Together AI Alternatives for Open-Model Inference
November 13, 2026
The right Together AI alternatives depend on what you need that Together isn't giving you. For a similar all-in-one platform for open models, try Fireworks AI or Baseten. For low per-token cost with little else, try DeepInfra. For much faster tokens on a short model list, try Cerebras or Groq. To stop depending on one provider's catalog, route through OpenRouter or Hugging Face Inference Providers. To run your own model code on rented GPUs, use Modal or RunPod.
Together AI is healthy. It announced an $800 million Series C in July 2026, keeps adding capacity, and ships new products like Together Post-Training. People mostly look elsewhere for a narrower reason: the serverless model they built on was retired, or they need speed, control or redundancy that a single shared catalog doesn't give them. This guide sorts the options by that reason.
Quick Comparison
| Provider | Best for | What you run | Billing model |
|---|---|---|---|
| Fireworks AI | Production serving with cost levers | Their catalog or your fine-tunes | Per token, batch at half price, per GPU-second |
| Baseten | Dedicated deployments of your own model | Their Model APIs or your packaged model | Per token, or per GPU-minute |
| DeepInfra | Cheap per-token access | Their catalog | Per token, with priority and flex options |
| Cerebras | Fastest tokens on larger open models | A curated catalog | Prepaid pay-as-you-go and agreements |
| Groq | Very fast gpt-oss | A short self-serve list | Per token |
| OpenRouter | One key, many providers | Other providers' endpoints | Provider prices plus a fee on credit purchases |
| Hugging Face Inference Providers | Open models through one HF token | Partner providers' endpoints | Pay-as-you-go, no markup on provider rates |
| Modal | Your own Python and model code | Containers you write | Per second of compute |
| RunPod Serverless | GPU workers that scale to zero | Containers or vLLM workers | Per second while workers run |
What Changed at Together AI
Together's own pricing page shows how broad the platform is: serverless inference billed per token, provisioned throughput, dedicated single-tenant GPUs billed per hour, GPU clusters, fine-tuning, post-training, code sandboxes and managed storage. The page says most teams start on serverless and move to dedicated endpoints at scale.
The friction sits in serverless. Together's deprecations page sets the rules:
- Upgrades redirect. When a model gets a same-architecture update, the old endpoint redirects to the new version after a 3-day notice.
- New models don't. When a replacement behaves differently, Together deprecates the old model on a 2-week timeline, and you must change the model ID yourself.
- Notice is short. Serverless and on-demand dedicated endpoints get 2 or 3 weeks of notice. Preview models can go with less than 24 hours of notice after 30 days.
The history table shows how often that happens. On October 22, 2026, meta-llama/Llama-3.3-70B-Instruct-Turbo and a DeepSeek V4 Flash model were removed from serverless. In September, gpt-oss-20b and Gemma 4 31B went. In July, a Qwen3 235B model, Llama 3 8B Instruct Lite and GLM-5.1 were removed.
After removal, Together offers three paths: an on-demand dedicated endpoint billed per minute (where the model supports it), a monthly reserved endpoint, or a newer serverless model. That's reasonable. But if your app was tuned around one model's behaviour, a few weeks is a short time to re-test prompts, evals and costs. That's when people start comparing.
The Best Together AI Alternatives
Fireworks AI: the closest all-round match
Best for: teams running open models in production who want many ways to trade speed against cost.
Fireworks AI bills serverless requests on input, cached input and output tokens. Batch jobs get 50% off input and output, and on-demand GPUs bill per GPU-second, with startup time not charged. It also offers managed fine-tuning and dedicated training. In March 2026 it bought Hathora, a global container orchestration platform.
Pick it if you want a Together-shaped platform with fine-tuning and dedicated capacity, plus a cheap batch lane for jobs that can wait. Skip it if you only need a handful of calls a day. Any per-token provider will do.
Baseten: dedicated deployments of your own model
Best for: teams who want a specific model, or their own fine-tune, on dedicated GPUs they control.
Baseten's pricing covers Model APIs billed per token, dedicated inference billed per minute of GPU time, training on on-demand GPUs, and sandboxes for agent environments. Deployments are packaged with Truss, an MIT-licensed CLI that handles containers, dependencies and GPU settings for vLLM, TensorRT-LLM, transformers or PyTorch models.
Pick it if model retirements are your problem. On a dedicated deployment, the model stays as long as you keep paying for it. Skip it if you mainly want to try dozens of models casually. Baseten's strength is running a chosen model well, not browsing a catalog.
DeepInfra: plain per-token access
Best for: low-cost access to many open models with no contract.
DeepInfra offers an OpenAI-compatible chat completions API. Switching takes three changes: base URL, API key and model name. Its docs describe priority requests at a 50% surcharge and flex requests at a 20% discount on the standard per-token price.
Pick it if you mainly use Together's serverless endpoints and want a similar catalog at low prices. Skip it if you need fine-tuning, GPU clusters or post-training in the same account.
Cerebras and Groq: speed over choice
Best for: latency-sensitive apps like voice agents and real-time coding tools.
Cerebras runs inference on its wafer-scale chips through an API it calls "mostly compatible" with OpenAI's client libraries. The catalog is curated, and Cerebras inference is also available through partners including AWS Marketplace, OpenRouter, Hugging Face and Vercel.
Groq is the other speed option, with one big caveat. Groq's model list now shows gpt-oss-120b and gpt-oss-20b as its self-serve production chat models, while its Llama models are marked for enterprise pricing. If you're leaving Together because a Llama model was retired, Groq may not have it either. Our Groq alternatives guide covers those retirements in detail.
Pick these if tokens per second matter more than catalog size. Skip them if you need a long tail of models or fine-tuning.
OpenRouter: hedge across providers
Best for: teams who never want one provider's deprecation to break their app.
OpenRouter puts many providers behind one API. Its FAQ says it passes through provider prices with no markup on inference and charges a fee when you buy credits. If a provider returns an error, OpenRouter automatically falls back to the next one. The :floor suffix sorts providers by price, and :nitro sorts by throughput.
Pick it if you want automatic fallback when one provider drops a model or goes down. Skip it if you need fine-tuning or dedicated capacity. OpenRouter routes requests, it doesn't host models. We compared routers in our OpenRouter alternatives guide.
Hugging Face Inference Providers: open models through one token
Best for: teams who already pick models on the Hugging Face Hub.
Hugging Face Inference Providers route requests to partners including Together, Fireworks, Groq, Cerebras, DeepInfra, Baseten and others, through one Hugging Face token. The docs say it's pay-as-you-go with no extra markup on provider rates, and it offers a drop-in OpenAI-compatible endpoint for chat. Add :fastest, :cheapest or a provider name to the model ID to control routing.
Pick it if you want to try a new open model the day it lands on the Hub without signing up with each host. Skip it if you need non-chat tasks through the OpenAI endpoint; those use Hugging Face's own clients.
Modal: run your own code
Best for: custom pipelines, unusual models, or anything a shared catalog doesn't serve.
Modal is a serverless platform for your own Python code, including GPU workloads. It meters GPUs, CPUs and memory per second and autoscales with request volume. Billable time includes app load time and a default 60-second keep-alive after the last input.
Pick it if you'd rather serve your own vLLM or custom model than depend on anyone's model list. Skip it if you want a wide model catalog with no code. Modal now lists a short set of per-token model APIs, but its strength is serving code you write and deploy.
RunPod Serverless: GPUs that scale to zero
Best for: teams who want raw GPU capacity for their own containers at per-second rates.
RunPod Serverless bills from when a worker starts until it fully stops, rounded up to the nearest second. Flex workers scale to zero, while active workers run around the clock. Its vLLM workers expose OpenAI-compatible /chat/completions, /completions and /models endpoints, so existing OpenAI client code works with a new base URL and key.
Pick it if you want to pin an exact model version on GPUs you control. Skip it if cold starts are a problem and you don't want to pay for always-on workers.
For image, video and audio models rather than LLMs, see our fal.ai alternatives and Replicate alternatives guides.
Where Together AI Is Still the Right Call
Stay on Together if:
- You use more than inference. Serverless, dedicated endpoints, fine-tuning, post-training and GPU clusters all live in one account and one bill.
- You want frontier open models early. Together's catalog is wide and turns over quickly, which is the upside of the deprecation pace.
- You can absorb model changes. If your evals are automated and you pin replacements quickly, a 2-week window is workable.
- You'd move to dedicated anyway. Together keeps retired models available on on-demand dedicated endpoints where supported, so you can stay on an old model while you test a new one.
How to Choose a Together AI Alternative
| Your reason for leaving | Look at | Why |
|---|---|---|
| A serverless model you rely on was retired | Baseten, RunPod or Modal | You control how long the model runs |
| Want the same platform shape | Fireworks AI | Serverless, batch, dedicated and training |
| Want lower per-token cost | DeepInfra | Simple per-token pricing |
| Need much faster responses | Cerebras, Groq | Custom chips, curated models |
| Want no single point of failure | OpenRouter, Hugging Face | Automatic routing across providers |
| Need custom serving code | Modal | Your code, per-second compute |
Before you switch, run the same test set on two or three candidates. The same open model can behave differently across hosts because of quantization, context limits and default parameters. Compare quality, latency and cost on your own prompts, not on a leaderboard.
Where Taku Fits
Taku isn't an inference provider, and it doesn't replace Together AI or anything on this list. It helps with a different problem: you'd rather run a finished AI tool than wire up endpoints and keys yourself.
That's where Taku comes in. Taku Apps and Sites, built by the best AI minds. Find one, run it, and keep it in your Mac Dock. Taku is in Beta, and the Mac app is available now.
FAQ
What is the best Together AI alternative?
Fireworks AI is the closest all-round match, with serverless, batch, dedicated capacity and fine-tuning. If you only need cheap per-token access, DeepInfra is simpler. If you need speed, look at Cerebras.
Why did my Together AI model stop working?
Together removes serverless models on a published schedule, usually with 2 or 3 weeks of notice. Check its deprecations page. You can move to the recommended replacement or, where supported, run the old model on a dedicated endpoint.
Is Together AI OpenAI-compatible?
Mostly. Together says its API is compatible with the OpenAI REST API and SDKs for chat, completions, embeddings, images and speech. Most alternatives here are too, including DeepInfra, Cerebras, Groq, OpenRouter, Hugging Face's router and RunPod's vLLM workers. Switching is usually a change of base URL, API key and model name.
Which Together AI alternative is cheapest?
It depends on the model and traffic. Per-token providers like DeepInfra suit spiky traffic. Batch lanes, like Fireworks' half-price batch, suit jobs that can wait. Dedicated GPUs only pay off at steady high volume.
Can I avoid model deprecations entirely?
Not on any shared serverless catalog. To control the timeline, run the model yourself on dedicated GPUs through Baseten, Modal or RunPod, or route through OpenRouter so another provider can take over.