Your agent made 12 tool calls in the last minute. Each one waited on a model. The user felt every wait.
That’s the 2026 reality of fast inference APIs: tokens per second stopped being a benchmark metric and became a product metric. Agent loops multiply latency — one conversation, ten sequential model calls — so a provider that’s 3× faster on paper can make your product feel 3× more responsive. But the fast-inference market is also the most volatile corner of the API landscape: model lineups change monthly, prices get repriced quarterly, and every vendor’s marketing page claims the crown.
This guide compares the four providers that matter — Groq, Cerebras, Together, and Fireworks — on tokens/sec, price, hard limits, and workload fit. We’ll give you verified anchor numbers where they exist, a reproducible test methodology for your own workload, and a routing strategy that treats speed as a tier you buy per request, not a religion you commit to.
The Speed Race: Why 2026 Changed the Rules
Takeaway: specialized silicon (LPU, WSE) turned inference into a hardware race — and that race has tradeoffs most benchmarks don’t show.
Groq’s LPU and Cerebras’s WSE are purpose-built inference chips. They don’t just run faster than GPU clusters — they run at fundamentally different economics, trading memory flexibility for raw token throughput. The result: 300-1,000+ tokens/sec on supported models, versus the tens-to-hundreds typical of general GPU hosting.
The hidden axis is memory. Purpose-built chips carry fixed on-chip memory, which means model availability is a hardware constraint, not a business decision. A provider that can’t fit your model on the chip doesn’t offer your model. That single fact explains most of the lineup differences you’ll see below. And it’s the first thing to check before any speed comparison — our model catalog is a practical starting point for what runs where.
Option 1: Groq — Low-Latency Benchmark Leader
Takeaway: Groq is the reference point for fast inference — verified at 394 tokens/sec on Llama 70B — and its curated lineup is the price you pay for the speed.
Groq is the name most developers mean by “fast inference.” The verified numbers are real: Llama 70B at roughly 394 tokens/sec, priced around $0.59 per million input tokens and $0.79 per million output (mid-2026 rates; check the current pricing page before budgeting). The API is OpenAI-compatible and the developer experience is polished — a first call takes minutes via the quickstart — and the free tier is generous enough for real evaluation work.
The lineup is the constraint. Groq curates models that fit its LPU: Llama variants, GPT-OSS 20B and 120B, Qwen3 32B, Kimi K2, Llama 4 Scout. If your workload’s model isn’t in that set, Groq isn’t an option for you this quarter — and it may be next quarter, because the lineup grows as the hardware generation does. Plan around availability, not promises.
Who it fits: agent loops and interactive products where the model you need is in the lineup, and teams that want the fastest time-to-first-token without managing GPUs.
Option 2: Cerebras — WSE Giant Core & Throughput
Takeaway: Cerebras matches Groq on speed with a different model lineup and a real focus on long-context performance — verify your model and your context length before you commit.
Cerebras runs the same play on different hardware: a wafer-scale engine that trades conventional flexibility for massive throughput. Its lineup overlaps partially with Groq’s (GPT-OSS 120B, Zai GLM 4.7, and others) and its long-context behavior is a genuine differentiator — the architecture handles big context windows better than the “fast but short-memory” stereotype of specialized chips.
Two checks before adopting: model availability (the lineup differs from Groq’s — verify yours) and context limits at speed (a model running at 800 tokens/sec with a 32K ceiling behaves differently from one at 300 tokens/sec with 128K). Long-context performance is a known focus area for Cerebras’s architecture, but “known focus area” is not “measured on your workload” — run your own test.
Who it fits: teams with long-context agent workloads that fit Cerebras’s lineup, and anyone who wants an alternative to Groq’s model set.
Option 3: Together — Open-Model Depth
Takeaway: Together is the flexibility play — the widest open-model catalog, plus fine-tuning — and it competes on choice, not on the speed crown.
Together hosts the broadest open-model catalog of the four: Llama, Mistral, Qwen, DeepSeek — if it’s open-weight and popular, it’s probably there. The tradeoff is explicit: on headline speed benchmarks like Llama 70B, Groq is faster and cheaper on the same model. Together’s answer is breadth plus platform: fine-tuning, dedicated GPU clusters, and a new provisioned-throughput offering for teams that need predictable performance under load.
That combination is the actual use case: you don’t go to Together for the fastest single model; you go for the model that Groq doesn’t host, or the fine-tuned variant that only a full platform can serve. For multi-model products with custom weights, the platform value dwarfs the per-token delta.
Who it fits: teams running open models outside the specialized-chip lineups, fine-tuning workflows, and products that need predictable provisioned throughput.
Option 4: Fireworks — Fine-Tuning & Serverless
Takeaway: Fireworks is the build-your-own-model host — 100+ open models, fine-tuning with up to 100 LoRA adapters, and serverless deployment that skips GPU ops entirely.
Fireworks competes on the full lifecycle: host a model, fine-tune it (SFT, DPO, RFT), attach LoRA adapters, and deploy serverless — no GPU cluster, no MLOps team. For teams whose differentiator is a custom-tuned model, that’s the entire value proposition in one sentence. The Fireworks docs on serverless vs dedicated training lay out the choice cleanly.
The speed story is solid but not the headline — Fireworks is a general GPU platform with good throughput, not a specialized-chip contender. Its cost structure rewards consolidation: one platform for inference plus fine-tuning plus adapters beats three vendors for the same lifecycle — the same consolidation math our cloud vs self-hosting TCO analysis applies to the build-vs-buy decision.
Who it fits: teams fine-tuning open models, products that need LoRA-based personalization at scale, and anyone who’d rather pay for a platform than hire a GPU team.
Head-to-Head: Same Models, Same Load
Takeaway: one verified anchor (Groq at 394 tok/s on Llama 70B) plus a methodology you run on your own model — because the vendor’s fastest model is never your model.
Here’s what’s verified as of mid-2026, and what you must measure yourself:
| Provider | Verified anchor | Model lineup | Key constraint |
|---|---|---|---|
| Groq | Llama 70B: ~394 tok/s, $0.59/$0.79 per M | curated: Llama, GPT-OSS, Qwen3 32B, Kimi K2, Llama 4 Scout | model must fit the LPU |
| Cerebras | comparable speed class | GPT-OSS 120B, Zai GLM 4.7, others | verify model + context limits |
| Together | broadest open catalog | Llama, Mistral, Qwen, DeepSeek + fine-tuning | speed second to choice |
| Fireworks | solid, not leading | 100+ models + fine-tuning/LoRA | platform play, not speed crown |
The anchor numbers come from repeated third-party measurements (e.g., morphllm’s provider comparisons, which track per-token rates and tokens/sec across vendors); the rest of the table changes monthly, which is the point — and our full model pricing comparison tracks the broader landscape.
Run your own test — 30 minutes:
- Pick the exact model you’ll use in production (not the provider’s flagship).
- Load-test with your real prompt sizes and concurrency: measure TTFT, tokens/sec, and error rate under load, from your real region.
- Compare price per 1M tokens at your prompt mix — input-heavy agent workloads price differently than output-heavy chat.
- Test the failure modes: rate limits, model unavailability, timeout behavior — production is where providers differ most.
The load-balancing and failover docs cover what to do with the results: route the speed-critical path to the fast provider, fall back to the general one on failure.
Pick by Workload: Speed–Cost–Quality Triangle
Takeaway: buy speed as a tier, not as a provider — route the UX-critical path to the fast chip and the background work to the cheap token.
| Workload | Default pick | Why |
|---|---|---|
| Agent loops (sequential calls) | Groq or Cerebras | every call waits on the model |
| Long-context analysis | Cerebras (verify) | long-context at speed |
| Fine-tuned open models | Together or Fireworks | platform lifecycle |
| Background/batch work | cheapest token provider | speed irrelevant offline |
| Frontier closed models | not these four | different category entirely |
The routing pattern that makes this work without lock-in: a unified endpoint with per-workload routing — fast tier for the interactive path, cost tier for everything else, and BYOK when you want to bring a provider’s key into the same control plane. The provider BYOK docs show how to attach your own keys to a unified setup, and custom routing handles the tiering. One endpoint, one dashboard, four speed tiers — that’s the architecture that survives the next repricing.
FAQ
Which fast inference provider is fastest in 2026?
Groq and Cerebras lead on tokens/sec for the models they host — the verified anchor is Groq at ~394 tok/s on Llama 70B. But “fastest” is model-specific: the provider’s flagship isn’t your model. Run the 30-minute test above on your actual workload.
How much speed difference justifies switching?
A 30% improvement in TTFT or a 2× jump in tokens/sec is worth a migration; anything less usually isn’t, once you count the integration and monitoring work. Measure on your model, in your region, under your concurrency.
Why does Groq host fewer models than Together?
LPU memory is fixed hardware — a model that doesn’t fit the chip isn’t offered, period. That’s an architectural constraint, not a business choice. Check availability before you design around a provider.
Is Cerebras’s context window enough for my workload?
Depends on the model and plan — long-context is a genuine focus of the architecture, but the ceiling varies by model. Test with your real document sizes at production concurrency before committing.
Should production depend on a single fast provider?
No. Model lineups change, prices get repriced, and rate limits happen — a single-provider architecture is an outage waiting for a news cycle. Route the fast path with a fallback to a general provider, and treat the fast tier as a tier.
How often do fast-inference prices change?
Frequently — 2026 has already seen multiple repricings across these four providers. Budget quarterly, verify monthly, and keep the model-and-provider layer configurable so a repricing is a routing change, not a migration.
Summary
When you compare fast inference APIs in 2026, you get one verified speed leader (Groq, ~394 tok/s on Llama 70B), one architectural peer (Cerebras, with a long-context edge), and two platform plays (Together and Fireworks) that trade the speed crown for model choice and lifecycle depth. The lineup and price tables change monthly — so pick your workload’s anchor, run the 30-minute test, and route speed as a tier through a unified endpoint instead of betting the product on one vendor’s chip.
Don’t take our anchor numbers — load-test your own workload. Get your TokSpan API key, run the TPS test across fast and budget tiers, and bring your own keys when you want (BYOK). $5 in free credits, no card required.