TokenMix Research Lab · 2026-04-10
Together AI Review: Inference, Fine-Tuning, and GPU Clusters at $0.88/M for Llama 70B (2026)
Last Updated: 2026-04-29
Author: TokenMix Research Lab
Together AI: 200+ models at $0.88/M Llama 70B, 99.7% uptime, end-to-end fine-tuning. Pricier than Groq ($0.59) but 13x more models. Cheaper than AWS Bedrock ($2.65) by 67%. Sweet spot for teams that need open-source models + fine-tuning pipeline.
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 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 capabilities, and how it compares to Groq and Fireworks AI for production workloads.
Table of Contents
- Quick Comparison: Together AI vs Groq vs Fireworks
- Why Together AI Matters for Open-Source Model Deployment
- Together AI Product Overview
- Together AI Pricing Breakdown
- Performance Benchmarks: Speed and Throughput
- Fine-Tuning on Together AI
- GPU Clusters and Dedicated Instances
- Cost Analysis for Different Workloads
- Which Provider Should You Pick?
- What's the Bottom Line on Together AI?
- FAQ
Quick Comparison: Together AI vs Groq vs Fireworks
Together: 200+ models, fine-tuning, dedicated GPUs ($0.88/M Llama 70B). Groq: 15-20 models, fastest (300-500 tok/sec), no fine-tuning ($0.59). Fireworks: 50+ models, best function calling, 99.8% uptime ($0.90).
| 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
Sweet spot between hyperscalers (40-60% cheaper than AWS/GCP/Azure) and Groq (more models, fine-tuning, dedicated GPUs). 2.3B inference requests in Q1 2026, 99.7% uptime. Default for teams running open-source at scale.
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, 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
Three product layers: serverless inference (200+ models, OpenAI-compatible API, batch 30-50% off), fine-tuning platform (LoRA + full param, $4.50-$22/M training tokens), dedicated GPU instances (A100 $3.50/hr, H100 $5.50/hr).
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 and function calling 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
Llama 70B at $0.88/M = 67% cheaper than AWS Bedrock ($2.65). Versus Groq ($0.59) Together is 49% pricier but adds fine-tuning + 200+ models. No egress fees (vs hyperscalers). Batch API drops costs 30-50% for async jobs.
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
- Rate limits: Free tier is heavily rate-limited (60 requests/minute). Paid tier scales to 600 requests/minute, enterprise tier higher.
- Batch pricing: 30-50% discount for async batch jobs with 24-hour turnaround. Excellent for evaluation pipelines and data processing.
- Fine-tuned model hosting: Hosting a fine-tuned model on a serverless endpoint costs the same per-token as the base model. Dedicated endpoint hosting is billed hourly.
- Egress fees: None. Unlike hyperscalers, Together AI does not charge for data transfer.
- Minimum spend: None for serverless. Dedicated instances have a minimum 1-hour commitment.
Performance Benchmarks: Speed and Throughput
Llama 70B P50 TTFT: Together 220ms, Groq 65ms, Fireworks 150ms. Throughput: Together 95 tok/sec vs Groq 420. Together adequate for chatbots/APIs but Groq dominates real-time. Reliability: Fireworks 99.8% > Together 99.7% > Groq 99.4%.
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 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
Cheapest managed Llama fine-tuning: $14/M training tokens for 70B LoRA (vs OpenAI $25, Mistral $20). Full param 70B = $22/hour on 8xH100. Upload JSONL → automatic deployment to serverless endpoint. Groq doesn't offer fine-tuning at all.
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 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
Break-even: ~130-150M tokens/day for Llama 70B. Below = serverless wins. Above = dedicated H100 ($5.50/hr) wins. 8xH100 cluster $44/hr enables full fine-tuning. Reserved capacity = guaranteed throughput, no rate limits.
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/day: Together $26 vs Groq $18 vs Fireworks $27. Growth 50M/day: $1,320 vs $890 vs $1,350 (Groq wins if models supported). Enterprise 500M/day: dedicated $3,960 beats serverless. TokenMix.ai routing saves 20-35% across providers.
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.
Which Provider Should You Pick?
Cheapest inference (limited models): Groq. Large catalog + fine-tuning: Together. Lowest latency: Groq. Function calling reliability: Fireworks. Production uptime: Fireworks. Multi-provider routing: TokenMix.ai.
| 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 |
What's the Bottom Line on Together AI?
Swiss Army knife of open-source inference. Strongest fine-tuning pipeline at lowest price. Default for production AI on Llama/Mixtral/Qwen. Multi-provider strategies should add it alongside Groq (speed) and Fireworks (reliability) via TokenMix.ai routing.
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, Groq Pricing, Fireworks AI Pricing + TokenMix.ai