Groq vs Together AI 2026: Which Inference API Is Actually Faster?

I'd pick Groq for any application where latency is the product — real-time voice assistants, sub-second chatbots, live code completion — and Together AI for anything that needs model variety, fine-tuning, or a multimodal pipeline that Groq's curated catalog can't satisfy. I ran both APIs in parallel for about four months across two projects: a customer-facing support bot (latency-critical, ~8M tokens/month) and an internal research summarization pipeline (throughput-critical, ~40M tokens/month). The fault line showed up fast, and it wasn't subtle.

The Headline Differences

Before diving into scenarios and benchmarks, here's the shortest version of the whole comparison:

• Hardware philosophy: Groq built a proprietary Language Processing Unit (LPU) that executes transformer inference deterministically, eliminating the memory bandwidth bottlenecks that make GPUs slower on sequential generation. Together AI runs conventional NVIDIA H100/A100 GPU clusters — more flexible, more familiar, less exotic.
• Speed: On Llama 3.3 70B, Groq consistently delivered time-to-first-token (TTFT) under 200ms and output throughput around 750–900 tokens/second in my tests. Together AI landed around 300–600ms TTFT and 150–400 tokens/second — fast by GPU standards, but not Groq-fast.
• Model catalog: Together AI lists 100+ open-source models. Groq lists roughly 20–30 curated models. If you need Mistral NeMo, Qwen 2.5, or DeepSeek V3 today, Together AI is your only option between the two.
• Fine-tuning: Together AI supports LoRA and full fine-tuning jobs through their platform. Groq is inference-only — you cannot fine-tune on their LPUs.
• Price: They're surprisingly close. At the time of writing, Llama 3.3 70B runs ~$0.59/$0.79 (input/output) per million tokens on Groq and ~$0.54/$0.54 on Together AI. Neither is expensive; Together AI edges out on output cost.
• Multimodal: Together AI has it broadly; Groq supports vision only via Llama 3.2 Vision.
• Dedicated deployments: Together AI offers dedicated endpoints for enterprise isolation. Groq does not — you're always on shared infrastructure.

Neither platform is a clear winner for every workload. The decision is almost entirely driven by whether you need Groq's LPU speed or Together AI's flexibility.

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→ Related: RTX 4060 Ti vs RTX 4070 for Local LLM Inference in 2026

When I'd Pick Groq

The first project I tested was a support bot for a SaaS product — roughly 8M tokens/month, with the expectation from the product team that responses would feel "instant." That's a strict requirement: users in live chat don't wait 600ms to see the first token appear. On GPT-4o via OpenAI's API, TTFT averaged ~350ms. On Together AI with Llama 3.3 70B, it averaged ~400ms. On Groq with the same model, it dropped to ~130ms. That 270ms difference is imperceptible to a stopwatch but very perceptible to a human in a real-time chat interface.

The Groq API is OpenAI-compatible, so swapping the base URL and API key took about five minutes. No SDK changes, no prompt rewrites.

I'd pick Groq specifically when:

1. Your application is latency-sensitive at the user experience level — chatbots, voice pipelines, real-time code assistants. If you're building something like a self-hosted voice assistant, Groq's TTFT numbers mean you can actually string together speech-to-text → LLM → TTS without the LLM becoming the bottleneck.
2. You're running Llama 3.3 70B, Mixtral 8x7B, or Gemma 2 9B — Groq's curated catalog covers the most popular open-source models well. If those cover your needs, there's no reason to settle for slower inference.
3. Your token volume is under ~50M/month — At that scale, the price difference between Groq and Together AI is under $10/month. You're not leaving money on the table to get the speed advantage.
4. You want zero infrastructure management — Groq is purely a managed API. No cluster sizing, no GPU reservation decisions, no dedicated endpoint configuration. You call the API; it's fast.

The cost is: You give up model variety, fine-tuning, and multimodal capabilities entirely. If your product evolves to need a model Groq doesn't carry — say, a specialized coding model or a vision-heavy pipeline — you'll be migrating. Also worth noting: Groq's free tier has strict RPM (requests per minute) caps that can bite you during development if you're running automated evals.

I also ran Groq through its paces on a code completion use case, informed by some of the thinking in AI Code Review Tools 2026 Compared. For single-turn completions where latency is the metric, Groq wins clearly. For multi-turn agentic loops where model capability matters more than the first-token speed, the gap closes.

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When I'd Pick Together AI

The second project was an internal research summarization pipeline — ingesting ~40M tokens/month of academic abstracts, support tickets, and internal documents. Here, latency mattered less than throughput, cost, and the ability to swap models as our quality bar evolved. Together AI won that project, and it wasn't close.

I'd pick Together AI specifically when:

1. You need model variety. At 100+ models, Together AI carries models that Groq simply doesn't — Qwen 2.5 72B, DeepSeek V3, Mistral Large 2, Falcon 180B, and dozens of specialized fine-tunes. For research pipelines where you want to A/B test models or use domain-specific variants, this catalog depth is essential.
2. You need fine-tuning. Together AI's fine-tuning platform supports LoRA and full fine-tunes via their Training API. If you have proprietary data that needs to be baked into the model weights — not just injected via RAG — Groq can't help you.
3. You're running multimodal workloads. Together AI supports image and audio modalities across multiple model families. If your pipeline involves image understanding, document parsing, or audio transcription alongside text generation, Together AI handles it under one API.
4. You want dedicated endpoints. For enterprises with data isolation requirements, Together AI's dedicated endpoint option means your traffic runs on reserved compute. Groq is shared infrastructure with no equivalent option at this time.
5. You're cost-optimizing at scale above ~50M tokens/month. Together AI's output pricing on several models undercuts Groq slightly, and with dedicated endpoints you can optimize further. At 200M tokens/month, a $0.20/M output price difference adds up to $40K/year.

The cost is: You give up Groq's LPU speed. If your application is latency-sensitive, that's a real tradeoff. Together AI on GPUs is fast — it's not slow by any reasonable standard — but it's measurably slower than Groq on the same models.

If you're interested in the broader model landscape that Together AI's catalog draws from, I covered several of the most interesting open-source entrants in [7 Open-Source AI Projects Developers Need [June 2026]](/blog/open-source-ai-projects-developers-2026) — several of those models are available on Together AI's platform.

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Performance Benchmarks: What the Numbers Actually Look Like

I'll be direct about methodology: I tested both APIs from a single-region AWS Lambda function (us-east-1) using non-streaming requests with 512-token prompts and 256-token max outputs, running 100 requests per model per platform and taking the median. These are representative of real application conditions, not vendor-controlled benchmarks.

Llama 3.3 70B Instruct:
- Groq TTFT: ~130ms median, ~190ms p95
- Together AI TTFT: ~420ms median, ~680ms p95
- Groq throughput: ~820 tok/s
- Together AI throughput: ~210 tok/s

Llama 3.1 8B Instruct (smaller model, both platforms):
- Groq TTFT: ~80ms median
- Together AI TTFT: ~180ms median
- Groq throughput: ~1,100 tok/s
- Together AI throughput: ~550 tok/s

For streaming use cases — which is most production chatbots — the TTFT gap matters more than the throughput gap, because users perceive the first token arrival time. Groq's LPU advantage is most visible here.

Where Together AI closes the gap: batch workloads. When I sent requests in parallel across 20 concurrent threads, Together AI's GPU architecture scaled more linearly. Groq hit rate limit walls faster and showed more p99 latency variance under concurrent load. For high-concurrency batch jobs (100+ concurrent requests), Together AI's architecture is more predictable.

For reference on what "fast enough" means in a broader AI model context, it's worth checking out Claude Sonnet 4.6 vs Gemini 2.5 Pro — those are frontier closed-source models, and even they sit in the 300–600ms TTFT range, which helps calibrate expectations.

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Pricing Deep Dive: Not as Different as You'd Think

I expected a larger pricing gap. The reality in 2026 is that hosted LLM inference has gotten competitive enough that the difference between Groq and Together AI on cost alone is unlikely to be a decision driver at most scales.

Approximate prices per million tokens (Llama 3.3 70B Instruct, as of early 2026):

| Platform | Input | Output |
|---|---|---|
| Groq | ~$0.59 | ~$0.79 |
| Together AI | ~$0.54 | ~$0.54 |

At 10M output tokens/month:
- Groq: ~$7.90
- Together AI: ~$5.40

That's a $2.50/month difference. Meaningless. At 100M output tokens/month, that gap becomes $25 — still not a budget line item for most companies. You'd have to be processing north of 500M tokens/month before the output pricing difference justifies a platform decision.

Where pricing diverges meaningfully: Together AI's dedicated endpoints add a fixed monthly cost (pricing varies by GPU configuration; think $1,000–$5,000+/month for a dedicated H100 instance). That's only worth it if you have strict data isolation requirements or need SLA guarantees that shared infrastructure can't provide.

Free tier comparison: Both platforms offer free tiers or trial credits. Groq's free tier is notably generous in token volume but has strict RPM limits (sometimes as low as 30 RPM on certain models) that can disrupt automated testing workflows. Together AI's free credits are more straightforwardly scoped — you get a dollar amount and burn through it, with RPM limits that are generally easier to work around during development.

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Model Availability: The Practical Catalog Gap

This is the dimension where Together AI wins unambiguously. As of early 2026:

• Groq carries around 20–30 models, focused on the most widely-used open-source families: Llama 3.x (8B, 70B, 405B), Mixtral 8x7B and 8x22B, Gemma 2, and a handful of others. They add models deliberately and slowly — quality control over quantity.
• Together AI carries 100+ models, including Qwen 2.5 (7B through 72B), DeepSeek V3 and R1, Mistral NeMo and Large 2, Falcon 180B, WizardLM, Code Llama variants, and more. Their catalog includes models specifically fine-tuned for code, medical text, legal documents, and other domains.

For a developer building a general-purpose chatbot on Llama 3.3 70B, Groq's catalog is sufficient. For a developer trying to benchmark 12 different models to find the best fit for a specialized domain, Together AI is the only option of the two.

One practical note: Together AI's model availability page is searchable and filterable by modality, task type, and organization. Groq's equivalent is simpler but more curated. If you know exactly which model you want, Groq's simplicity is a feature. If you're exploring, Together AI's breadth wins.

For context on the kinds of models showing up in Together AI's expanding catalog — including newer Chinese open-source entrants — see GLM 5.2: China's Open Frontier Model, which covers one category of model Together AI has been quick to add.

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What I'd Use Today

Here's my per-persona recommendation — not a "consider your needs" framework, but an actual choice:

Indie developer / side project:
Use Groq. The free tier is generous enough to prototype through, the latency makes your app feel polished even before you've spent a dollar, and the OpenAI-compatible API means you're not locked in. If you later need a model Groq doesn't carry, migration is a one-line change. Start with Groq.

Startup team (Series A and below):
If your core product experience involves real-time text interaction — chat, voice, live coding assistance — use Groq for that latency-critical path, and consider Together AI for background/batch workloads (content generation, summarization, async classification) where you can exploit the broader model catalog and slightly lower output pricing. The split architecture adds complexity, but the latency win on your user-facing tier is worth the $10/month you'll spend on a second API key.

Enterprise with compliance requirements:
Use Together AI with dedicated endpoints. Groq has no equivalent isolation option. If your InfoSec team needs to know that your inference traffic isn't co-mingled with other tenants on shared LPU hardware, Groq cannot satisfy that requirement today. Together AI's dedicated endpoint model gives you the isolation, the SLA, and the model variety to serve diverse internal use cases under one contract.

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Common Mistakes When Choosing Between Groq and Together AI

1. Optimizing for speed when your bottleneck isn't the LLM.
I see developers switch to Groq for latency gains, then discover their p95 latency is still 800ms because their database query or RAG retrieval is the actual bottleneck. Profile your full request path before assuming the LLM is the slow part. Groq's 130ms TTFT only matters if the rest of your stack is sub-100ms.

2. Treating the model catalog gap as trivial.
"I'll just use Llama 3.3 70B everywhere" is a reasonable starting assumption, but many production systems evolve to need model variety — A/B testing, fallback models, specialized fine-tunes. If you build deeply on Groq and then need a model they don't carry, you're not just adding an API key — you're adding a second latency profile, a second rate limit strategy, and a second reliability dependency.

3. Ignoring rate limit implications during development.
Groq's free tier RPM caps are strict. I've seen developers burn hours debugging intermittent 429 errors during automated eval runs, not realizing the free tier caps out at 30 RPM on some models. Together AI's credit-based model is more predictable for development workflows. If you're running evals or CI/CD pipelines that call the LLM API, factor this in.

4. Conflating "inference-only" with "production-ready."
Both platforms are inference APIs — neither gives you conversation history management, prompt caching (at the time of writing), automatic fallback, or observability tooling out of the box. You'll need to add LLM gateway tooling (LiteLLM, PortKey, Helicone) on top of either one for production systems. Don't let "managed API" imply "fully managed production system."

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Where to Go Deeper

If this comparison has you thinking more broadly about the AI infrastructure stack:

• **Local LLM Hardware Requirements in 2026** — If you're weighing hosted inference against self-hosting, this guide breaks down exactly what hardware you'd need to run Llama 3.3 70B yourself vs. paying Groq or Together AI.
• **The Complete Guide to Running Local LLMs in 2026** — For teams seriously considering on-premise inference instead of any hosted API, this is where to start.
• **Aider vs Claude Code vs OpenHands: CLI AI Coding Tested** — If your use case is AI-assisted coding and you're trying to figure out which model backend powers the best developer tools, this gives the application-layer context that raw inference benchmarks miss.
• **Cursor vs Windsurf in 2026: Which AI Code Editor Should You Use?** — These editors abstract away the inference layer but are still affected by it; understanding the underlying API performance helps explain the latency differences you'll feel in the editor.

The hosted inference market is moving quickly. Groq is actively expanding its LPU cluster capacity, and Together AI continues to add models faster than any other platform I track. Whatever you decide today, build your integration behind an abstraction layer — a gateway or a standard client — so switching or splitting traffic later costs you hours, not weeks.