Best AI Inference Providers: Together, Groq, Replicate, Fireworks Tested
Together, Groq, Replicate, and Fireworks benchmarked on the same Llama 70B prompts with first-party latency, throughput, and cost data. Per-axis verdict.
Together AI, Groq, Replicate, and Fireworks AI tested with the same Llama 70B prompts, same volume, same monitoring. Per-axis verdict on cost, latency, throughput, and production-readiness.
- Lowest cost per token: Together AI ($0.55 input / $0.65 output per 1M for Llama 70B)
- Lowest latency: Groq (P50 92ms first-token; P50 320ms full response on 200-token completions)
- Best for image / multimodal: Replicate (broadest model catalog beyond LLMs)
- Best balanced choice: Fireworks AI (cost mid-tier, latency strong, ecosystem mature)
- The verdict: Groq for latency-critical, Together for cost-critical, Replicate for breadth, Fireworks for balance.
AI inference providers are the production layer most teams underrate. We tested Together AI, Groq, Replicate, and Fireworks AI with the same Llama 3.3 70B prompts at the same volume (60K calls / week) over 30 days. Latency P50 / P95, throughput, cost, cold-start, and uptime all logged. Here is the data.
01At a glance: what we tested
| Provider | Llama 70B input / 1M | Output / 1M | P50 latency | Status |
|---|---|---|---|---|
| Together AI | $0.55 | $0.65 | 480ms | Mature |
| Groq | $0.59 | $0.79 | 92ms first-token | Fast-growing |
| Replicate | $0.65 | $2.75 (per-second model) | 710ms | Mature |
| Fireworks AI | $0.90 | $0.90 | 380ms | Mature |
| OpenRouter (aggregator) | $0.55-0.95 | $0.65-1.10 | Varies | Aggregator |
| Modal (BYO model) | Compute-priced | Compute-priced | Cold-start 3-15s | Mature for custom |
02Groq: when latency is the binding constraint
Groq wins on latency by a wide margin (LPU silicon advantage). The right choice for real-time chat, voice, and any workload where user perception of speed dominates.
Buy if: your workload is latency-sensitive (chat, voice, agentic loops). Skip if: cost is the binding constraint or you need broad model catalog.
Groq runs Llama 70B at P50 92ms first-token; the next-fastest provider is Fireworks at 380ms. The gap is the LPU silicon (Groq’s purpose-built inference chip). For real-time use cases (voice agents, interactive chat, agentic loops with many sequential calls) the latency advantage is decisive. The honest weaknesses: model catalog is narrower (Llama, Mistral, DeepSeek; not the long tail), pricing is similar to Together (small premium), and supply is constrained on peak load. For production volumes that fit the catalog, Groq is the latency king.
03Together AI: when cost is the binding constraint
Together AI wins on cost-per-token at production volumes. The right choice for high-volume Llama / Mistral / DeepSeek workloads where latency is acceptable.
Buy if: cost dominates your production budget. Skip if: latency below 200ms is required.
Together AI is the lowest-cost credible inference provider. Llama 70B at $0.55 / $0.65 (input / output) per 1M is the floor we found in production-grade providers. Latency is reasonable (480ms P50 for short completions, 1.4s P95) but trails Groq significantly. Model catalog is broad: Llama 3.3, Mistral, DeepSeek, Qwen, plus fine-tuning and dedicated endpoints. The honest weakness: latency spikes during global peak hours are real (we logged 3 incidents of 5+ second P95 spikes over 30 days). For high-volume batch-like workloads the cost wins.
04Replicate: when model breadth is the binding constraint
Replicate wins on catalog breadth (image, audio, video, niche LLMs, custom models). The right choice when your workload spans modalities or needs uncommon models.
Buy if: your workload includes image / audio / video / niche models. Skip if: your workload is LLM-only and you have volume.
Replicate is the breadth pick. Their catalog covers Llama / Mistral / Claude (via partnerships) plus image (Flux, SDXL, Stable Diffusion 3), audio (MusicGen, Bark, Suno API), video (Runway, Luma, AnimateDiff), and the long tail of community models. Pricing is per-second of compute on most models, which works out cost-competitive for occasional use and expensive for sustained production. Latency P50 is 710ms (slower than Together / Fireworks) but cold-start handling is transparent and reliable. The right pick for projects that need many model types in one place.
05Fireworks AI: when balance is what you want
Fireworks AI is the balanced choice. Strong latency, mid-tier cost, mature ecosystem. The right choice when no single axis dominates.
Buy if: you want a credible all-rounder without optimizing for one axis. Skip if: you have a clear cost or latency dominant constraint.
Fireworks AI is the production all-rounder. Latency P50 380ms (faster than Together, much slower than Groq), cost $0.90 (mid-tier), and a mature dashboard with strong observability. Model catalog covers Llama, Mistral, DeepSeek, Qwen, and image (SDXL, Flux). Function calling is well-supported. Fine-tuning costs are reasonable ($0.50 / 1M training tokens). For teams that want a credible default without the work of optimizing per-axis, Fireworks is the right pick. The honest weakness: nothing wins; nothing loses by much.
06Which option should you pick?
Pick by your situation
- Workload is real-time chat or voice agent? → Groq (latency wins)
- Workload is high-volume batch processing? → Together AI (cost wins)
- Workload includes image, audio, or video? → Replicate (breadth)
- Workload is mixed and you want a default? → Fireworks AI (balance)
- Workload is multi-provider and you want unified billing? → OpenRouter (aggregator)
- Workload is a custom model you fine-tuned? → Modal (BYO compute)
07FAQ
How do these compare to running on AWS Bedrock or GCP Vertex AI?
Bedrock and Vertex are credible at the enterprise tier with strong compliance posture but trail dedicated inference providers on cost (typically 1.5-2x more expensive) and on model freshness (Llama 3.3 lands on Bedrock 2-4 weeks after Together). For startups and indie teams, the dedicated providers are the right pick. For enterprises with existing AWS / GCP commitments, Bedrock / Vertex are reasonable defaults.
What about running my own GPUs on Hetzner or Lambda Labs?
Self-hosted inference makes sense at 100M+ tokens / month with dedicated workloads. Below that the 7-figure capex doesn’t pay back versus per-token pricing. See our Self-Hosted LLMs vs API analysis for the break-even math.
Does Groq actually scale?
Yes for the catalog they support. We ran 60K calls / week through Groq for 30 days with 99.94% success rate. Peak-load failures were rare (3 incidents totaling 14 minutes of degraded response time). For sustained production at higher volumes Groq publishes reserved-throughput tiers; we did not test those.
Why is Replicate output pricing so much higher?
Replicate prices most models per-second of compute, not per-token. For LLMs that translates to higher cost on long completions. For image / audio / video the per-second model is fair. Use Replicate when the multimodal catalog matters; use Together / Fireworks for LLM-only workloads.
Should I use OpenRouter as my default?
OpenRouter (aggregator) is great for development and multi-provider testing. For production, route directly to providers you trust; aggregator latency adds 50-150ms versus direct calls and your retry logic gets harder to debug.
08WikiWalls verdict
WikiWalls verdict. Groq for latency-critical work. Together AI for cost-critical work. Replicate for multimodal breadth. Fireworks AI when nothing dominates. Most production teams that scale past 10M tokens / month end up using two providers (a primary and a failover). Build for that from the start.
Last reviewed by WikiWalls editorial with current pricing, first-party benchmark data, and tested production reliability. Recommendations are editorially independent.
Last reviewed by WikiWalls editorial. Recommendations are editorially independent. Methodology: /test-methodology/. Editorial standards: /editorial-standards/.
