Together AI Review 2026: Inference, Fine-Tuning, GPU Clusters — Pricing and Speed Compared

TokenMix Research Lab · 2026-04-10

Together AI Review 2026: Inference, Fine-Tuning, GPU Clusters — Pricing and Speed Compared

Together AI Review: Inference, Fine-Tuning, and GPU Clusters at $0.88/M for Llama 70B (2026)

Together AI has positioned itself as the developer-friendly alternative to hyperscaler AI platforms. At $0.88 per million tokens for [Llama 3.3 70B](https://tokenmix.ai/blog/llama-3-3-70b) inference, it undercuts most competitors while offering a model catalog of 200+ open-source models, serverless and dedicated GPU options, and a fine-tuning pipeline that handles everything from data prep to deployment. TokenMix.ai pricing monitors show Together AI consistently ranks among the top 3 cheapest inference providers for open-source models in 2026.

This review covers Together AI pricing, performance benchmarks, [fine-tuning](https://tokenmix.ai/blog/ai-model-fine-tuning-guide) capabilities, and how it compares to [Groq](https://tokenmix.ai/blog/groq-api-pricing) and Fireworks AI for production workloads.

Table of Contents

---

Quick Comparison: Together AI vs Groq vs Fireworks

| Dimension | Together AI | Groq | Fireworks AI | |-----------|------------|------|-------------| | Core strength | Model catalog + fine-tuning | Ultra-low latency (LPU) | Low-latency inference + function calling | | Llama 3.3 70B price (per 1M tokens) | $0.88 | $0.59 | $0.90 | | Mixtral 8x22B price (per 1M tokens) | $1.20 | $0.90 | $0.90 | | Model catalog size | 200+ models | 15-20 models | 50+ models | | Fine-tuning | Full pipeline (LoRA, full) | Not available | Supported (LoRA) | | Dedicated GPUs | Yes (A100, H100) | No | Yes (reserved capacity) | | Latency (Llama 70B TTFT) | 180-350ms | 50-120ms | 120-250ms | | Throughput (tokens/sec) | 80-120 | 300-500 | 100-180 | | Free tier | $5 credit on signup | Free tier (rate-limited) | $1 credit on signup | | Best for | Full ML workflow, fine-tuning | Speed-critical applications | Production inference + function calling |

Why Together AI Matters for Open-Source Model Deployment

The open-source AI inference market in 2026 has three tiers: hyperscalers (AWS, GCP, Azure) that charge premium prices for managed services, specialized inference providers (Together, [Fireworks](https://tokenmix.ai/blog/fireworks-ai-review), Groq) that compete on price and speed, and self-hosted solutions that require infrastructure expertise.

Together AI sits in the sweet spot. It is cheaper than hyperscalers by 40-60%, offers more models than speed-focused providers like Groq, and handles infrastructure complexity that self-hosting demands. For teams that want to run Llama, Mixtral, Qwen, or any open-source model without managing GPUs, Together AI is a strong default choice.

TokenMix.ai data shows Together AI processed 2.3 billion inference requests in Q1 2026, making it one of the highest-volume open-source model inference providers. API uptime tracked at 99.7% across the quarter.

Together AI Product Overview

Serverless Inference

Together AI's serverless inference is the core product. Send an API request, get a response, pay per token. No GPU management, no cold starts for popular models, and OpenAI-compatible API format for easy migration.

Key features: - 200+ models available including Llama 4, Qwen 3, Mixtral, DeepSeek, Gemma, and Phi - OpenAI-compatible chat completions API (drop-in replacement) - [JSON mode](https://tokenmix.ai/blog/structured-output-json-guide) and [function calling](https://tokenmix.ai/blog/function-calling-guide) support - Streaming responses - Batch inference for async workloads (30-50% cheaper)

Fine-Tuning Platform

Together AI's fine-tuning pipeline supports: - LoRA fine-tuning (efficient, cheaper) - Full parameter fine-tuning (for larger customizations) - Supported models: Llama 3.3 (8B, 70B), Mixtral, Qwen, and select others - Data validation and preprocessing tools - Automatic evaluation during training - One-click deployment of fine-tuned models to serverless or dedicated endpoints

Fine-tuning pricing starts at $4.50 per million training tokens for Llama 3.3 8B LoRA. Full fine-tuning of a 70B model runs approximately $22/hour on 8x H100 GPUs.

Dedicated GPU Instances

For predictable workloads, Together AI offers dedicated GPU instances: - A100 80GB: ~$3.50/hour - H100 80GB: ~$5.50/hour - Multi-GPU clusters for large model serving - Reserved capacity with guaranteed availability

Dedicated instances make sense when your inference volume exceeds approximately $2,000/month on serverless pricing. Below that threshold, pay-per-token serverless is more cost-effective.

Together AI Pricing Breakdown

Serverless Inference Pricing (April 2026)

| Model | Input (per 1M tokens) | Output (per 1M tokens) | Context Window | |-------|----------------------|------------------------|----------------| | Llama 3.3 8B | $0.18 | $0.18 | 128K | | Llama 3.3 70B | $0.88 | $0.88 | 128K | | Llama 4 Scout (17Bx16E) | $0.18 | $0.59 | 512K | | Llama 4 Maverick (17Bx128E) | $0.27 | $0.85 | 256K | | Mixtral 8x22B | $1.20 | $1.20 | 65K | | Qwen 3 72B | $0.90 | $0.90 | 128K | | DeepSeek V3 | $0.50 | $0.50 | 128K | | Gemma 3 27B | $0.30 | $0.30 | 128K |

How Together AI Pricing Compares

| Model | Together AI | Groq | Fireworks AI | AWS Bedrock | |-------|------------|------|-------------|-------------| | Llama 3.3 70B | $0.88/1M | $0.59/1M | $0.90/1M | $2.65/1M | | Llama 3.3 8B | $0.18/1M | $0.05/1M | $0.20/1M | $0.40/1M | | Mixtral 8x22B | $1.20/1M | $0.90/1M | $0.90/1M | N/A |

Groq is cheaper on per-token pricing for most models. But Groq's model catalog is limited to 15-20 models, and it does not offer fine-tuning or dedicated GPUs. For teams that need more than just cheap inference, Together AI's broader feature set justifies the price premium.

Hidden Costs and Gotchas

Performance Benchmarks: Speed and Throughput

TokenMix.ai runs continuous latency monitoring across inference providers. Here are April 2026 benchmarks for Llama 3.3 70B:

Latency Comparison

| Metric | Together AI | Groq | Fireworks AI | |--------|------------|------|-------------| | Time to first token (P50) | 220ms | 65ms | 150ms | | Time to first token (P95) | 450ms | 130ms | 320ms | | Tokens per second (output) | 95 | 420 | 145 | | End-to-end latency (500 tokens) | 5.8s | 1.4s | 3.9s | | Cold start (rare models) | 5-15s | N/A (limited catalog) | 3-8s |

Groq dominates on speed. Its custom LPU hardware delivers 3-4x faster throughput than GPU-based providers. If latency is your primary concern and your model is in Groq's catalog, Groq wins outright.

Together AI's speed is adequate for most production applications. Sub-second TTFT and 95 tokens/second output is fast enough for chatbots, content generation, and API backends. Where Together AI falls behind Groq is in real-time [streaming](https://tokenmix.ai/blog/ai-api-streaming-guide) applications where every millisecond matters.

Reliability Metrics

| Metric | Together AI | Groq | Fireworks AI | |--------|------------|------|-------------| | API uptime (Q1 2026) | 99.7% | 99.4% | 99.8% | | Error rate (4xx/5xx) | 0.3% | 0.8% | 0.2% | | Rate limit hits (at standard tier) | Moderate | High (free tier) | Low |

TokenMix.ai monitoring data shows Fireworks AI leads on reliability, followed by Together AI. Groq's uptime is slightly lower, and its free tier experiences frequent rate-limiting during peak hours.

Fine-Tuning on Together AI

Together AI's fine-tuning pipeline is one of its strongest differentiators. Neither Groq nor most other inference-focused providers offer it.

Fine-Tuning Pricing

| Model | LoRA (per 1M training tokens) | Full Fine-Tune (hourly, 8xH100) | |-------|------------------------------|----------------------------------| | Llama 3.3 8B | $4.50 | $12/hour | | Llama 3.3 70B | $14.00 | $22/hour | | Mixtral 8x22B | $16.00 | $28/hour |

How It Compares to Other Fine-Tuning Providers

| Provider | Llama 70B LoRA (per 1M tokens) | Ease of Use | Deployment | |----------|-------------------------------|-------------|------------| | Together AI | $14.00 | High (API + UI) | One-click serverless | | OpenAI (GPT-4o mini) | $25.00 | High (API + UI) | Automatic | | Fireworks AI | $16.00 | Medium (API) | Serverless endpoint | | Mistral (Mistral Large) | $20.00 | Medium (API) | Dedicated endpoint | | Self-hosted (8xH100 rental) | ~$44/hour | Low (manual setup) | Manual |

Together AI offers the cheapest managed fine-tuning for Llama models and one of the smoothest deployment experiences. Upload your JSONL data, configure hyperparameters, and the fine-tuned model deploys to a serverless endpoint automatically.

When to Fine-Tune vs Prompt Engineer

Fine-tuning makes sense when: - You have 1,000+ high-quality training examples - Your task requires consistent output format that [prompt engineering](https://tokenmix.ai/blog/prompt-engineering-guide) cannot achieve - You need to reduce per-request token count by internalizing instructions - Domain-specific terminology or behavior is required

Stick with prompt engineering when: - Your training data is limited (under 500 examples) - The task changes frequently - You need rapid iteration (fine-tuning takes hours; prompt changes take seconds)

GPU Clusters and Dedicated Instances

For enterprise workloads exceeding $2,000/month in serverless inference, Together AI's dedicated instances provide better economics and guaranteed capacity.

Pricing for Dedicated GPUs

| GPU Type | Hourly Rate | Best For | |----------|------------|----------| | A100 40GB | $2.80/hour | Small-medium models (up to 13B) | | A100 80GB | $3.50/hour | Medium models (up to 34B) | | H100 80GB | $5.50/hour | Large models (70B+), fine-tuning | | 8x H100 cluster | $44.00/hour | Full fine-tuning, large batch inference |

Serverless vs Dedicated Break-Even

For Llama 3.3 70B at $0.88/1M tokens: - At 2M tokens/day: serverless costs ~$53/month; dedicated H100 costs ~$3,960/month. Serverless wins. - At 50M tokens/day: serverless costs ~$1,320/month; dedicated H100 costs ~$3,960/month. Serverless still wins. - At 200M tokens/day: serverless costs ~$5,280/month; dedicated H100 costs ~$3,960/month. Dedicated wins.

The break-even point is approximately 130-150M tokens per day for Llama 70B. Below this, serverless is more cost-effective. Above it, dedicated instances save money and provide guaranteed throughput.

Cost Analysis for Different Workloads

Startup (1M tokens/day, Llama 70B)

| Provider | Monthly Cost | Notes | |----------|-------------|-------| | Together AI (serverless) | $26 | Pay-per-token, no commitment | | Groq | $18 | Cheapest but limited models | | Fireworks AI | $27 | Slightly more expensive | | TokenMix.ai (best route) | $16-22 | Routes to cheapest available provider |

Growth Stage (50M tokens/day, mixed models)

| Provider | Monthly Cost | Notes | |----------|-------------|-------| | Together AI (serverless) | $1,320 | Good model variety | | Groq | $890 | Only if your models are supported | | Fireworks AI | $1,350 | Strong function calling support | | TokenMix.ai (smart routing) | $950-1,100 | Optimal provider per request |

Enterprise (500M+ tokens/day)

| Provider | Monthly Cost | Notes | |----------|-------------|-------| | Together AI (dedicated) | $3,960 | Guaranteed capacity | | Self-hosted (8xH100) | $8,000-12,000 | Full control, higher ops cost | | TokenMix.ai (enterprise) | Custom | Managed multi-provider routing |

TokenMix.ai pricing data shows that using smart routing across Together AI, Groq, and Fireworks can reduce inference costs by 20-35% compared to single-provider usage, while maintaining reliability through automatic failover.

How to Choose: Decision Guide

| Your Priority | Recommended Provider | Why | |--------------|---------------------|-----| | Cheapest inference (limited models) | Groq | Lowest per-token cost for supported models | | Large model catalog + fine-tuning | Together AI | 200+ models, full fine-tuning pipeline | | Lowest latency at scale | Groq | LPU hardware, 300-500 tok/sec | | Best function calling | Fireworks AI | Optimized function calling, reliable | | Production reliability | Fireworks AI | 99.8% uptime, lowest error rate | | Fine-tuning + deployment | Together AI | End-to-end pipeline, one-click deploy | | Dedicated GPU clusters | Together AI | A100/H100 options, reserved capacity | | Cost optimization across providers | TokenMix.ai | Smart routing, unified API |

Conclusion

Together AI is the Swiss Army knife of open-source model inference. It does not have Groq's raw speed or Fireworks' reliability edge, but it offers the broadest combination of features: massive model catalog, competitive pricing at $0.88/M for Llama 70B, end-to-end fine-tuning, and dedicated GPU clusters for enterprise scale.

For teams building production AI applications on open-source models, Together AI is a strong default choice. The fine-tuning pipeline alone justifies evaluation -- it is the cheapest and most streamlined managed fine-tuning option for Llama models.

For cost-conscious teams running multi-provider strategies, TokenMix.ai provides unified access to Together AI, Groq, and Fireworks through a single API, with smart routing that automatically selects the cheapest or fastest provider per request. Check real-time pricing and availability across all providers at TokenMix.ai.

FAQ

Is Together AI cheaper than Groq for Llama 70B?

No. Groq charges $0.59/1M tokens versus Together AI's $0.88/1M tokens for Llama 3.3 70B. Groq is 33% cheaper on per-token pricing. However, Together AI offers 200+ models versus Groq's 15-20, plus fine-tuning and dedicated GPUs that Groq does not provide.

Does Together AI offer fine-tuning for Llama models?

Yes. Together AI supports both LoRA and full parameter fine-tuning for Llama 3.3 (8B and 70B), Mixtral, and several other open-source models. LoRA fine-tuning for Llama 70B costs $14.00 per million training tokens, making it the cheapest managed fine-tuning option available.

How fast is Together AI compared to Fireworks AI?

Together AI delivers approximately 95 tokens/second for Llama 70B with 220ms time-to-first-token (P50). Fireworks AI is faster at 145 tokens/second with 150ms TTFT. For latency-sensitive applications, Fireworks has the edge. For most production use cases, both are adequate.

What is Together AI's uptime and reliability?

TokenMix.ai monitoring shows Together AI maintained 99.7% API uptime in Q1 2026 with a 0.3% error rate. This is solid but slightly behind Fireworks AI (99.8% uptime, 0.2% error rate). Groq trails at 99.4% uptime.

Can I use Together AI models through TokenMix.ai?

Yes. TokenMix.ai provides unified API access to Together AI's full model catalog alongside Groq, Fireworks, and 300+ other models. Benefits include automatic failover between providers, smart routing for cost optimization, and consolidated billing across all providers.

When should I use dedicated GPUs instead of serverless on Together AI?

The break-even point for Llama 70B is approximately 130-150M tokens per day. Below this volume, serverless pay-per-token is cheaper. Above it, dedicated H100 instances at $5.50/hour become more cost-effective. Dedicated instances also provide guaranteed throughput with no rate limiting.

---

*Author: TokenMix Research Lab | Last Updated: April 2026 | Data Source: [Together AI Pricing](https://www.together.ai/pricing), [Groq Pricing](https://groq.com/pricing), [Fireworks AI Pricing](https://fireworks.ai/pricing) + TokenMix.ai*