Together AI Review 2026: Pricing, Models, Pros & Cons
Together AI built the largest catalog of hosted open-source models in one inference platform — 100+ models, fine-tuning support, and OpenAI-compatible APIs at prices well below the major cloud providers. Here's an honest look at what Together AI actually delivers in 2026, where it lags behind competitors, and when it's the right call over Groq or Fireworks.
Quick Verdict
Best for: Teams that need access to a wide range of open-source models, fine-tuning on custom data, or a cost-effective OpenAI alternative for production workloads that aren't latency-critical. If your primary constraint is raw inference speed, Groq is faster. If you need frontier model capability (GPT-4o reasoning), you'll still want OpenAI or Anthropic. For everything in between — model experimentation, domain-adapted models, and cost-conscious production inference — Together AI is a strong default.
What Is Together AI?
Together AI is an AI research and infrastructure company founded in 2022 by researchers from Stanford, CMU, ETH Zurich, and major AI labs. Its core product is a hosted inference platform that runs 100+ open-source large language models, multimodal models, and embedding models via an OpenAI-compatible REST API — effectively a single integration point for the entire open-source model ecosystem.
Beyond shared inference, Together AI offers fine-tuning (upload your data, train a custom model, deploy to your private endpoint), dedicated deployments (reserved GPU capacity for production SLAs), and batch inference (async offline processing at 50% cost reduction). The platform targets developers and enterprises who want the flexibility of open-source models without the infrastructure overhead of self-managed GPU clusters.
In 2026, Together AI has established itself as the default platform for teams doing serious open-source model work — particularly those running model evaluations across many architectures, building fine-tuned vertical applications, or needing a reliable OpenAI fallback that isn't vendor-locked to proprietary weights.
Together AI Pros & Cons
✓ Pros
- •Widest open-source model selection in the market: Together AI hosts 100+ models including Llama 3.1 8B/70B/405B, Mixtral 8x7B/8x22B, Qwen 2.5 7B/72B, Gemma 2 9B/27B, DeepSeek V3/R1, Falcon, Mistral, DBRX, and many more — if you're evaluating which open model performs best for your specific use case, Together AI is the only provider with enough coverage to run a meaningful head-to-head test without switching platforms
- •Fine-tuning on your own data, hosted in the cloud: Together AI's fine-tuning API lets you upload a dataset (JSONL format, chat or completion style), kick off a fine-tuning run, and deploy the resulting checkpoint without managing any GPU infrastructure; fine-tuned models can be accessed via the same API endpoint as base models — this is the key differentiator against Groq and Fireworks, which run pre-trained weights only
- •OpenAI-compatible API reduces integration friction: Together AI's REST API matches OpenAI's chat completions format exactly — the same request body, streaming protocol, and response schema; swapping the base URL and API key is all that's required for most Python/JS SDKs; teams migrating from OpenAI or building provider-agnostic apps can add Together AI as a fallback or primary option with minimal engineering effort
- •Competitive pricing on high-quality models: Together AI prices Llama 3.3 70B at $0.88/1M input tokens and DeepSeek V3 at $0.27/1M — both cheaper than OpenAI GPT-4o at $2.50/1M input while offering comparable quality on most real-world tasks; batch inference discounts of up to 50% are available for offline workloads that don't need real-time latency, making large-scale data processing cost-effective
- •Dedicated deployment for production isolation: Beyond the shared inference pool, Together AI offers dedicated endpoints where your requests run on reserved GPU capacity — no cold starts, predictable latency, and guaranteed throughput; enterprises running compliance-sensitive workloads or applications that need SLA-backed response times use dedicated deployments to avoid the variability of shared infrastructure
- •Strong research pedigree and model quality guarantees: Together AI was founded by AI researchers from Stanford, CMU, and industry labs; the company has contributed to open research (Together Compute, RedPajama dataset) and maintains close relationships with model providers; this means Together AI typically has early access to new open-source releases and maintains clean, well-tested model weights rather than unofficial community checkpoints
✗ Cons
- •Slower raw inference than Groq on equivalent models: Together AI runs models on GPU clusters while Groq uses custom LPU hardware — on Llama 3.3 70B, Groq achieves 300–800 tokens/second versus Together AI's 60–120 tokens/second; for real-time voice AI, streaming chat, or interactive coding assistants where sub-200ms time-to-first-token matters, Groq is noticeably faster; Together AI is better suited for applications where quality and model selection matter more than raw speed
- •Free tier is limited: Together AI's free tier offers $1 in starting credits (typically enough for a few thousand inference calls) with no ongoing free allocation; Groq's free tier includes 14,400 requests per day indefinitely with no credit card required; for developers who want to prototype without immediately spending money, this is a meaningful disadvantage — you'll hit the free credit limit quickly during active development
- •Fine-tuning costs add up at scale: Fine-tuning on Together AI charges per GPU-hour (roughly $3–7/hr depending on model size) plus storage costs for the resulting checkpoint; a typical Llama 3.3 70B fine-tuning run on a medium dataset takes 4–12 hours and costs $40–100+; for teams running continuous fine-tuning pipelines or experimenting with many dataset variants, this can add up faster than self-managed GPU infrastructure at scale
- •Model catalog size creates selection friction: 100+ hosted models is a strength for coverage but a challenge for production decision-making — it's not immediately obvious which model variant to choose for a given task, and Together AI's model card documentation varies in quality; teams without ML engineering experience may struggle to evaluate tradeoffs between Mixtral 8x7B, DBRX, and Qwen 2.5 72B without running their own benchmarks
- •Rate limits on standard tier require active management: Together AI's pay-as-you-go tier has rate limits that can surprise teams scaling from development to production; burst traffic during peak hours can hit token-per-minute caps, requiring backoff logic and retry handling; teams running high-concurrency applications or batch evaluation pipelines need to request rate limit increases proactively or architect around limit behavior from the start
- •Enterprise pricing is opaque without sales engagement: Dedicated deployments and enterprise SLAs require going through a sales process — pricing isn't publicly listed for dedicated GPU capacity or high-volume discounts; teams evaluating Together AI for large production deployments need to engage sales before they can accurately model costs, which adds friction to procurement processes where self-serve pricing transparency matters
Together AI Pricing 2026
Pay-as-you-go
- •Llama 3.1 8B: $0.10/$0.10 in/out per 1M
- •Llama 3.3 70B: $0.88/$0.88 in/out per 1M
- •DeepSeek V3: $0.27/$1.10 in/out per 1M
- •No monthly minimum
- •OpenAI-compatible API
Teams with variable inference workloads who want access to 100+ models
Batch Inference
- •Async job queue (not real-time)
- •50% discount on standard pricing
- •All hosted models eligible
- •JSONL file input/output
- •Results via webhook or polling
Data processing, evaluation pipelines, and offline annotation tasks
Fine-Tuning
- •JSONL dataset upload
- •Chat and completion formats
- •LoRA and full fine-tuning
- •Hosted checkpoint deployment
- •Evaluation on held-out data
Domain adaptation, instruction tuning, and custom model development
Dedicated Deployments
- •Reserved GPU capacity
- •Guaranteed throughput
- •SLA-backed uptime
- •Private endpoint
- •Enterprise support
Production applications needing predictable latency and compliance
Note: Prices vary by model; input and output tokens are often priced the same on Together AI unlike providers that charge more for output. Batch inference applies a 50% discount on eligible models. Check together.ai/pricing for current rates.
Together AI vs Groq vs Fireworks AI
| Feature | Together AI | Groq | Fireworks AI |
|---|---|---|---|
| Model selection | ✅ 100+ models | ⚠️ ~15 curated models | ✅ 50+ models |
| Inference speed (70B) | ⚠️ 60–120 tok/s | ✅ 300–800 tok/s | ⚠️ 80–150 tok/s |
| Fine-tuning | ✅ LoRA + full FT | ❌ Not supported | ✅ LoRA |
| Llama 3.3 70B price (input) | ⚠️ $0.88/1M | ✅ $0.59/1M | ✅ $0.90/1M |
| OpenAI-compatible API | ✅ Yes | ✅ Yes | ✅ Yes |
| Free tier | ⚠️ $1 credit | ✅ 14.4K req/day free | ⚠️ $1 credit |
| Dedicated deployments | ✅ Yes | ⚠️ Enterprise only | ✅ Yes |
| Batch inference discount | ✅ Up to 50% off | ⚠️ Limited | ✅ Available |
Frequently Asked Questions
Is Together AI better than Groq?
It depends on your use case. Groq is significantly faster — 300–800 tokens/second versus Together AI's 60–120 tokens/second on Llama 3.3 70B — making Groq the better choice for real-time applications like voice AI, streaming chat, and interactive coding assistants. Together AI is better when you need model variety (100+ models vs. Groq's ~15), fine-tuning on custom data, dedicated deployments with guaranteed throughput, or access to newer/less-common model architectures. For most production applications that aren't latency-critical, Together AI's model breadth and fine-tuning capability outweigh the speed gap.
What models does Together AI support in 2026?
Together AI hosts 100+ open-source models as of 2026, including the full Llama 3.x family (8B, 70B, 405B), Mixtral 8x7B and 8x22B, Qwen 2.5 (7B through 72B), Gemma 2 (9B, 27B), DeepSeek V3 and R1, Falcon 180B, DBRX, Mistral 7B and Mixtral variants, Code Llama, and many more. The catalog is updated regularly as new open-source releases emerge. Together AI also supports Whisper variants for speech-to-text and several embedding models for vector search workloads.
How does Together AI fine-tuning work?
Together AI's fine-tuning API accepts datasets in JSONL format — each line is a JSON object with a 'messages' array (for chat models) or 'prompt'/'completion' fields (for base models). You upload the dataset file via API, specify the base model and hyperparameters (learning rate, epochs, batch size), and start a fine-tuning job. Jobs are billed per GPU-hour of training time. Once complete, the fine-tuned checkpoint is deployed as a private model accessible via your API key at a standard inference endpoint. LoRA fine-tuning is supported for most models, with full fine-tuning available for smaller models.
Is Together AI good for production use?
Yes, with some caveats. Together AI's shared inference tier is production-suitable for most applications, with pay-as-you-go billing and reasonable rate limits. For latency-sensitive or high-throughput production workloads, dedicated deployments provide reserved GPU capacity with SLA guarantees. The main considerations: if you need sub-200ms inference latency, Groq is faster; if you need 99.9%+ uptime SLAs without enterprise contracts, plan for dedicated deployments from the start. Together AI is a strong production choice for applications where model quality or model selection matters more than raw speed.
How does Together AI pricing compare to OpenAI?
Together AI is significantly cheaper than OpenAI for equivalent model quality on most tasks. DeepSeek V3 on Together AI costs $0.27/1M input tokens versus GPT-4o at $2.50/1M — about 9x cheaper. Llama 3.3 70B at $0.88/1M is approximately 3x cheaper than GPT-4o while delivering competitive quality on non-frontier tasks. Batch inference adds another 50% discount for offline workloads. The tradeoff: OpenAI's GPT-4o still outperforms open models on complex reasoning, creative writing, and instruction-following edge cases; for tasks where Llama 3.3 70B or DeepSeek V3 is sufficient, Together AI offers compelling cost savings.
Explore AI Inference Alternatives
See how Together AI stacks up against Groq, Fireworks AI, Replicate, and every other open-source inference option.
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