video generation latency directly shapes UX, run high-volume media batch pipelines where total compute time matters, use mainstream generative models (FLUX, Hunyuan, Wan, Kling, Veo), or want effective cost-per-generation lowest through speed efficiency. WaveSpeedAI is the media performance pick.
Pick Together AI if you run open-source LLMs in production (Llama, Mixtral, Qwen, DeepSeek), need OpenAI-compatible APIs for easy migration from closed providers, want competitive per-token pricing that scales aggressively, require fine-tuning on your own data, or run high-concurrency chat applications. Together AI is the LLM inference pick.
For teams doing both media and LLM work, the answer is usually “both”, each platform on its home turf. They don’t really compete for the same workloads.
⚡ Try WaveSpeedAI for Faster Media Generation
FLUX, Hunyuan, Wan, Kling, and Veo at the lowest latency on the market. Usage-based pricing, no minimums.
Try WaveSpeedAI Free →Frequently Asked Questions
Is WaveSpeedAI or Together AI better?
They’re not direct competitors. WaveSpeedAI is better for generative media (image, video). Together AI is better for open-source LLM inference. Pick based on your workload.
Can Together AI run image generation models?
Together AI supports some image and audio models but its core focus and performance optimisations target LLM workloads. For dedicated image and video generation speed, WaveSpeedAI is the specialist.
Does WaveSpeedAI serve LLMs?
WaveSpeedAI’s primary focus is generative media. For LLM inference workloads, Together AI or other LLM-focused providers are better fits.
Which has better pricing for high volume?
Both scale aggressively at volume on their home workloads. For high-volume LLM inference, Together AI’s per-token pricing becomes very competitive. For high-volume media generation, WaveSpeedAI’s speed efficiency keeps effective cost-per-generation low.
Is Together AI OpenAI-compatible?
Yes, Together AI offers OpenAI-compatible APIs, making migration from OpenAI’s chat completion API straightforward (often just a base URL change).
Can I fine-tune models on either platform?
Together AI supports full LLM fine-tuning on your own data with dedicated endpoints. WaveSpeedAI’s fine-tuning options are more limited and focus on its supported media models.
Do both offer free credits?
Yes, both provide free credits to evaluate. Enough to benchmark your actual workload on each before committing.
Should I use both?
If your product spans media generation and LLM features, yes, each platform on its home turf. The APIs are different enough that you’ll write platform-specific code, but the combination is common in production.
Final Word
WaveSpeedAI and Together AI are both credible inference platforms in 2026, but they serve different workloads. WaveSpeedAI is the right pick for generative media inference where speed shapes UX. Together AI is the right pick for open-source LLM inference in production. Choose based on workload, not platform brand, then add the other for the workloads it’s better at. For broader inference context, see our roundup of best AI video generation platforms for creators.