If you search for a comparison of Fireworks, Together AI, and Groq, you will find blog posts with benchmark tables, charts of tokens per second, and confident recommendations. Most of them were published six months ago, tested from a single data center, and priced against a rate card that may have changed since. They are useful for orientation, but you cannot deploy based on them.

Provider performance is not static. Groq's dedicated LPU hardware produces genuinely low latency at certain scales, but throughput can compress during peak hours. Fireworks has made significant investments in speculative decoding and fine-tuned model serving. Together AI competes aggressively on price for larger models. All three have updated their model catalogs, pricing, and infrastructure multiple times in the last year. A blog post cannot keep up with that.

The only comparison that matters for your application is the one run against your API keys, from your environment, today.

What varies between providers — and why it matters

Three metrics drive most routing decisions for production LLM applications.

Time to first token (TTFT) is the gap between sending the request and receiving the first token in the stream. For chat interfaces and streaming applications, this is the number that controls perceived responsiveness. A model that streams tokens quickly but starts late feels slow.

Tokens per second determines how fast a long completion finishes. For batch processing, summarization, or any use case where you care about wall-clock time for a full response, this is the number to optimize.

Cost per million tokens scales directly with volume. At low traffic, a 20% cost difference between providers is negligible. At a million tokens per day, it becomes a real line item.

For Llama 3.1 70B — one of the most common open-source models in production — all three providers serve the model, and all three compete on these dimensions. But the relative rankings shift depending on load, time of day, and how your account is provisioned. An account with reserved capacity on Fireworks will see different numbers than a pay-as-you-go account on the same model.

Why public leaderboards give you the wrong answer

Sites like LMSpeed, ProviderBench, and various community benchmarks do genuine work to collect performance data across providers. The problem is structural: they test from their own servers, against accounts they control, at testing cadences that do not match real production traffic.

Your application runs from a specific region. Your API key may be on a different capacity tier than the key a benchmarking site uses. Provider infrastructure is not uniform across all customers, and results from a shared test account can differ materially from results on a dedicated or high-volume account.

This means the leaderboard tells you which provider is fastest for the benchmarking site, not for you. For casual model evaluation that distinction may not matter. For production routing decisions, it matters quite a bit.

A better approach: benchmark with your own keys

Tesseract takes a different approach. You connect your own API keys for Fireworks, Together AI, Groq, Baseten, and OpenRouter, and Tesseract fires the same prompt to each provider in parallel, measuring TTFT, tokens per second, total latency, and cost per million tokens using your credentials. The results are specific to your account and your network path.

For a comparison between Fireworks, Together AI, and Groq on Llama 3.1 70B, a typical benchmark run in Tesseract takes under 30 seconds. The results table shows all three providers ranked by latency, with a green "Fastest" badge on the lowest-latency result and a blue "Cheapest" badge on the lowest-cost result. Every run is saved so you build a history over time rather than relying on a single point-in-time measurement.

The Pro tier adds automated scheduled benchmarks — hourly, every 6 hours, or daily — so the scoreboard stays current without manual intervention. If Groq spikes in latency on Tuesday afternoon, you see it in the trend chart. If Together AI drops their cost per million tokens for Llama 3.1 70B, the scoreboard reflects it on the next scheduled run.

Baseten and OpenRouter in the mix

Two providers that appear less often in public comparisons are worth including in any serious evaluation.

Baseten is aimed at teams that need custom model serving, fine-tuned models, or dedicated capacity. Its latency characteristics differ from shared inference endpoints, and its pricing is structured differently. For teams running proprietary or fine-tuned variants, Baseten is often the only provider that can serve the model at all.

OpenRouter acts as a meta-provider, routing requests across multiple underlying providers. Including it in a benchmark lets you compare OpenRouter's routing decision against your own benchmarked choice — sometimes the overhead of OpenRouter's routing adds latency, and sometimes its provider selection at a given moment is faster than any single provider you have connected.

From comparison to configuration

Once you know which provider is winning for your model and objective, Tesseract generates a routing rule with a copy-paste configuration: the base URL, the model identifier, a curl command, and a Python snippet. You update your application's inference client to point at the current winner without manually consulting provider documentation.

When the best provider changes — which it does, regularly — Tesseract's alert system can notify you before you notice the degradation in your own metrics. You set a latency threshold or a cost ceiling, and Tesseract emails you when the condition is met, naming the model, the condition, and the alternative currently available.

You can start comparing Fireworks, Together AI, and Groq against your own keys on the free tier at tesseract.click. The free tier supports two connected providers and five benchmark runs per day with no payment information required.

Frequently asked questions

Is Groq always the fastest inference provider? Groq's LPU hardware produces very low TTFT for supported models, but "always fastest" is not accurate. Performance depends on model size, request volume, time of day, and your account tier. The only reliable answer is a live benchmark against your keys.

How often does provider performance change? Significantly enough to matter. Infrastructure updates, model version changes, pricing adjustments, and capacity fluctuations all affect the rankings. Teams using automated benchmarks on Tesseract Pro typically see the fastest and cheapest provider flip at least once per week for actively used models.

Can I benchmark the same model across all five providers at once? Yes. If you have connected API keys for all five providers and all five serve the model you select, Tesseract runs the benchmark against all of them in parallel in a single run.

What if a provider times out during a benchmark? That provider's row shows "Failed" in the results table. The other providers' results are unaffected. A timeout after 60 seconds is excluded from the winner ranking.