Fireworks, Together AI, and Groq are three of the most popular inference providers for open-source models. They all have competitive pricing pages, fast marketing copy, and benchmark numbers that look excellent in their documentation. The problem is that those numbers describe their infrastructure under their conditions, not yours.

If you're trying to figure out which provider is actually fastest and cheapest for your specific model and workload, you need to run the comparison yourself, with your own API keys, your own prompts, and your own token budgets. This post walks through what that comparison looks like and how to set it up without building a script from scratch.

What public benchmarks get wrong

Sites like Artificial Analysis publish detailed latency and throughput data across providers and models. That data is real, but it comes from their servers, their regions, and their API accounts. An engineer at a startup in a different region with a different account tier will see different numbers.

The other issue is timing. Public benchmarks are snapshots. Providers update their infrastructure, adjust capacity, and change pricing without announcement. A benchmark from three weeks ago may not reflect what you'll see today. Groq was consistently the fastest provider for many models throughout 2024, but that gap has narrowed as Fireworks and Together AI improved their hardware and routing.

The only benchmark that matters for a production decision is one run against your own keys, close to the time you're making the decision.

What to measure

Three numbers matter most when comparing inference providers.

Time to first token (TTFT) is how long it takes from sending your request to receiving the first token in the streamed response. This controls perceived latency in any application where you're streaming output to a user. A provider with slower TTFT feels slower even if their total throughput is competitive.

Tokens per second measures throughput once the stream starts. For longer outputs, this matters more than TTFT. A provider with 50ms TTFT but 80 tokens/sec will finish a 500-token response in about 6.3 seconds. A provider with 80ms TTFT but 120 tokens/sec finishes the same response in about 4.2 seconds.

Cost per million tokens is straightforward but often misread. Providers price input and output tokens separately, and the ratio that matters depends on your prompt length versus your output length. A provider that looks cheaper on the pricing page may cost more for your specific prompt structure.

Fireworks, Together AI, and Groq compared

For Llama 3.1 70B, which is the most commonly benchmarked open-source model right now, the three providers are genuinely competitive with each other. All three serve the model and all three have invested heavily in optimizing for it.

Groq's custom LPU hardware gives it unusually low TTFT on shorter prompts. It has been the go-to choice for latency-sensitive applications where the first token matters more than throughput. The limitation is that Groq's model catalog is smaller than Fireworks or Together AI, and their pricing has adjusted as demand grew.

Fireworks has strong throughput numbers, particularly for longer generations. Their pricing is competitive for high-volume use cases, and their model catalog is wide, covering most popular open-source models across Llama, Mistral, and other families.

Together AI has historically been strong on cost for high-throughput workloads. Their infrastructure has improved significantly over the past year, and they offer a wide model catalog including fine-tuned variants that Groq doesn't serve.

These generalizations hold on average. For your specific workload, the answer may be different. A 512-token prompt with a 200-token output will produce different rankings than a 2000-token prompt with a 1000-token output on the same model.

How to run the comparison yourself

The manual way is to write a script that fires the same request to each provider, records timestamps around the stream, and computes TTFT and tokens/sec. This works for a one-time check. The problems are that it takes time to write correctly, it produces a one-off result with no history, and it goes stale as soon as you close the terminal.

The faster way is to use Tesseract, which is built specifically for this comparison. You connect your API keys for each provider (they're stored encrypted and verified against the provider's API), pick your model and prompt, and Tesseract fires the same request to all connected providers in parallel. You get a ranked results table per run, with TTFT, tokens/sec, total latency, and cost per million tokens for each provider. Every run is saved with the exact prompt and settings used, so you can look back at how the rankings have changed over time.

The free tier covers two connected providers and five benchmark runs per day, which is enough to run a real comparison before committing to anything.

What changes over time

Provider rankings are not stable. This is the most important thing to understand about the Fireworks vs Together AI vs Groq question, and the reason a one-time benchmark isn't enough.

Providers have bad hours. Groq has had cold start issues during peak demand. Together AI has had regional latency increases during high-traffic periods. Fireworks has adjusted pricing multiple times. If your routing decision is based on a benchmark from last month, you may be paying more or waiting longer than you need to.

The engineers who get this right are the ones who monitor continuously. That means running benchmarks on a schedule, keeping a rolling scoreboard of who is winning over the last 24 hours and 7 days, and getting an alert when the current leader stops being the best option.

Tesseract's Pro tier automates all of that. Scheduled benchmarks run on an hourly, 6-hour, or daily cadence, the scoreboard updates automatically, and you can set an alert that emails you when your current provider's latency exceeds a threshold or when a cheaper option appears. At $39 per month, it costs less than an hour of engineering time to set up and maintain manually.

Making the call

For most Llama 3.1 70B workloads today, you'll see real differences between Fireworks, Together AI, and Groq depending on your prompt length and output requirements. The only way to know which one wins for your app is to run the test with your own keys.

Start with the free tier at tesseract.click. Connect your Fireworks, Together AI, and Groq accounts, run a benchmark with a representative prompt from your actual use case, and see the real numbers. The results may match what public leaderboards say, or they may not. Either way, you'll know for certain.