Key takeaways
- Baseten leads in enterprise custom model deployment with $5B valuation and broad GPU selection
- Groq and Cerebras differentiate with custom silicon delivering 10x+ speed advantages over GPU-based platforms
- Together AI combines inference with research leadership (FlashAttention, Red Pajama) and cutting-edge NVIDIA hardware
- The market is splitting between API-first simplicity (Replicate, DeepInfra) and full-stack ML platforms (Baseten, Together AI)
FAQ
What's the fastest AI inference platform?
Groq (custom LPU) and Cerebras (Wafer-Scale Engine) offer the fastest raw inference speeds. Among GPU-based platforms, Fireworks AI and Together AI lead on throughput.
Which platform is cheapest for inference?
DeepInfra and Together AI compete on price for open-source models. Replicate offers pay-per-prediction simplicity. Groq has a generous free tier.
Should I use a GPU platform or custom silicon?
GPU platforms (Baseten, Together AI, Fireworks) offer broader model support and fine-tuning. Custom silicon (Groq, Cerebras, SambaNova) excels at raw speed for supported models.
Which platform is best for custom model deployment?
Baseten (Truss framework, broad GPU selection) and Fireworks AI (model lifecycle management) lead for deploying custom models.
Executive Summary
AI inference platforms have become critical infrastructure as organizations move from prototype to production with large language models and generative AI. These platforms solve the problem of "how do I serve AI models at scale?" — handling GPU orchestration, optimization, autoscaling, and cost management.
10 Platforms Compared: Baseten, Together AI, Fireworks AI, Modal, Replicate, Groq, DeepInfra, Cerebras, SambaNova
Key Findings:
- Baseten leads enterprise custom model deployment with $5B valuation, broad GPU selection (T4 through B200), and Truss open-source framework
- Groq and Cerebras differentiate with custom silicon delivering dramatically faster inference than GPU-based alternatives
- Together AI combines production inference with cutting-edge research (FlashAttention, Red Pajama) and the newest NVIDIA hardware
- Fireworks AI targets the speed+reliability sweet spot with 10T+ tokens/day and ex-PyTorch engineering pedigree
- Replicate and DeepInfra win on developer simplicity — minimal setup, pay-per-use
- Modal offers the most flexible serverless GPU platform (see full profile)
- The market is splitting between API-first simplicity and full-stack ML platforms
Comparison Matrix
| Platform | Focus | Custom Models | Fine-Tuning | Custom Silicon | OpenAI-Compatible | Compliance |
|---|---|---|---|---|---|---|
| Baseten | Custom + OSS deployment | ✅ | ✅ | — | ✅ | SOC2, HIPAA |
| Together AI | Full-stack AI cloud | ✅ | ✅ | — | ✅ | SOC2 |
| Fireworks AI | Speed + reliability | ✅ | ✅ | — | ✅ | SOC2, HIPAA |
| Modal | Serverless GPU compute | ✅ | ✅ | — | — | SOC2 |
| Replicate | Developer simplicity | Community | ✅ | — | — | — |
| Groq | Fastest LLM inference | — | — | ✅ (LPU) | ✅ | SOC2 |
| DeepInfra | Cost-efficient APIs | — | ✅ | — | ✅ | — |
| Cerebras | Wafer-scale inference | — | ✅ | ✅ (WSE) | ✅ | — |
| SambaNova | Enterprise on-prem | ✅ | ✅ | ✅ (RDU) | ✅ | Enterprise |
Pricing Comparison
| Platform | Model | Pricing Style | Free Tier |
|---|---|---|---|
| Baseten | Per-minute GPU billing | Usage-based, volume discounts | Trial credits |
| Together AI | Per-token | Usage-based | Free credits |
| Fireworks AI | Per-token | Usage-based, commitments | Free tier |
| Modal | Per-second compute | Usage-based | $30/month free |
| Replicate | Per-prediction | Usage-based | Free tier |
| Groq | Per-token | Usage-based | Generous free tier |
| DeepInfra | Per-token | Usage-based | Free tier |
| Cerebras | Per-token | Usage-based | Free tier |
| SambaNova | Enterprise contracts | Custom pricing | Free cloud tier |
Hardware Comparison
| Platform | Hardware | GPU Types |
|---|---|---|
| Baseten | NVIDIA GPUs | T4, L4, A10G, A100, H100, B200 |
| Together AI | NVIDIA GPUs | GB200, GB300 NVL72 |
| Fireworks AI | NVIDIA GPUs | A100, H100 |
| Modal | NVIDIA GPUs | A100, H100 |
| Replicate | NVIDIA GPUs | A40, A100, H100 |
| Groq | Custom LPU | Groq Language Processing Unit |
| DeepInfra | NVIDIA GPUs | A100, H100 |
| Cerebras | Custom WSE | Wafer-Scale Engine 3 |
| SambaNova | Custom RDU | Reconfigurable Dataflow Unit |
Product Profiles
Baseten
Enterprise AI inference platform with $5B valuation.[1] Deploys custom and open-source models with Truss open-source framework.
- $585M raised (Series E Jan 2026 — $300M from CapitalG, IVP, NVIDIA)
- GPU selection from T4 to B200, SOC 2 Type II and HIPAA compliant
- Per-minute billing, forward deployed engineers for enterprise
- ⚠️ More complex setup than pure API platforms
Best for: Enterprise teams deploying custom models at scale with compliance requirements.
Together AI
"The AI Native Cloud" — inference, fine-tuning, pre-training, and GPU clusters.[2] Research-driven with FlashAttention and Red Pajama contributions.
- 200+ models, OpenAI-compatible APIs
- NVIDIA GB200/GB300 NVL72 hardware
- Founded by researchers; 3.5x faster inference claimed
- ⚠️ Premium pricing reflects cutting-edge hardware
Best for: Teams wanting research-grade infrastructure with the latest hardware.
Fireworks AI
Speed-optimized inference from ex-PyTorch engineers.[3] $4B valuation, 10T+ tokens/day.
- $331M raised (Series C Oct 2025), 10K+ customers
- Model lifecycle: fine-tuning → optimization → serving
- Enterprise stability with startup agility
- ⚠️ Pricing can exceed simpler alternatives at scale
Best for: Production workloads requiring speed, reliability, and model lifecycle management.
Modal
Serverless Python infrastructure with elastic GPU scaling.[4] Full profile: Modal.
- Define infrastructure in Python code (no YAML/Docker)
- Sub-second cold starts, instant autoscaling
- GPU access without quotas (A100, H100)
- ⚠️ Python-centric
Best for: Python-native ML teams, GPU workloads, training and inference.
Replicate
Developer-friendly platform to run open-source models via API.[5] One line of code to run any model.
- Strong community of model creators
- Pay per prediction, no GPU management
- Popular for image/video generation
- ⚠️ Less control over infrastructure, limited custom model support
Best for: Developers wanting quick model access without infrastructure overhead.
Groq
Custom LPU hardware delivering the fastest LLM inference speeds.[6] Often 10x+ faster than GPU-based platforms.
- Deterministic latency, no batching delays
- Free tier available, OpenAI-compatible API
- Growing model support
- ⚠️ Limited to supported models, no custom model deployment
Best for: Applications where latency is the #1 priority.
DeepInfra
Cost-efficient inference APIs for open-source models.[7] Simple pricing with wide model selection.
- OpenAI-compatible API
- Competitive per-token pricing
- Quick setup, minimal configuration
- ⚠️ Limited enterprise features, no custom model deployment
Best for: Cost-conscious teams running open-source models via API.
Cerebras
Wafer-Scale Engine — the largest chip ever built — for AI inference.[8] Fastest single-model inference performance.
- WSE-3 with 4 trillion transistors
- Enterprise and research focused
- IPO plans signal maturity
- ⚠️ Limited model ecosystem, enterprise pricing
Best for: Enterprise/research teams needing maximum single-model throughput.
SambaNova
Custom RDU chip with enterprise-focused AI platform.[9] On-premise and cloud deployments.
- Reconfigurable Dataflow Unit optimized for AI workloads
- SambaNova Cloud for inference
- Strong enterprise/government presence
- ⚠️ Limited public cloud availability, enterprise-only pricing
Best for: Enterprise and government deployments requiring on-premise AI infrastructure.
Decision Guide
By Use Case
| Use Case | Recommended | Runner-Up |
|---|---|---|
| Custom model deployment | Baseten | Fireworks AI |
| Fastest LLM inference | Groq | Cerebras |
| Full ML lifecycle | Together AI | Modal |
| Developer simplicity | Replicate | DeepInfra |
| Cost-efficient APIs | DeepInfra | Together AI |
| Image/video generation | Replicate | Baseten |
| Enterprise compliance | Baseten | Fireworks AI |
| On-premise deployment | SambaNova | Baseten |
| Research/training | Together AI | Modal |
| Serverless GPU compute | Modal | Baseten |
By User Profile
Enterprise ML team with custom models: → Baseten (Truss framework, compliance, GPU breadth) or Fireworks AI (speed, lifecycle management)
Startup shipping fast: → Replicate (simplicity) or DeepInfra (cost) or Groq (speed + free tier)
Research team: → Together AI (latest hardware, research culture) or Cerebras (raw performance)
Latency-sensitive application: → Groq (custom LPU, deterministic) or Cerebras (WSE)
Budget-conscious team: → DeepInfra (cheapest per-token) or Groq (generous free tier)
Market Outlook
Near-Term (2026)
- Custom silicon (Groq, Cerebras, SambaNova) gaining share as model support expands
- GPU platforms competing on optimization (TensorRT-LLM, vLLM, SGLang)
- OpenAI-compatible APIs becoming table stakes
Medium-Term (2027)
- Consolidation as smaller players get acquired or run out of runway
- Custom silicon platforms expanding model support to match GPU versatility
- Fine-tuning + inference bundling becomes standard
Long-Term (2028+)
- 2-3 dominant platforms per segment (custom silicon, GPU cloud, developer API)
- On-premise inference growing as models commoditize
- Integration with agent orchestration platforms (Tembo, etc.)
Bottom Line
10 platforms serve the AI inference market with distinct strategies:
| Platform | Best For | Key Differentiator |
|---|---|---|
| Baseten | Enterprise custom models | Truss framework, GPU breadth, compliance |
| Together AI | Full-stack AI cloud | Research pedigree, latest NVIDIA hardware |
| Fireworks AI | Speed + reliability | Ex-PyTorch team, 10T+ tokens/day |
| Modal | Serverless GPU compute | Python-native, elastic scaling |
| Replicate | Developer simplicity | One-line API, model community |
| Groq | Fastest inference | Custom LPU, deterministic latency |
| DeepInfra | Cost efficiency | Cheapest per-token pricing |
| Cerebras | Maximum throughput | Wafer-Scale Engine, largest chip |
| SambaNova | Enterprise on-prem | Custom RDU, government/enterprise |
The market is bifurcating: custom silicon (Groq, Cerebras, SambaNova) competes on raw speed with purpose-built hardware, while GPU platforms (Baseten, Together AI, Fireworks, Modal) compete on flexibility, model support, and full-stack capabilities. API-first platforms (Replicate, DeepInfra) carve out the simplicity niche. The winners will be determined by which approach delivers the best price-performance as models continue to scale.
Research by Ry Walker Research
Disclosure: Author is CEO of Tembo, which may integrate with inference platforms for agent execution.