#09 · Foundation Models & Inference Infrastructure
Top 10 Inference Providers for Production AI
What is an inference provider?
An inference provider is a company that runs AI models on its own infrastructure and exposes them through an API — handling the complexity of GPU procurement, model serving, autoscaling, batching, and optimization so that developers and enterprises can call models without operating the underlying compute themselves. The category has consolidated dramatically in 2025–26 around a clear functional taxonomy. *Price leaders* (Together AI, Fireworks AI, DeepInfra) compete on per-token cost and broad model coverage, running vLLM-class stacks on H100/H200 clusters with aggressive batching. *Performance leaders* (Groq, Cerebras, SambaNova) compete on throughput and time-to-first-token using custom silicon — LPUs, wafer-scale engines, and dataflow architectures — that bypass the GPU stack entirely. *Coverage and flexibility leaders* (OpenRouter, Replicate, Baseten, Modal) compete on model breadth, multi-provider routing, or custom-model deployment. *Closed-API frontier* providers (OpenAI, Anthropic, Google) — covered in the foundation model lists — operate their own inference but only for their own models.
Why inference provider selection matters.
Pricing on the same open-weight model now spreads roughly 6× across the field, and latency spreads 5–7×. Custom-silicon throughput (Groq's LPU, Cerebras's wafer-scale engine) is 5–10× higher than commodity H100 endpoints for supported models. The right answer for most enterprise teams is multi-provider routing rather than single-vendor commitment: a fast provider for user-facing chat, a cheap provider for batch and embeddings, a GPU cloud for custom models, and a unified routing layer to manage the orchestration. The wrong default — picking one provider and accepting whatever they charge — typically costs more than the engineering work to switch.
What to evaluate.
Buyers should evaluate inference providers on: (1) supported model catalog (does the provider support the models you actually want to use?); (2) the right metric for your workload — TTFT for interactive chat, sustained tokens-per-second for batch and agentic chains, cost per million tokens for high-volume; (3) compliance posture (SOC 2, HIPAA, GDPR, FedRAMP — varies widely); (4) fine-tuning support on the same platform as inference, if you need it; (5) routing flexibility (some providers offer multi-region failover, others don't); and (6) reliability and historical uptime, which can vary meaningfully across providers. The list below ranks the ten inference providers most defensible for production enterprise deployment.
Price leader at scale with research pedigree
Together AI, founded in 2022 and now generating $150M+ in annualized revenue with a $305M Series B raised in early 2025, has emerged as the price leader in open-weight inference at scale — particularly on batch and reserved-capacity pricing for steady-state production. Together combines competitive pricing with credible research pedigree (FlashAttention, Red Pajama, and ongoing inference optimization research) and the latest NVIDIA hardware (Blackwell GPU availability ahead of most competitors). Named customers include Cursor. Best for steady-state production workloads at high volume, organizations wanting fine-tuning and inference on the same platform, and any team where per-token cost dominates the inference decision. Strengths include category-leading batch and reserved pricing, very broad open-weight model catalog (deep Qwen, Llama, and MoE coverage), research-driven inference optimization, and Instant Clusters for those who need infrastructure procurement alongside serving. Trade-offs are that developer experience is slightly behind Fireworks for first-time deployments, and not all models are available on the serverless tier (some require dedicated endpoints).
Developer-experience leader for agentic workloads
Fireworks AI, founded by ex-PyTorch engineers and now valued at $4 billion after an October 2025 Series C, has emerged as the developer-experience leader in inference — particularly for production agentic workloads that depend on structured output and function calling. FireFunction v2, the company's function-calling-optimized engine, consistently outperforms general engines (including vLLM) on structured generation by approximately 4×. Customer base includes Cursor, Perplexity, Notion, Sourcegraph, Uber, DoorDash, Shopify, and Upwork — heavily weighted toward agentic and code-assistance use cases. Best for production agentic workflows, structured-output and function-calling use cases, organizations valuing developer experience, and teams that want fast deployment iteration. Strengths include category-leading structured generation, very strong developer experience, broad open-weight catalog, fine-tuning support on the same platform, and high-quality observability tooling. Trade-offs are pricing premium over Together at scale, and some larger enterprises prefer Together's batch-tier pricing for the highest-volume workloads.
Latency leader with custom LPU silicon
Groq, founded in 2016 by Jonathan Ross (former Google TPU lead), runs inference on custom Language Processing Units (LPUs) that deliver consistently sub-second time-to-first-token and 450+ tokens/second sustained throughput on supported models — performance that GPU-based providers can't match. The company's 2025 partnership with Meta to accelerate the official Llama API has materially expanded its distribution and credibility for open-model serving. Best for real-time chat, voice agents, latency-critical agentic loops, and any interactive workload where time-to-first-token dominates user experience. Strengths include category-leading TTFT (consistently 0.6–0.9s across the catalog), strong sustained throughput, generous free tier for prototyping, and OpenAI-compatible API for easy migration. Trade-offs are a narrower model catalog than GPU-based providers (LPU hardware is optimized for specific architectures), no custom-model deployment, and limited debugging tooling for performance issues.
Throughput leader with wafer-scale silicon
Cerebras Systems, founded in 2016, builds the Wafer Scale Engine (WSE) — a single silicon die the size of a dinner plate containing approximately 4 trillion transistors and 900,000 AI cores. The architectural advantage is that large models fit on a single die, eliminating the inter-chip communication overhead that bottlenecks GPU clusters. The result is approximately 2,600–3,000 tokens/second sustained throughput on models like gpt-oss-120B and Llama 4 70B — the highest measured in the inference category. The January 2026 $10 billion inference deal with OpenAI validates Cerebras's position as a tier-1 inference provider. Best for throughput-bound workloads, bulk processing, long agentic chains where total wall-clock time matters, and high-volume batch processing. Strengths include unmatched sustained throughput on supported models, exceptional energy efficiency (1–3 joules per token vs. 10–30 for GPUs), generous free tier, and major-customer validation. Trade-offs are a narrow model catalog (~4 supported models at any time), no custom model deployment, premium pricing on the upper tier, and abstracted hardware that limits debugging visibility.
Cost leader for variable workloads
DeepInfra has built its positioning around aggressive per-token pricing across what is currently the widest open-source model catalog in the inference market — including the Kimi K2 family, Qwen3.5 family, GLM-5, DeepSeek V3.2, MiniMax-M2, gpt-oss-120B, and NVIDIA Nemotron. The pricing-first positioning makes it the natural choice for cost-sensitive background workloads and batch processing where peak latency is secondary. Best for cost-sensitive background workloads, batch processing, embeddings at scale, summarization workflows, and any application where per-token cost dominates. Strengths include widest open-source catalog in the market, very competitive per-token pricing, OpenAI-compatible API, and broad model variety for routing diversification. Trade-offs are higher variance latency than performance leaders (throughput ranges from 79–258 TPS in benchmarks, with 0.23–1.27s latency spread), and less polished developer experience than Fireworks.
Multi-provider routing layer
OpenRouter exposes a single OpenAI-compatible API that routes requests across dozens of underlying providers — Cerebras, DeepInfra, Fireworks AI, Groq, Nebius, SambaNova, and many more — with automatic routing, fallback, and uniform pricing visibility. The architecture is one hop away from going direct (adding small markup and minor latency overhead), in exchange for provider-independence and easy A/B testing across providers without code changes. Best for teams wanting provider-independence, easy A/B testing across providers, automatic failover during provider capacity tightening, and organizations evaluating multiple providers before committing. Strengths include broad model and provider coverage from a single integration, easy provider switching, transparent multi-provider pricing visibility, and routing-as-a-service for resilience. Trade-offs are a small markup over going direct to underlying providers, minor latency overhead from the routing hop, and abstraction over provider-specific features.
Custom model deployment with enterprise compliance
Baseten, valued at $5B after a January 2026 Series E with $585M in total raised, has positioned itself as the enterprise inference engineering platform — focused on deploying custom models with strong compliance posture and broad GPU selection (T4 through B200). The Truss open-source framework is the company's model-packaging abstraction. The platform is differentiated as more of a control plane for production model serving than a commodity inference API, with SOC 2 Type II and HIPAA compliance attestations that matter for regulated buyers. Best for custom-model deployment in production, regulated enterprise inference (healthcare, financial services), organizations needing both serverless endpoints and dedicated infrastructure, and ML teams that want platform-level production capabilities. Strengths include SOC 2 Type II and HIPAA compliance, broad GPU selection, mature observability and monitoring, Truss framework for model packaging, and serious enterprise sales motion. Trade-offs are higher list pricing than commodity serverless inference, and less suited to pure model-catalog use cases (Together, Fireworks, DeepInfra) where infrastructure abstraction matters less.
Code-first serverless GPU platform
Modal, valued at $1.1B after a September 2025 Series B, has built the Python-native serverless GPU platform — decorator-based function deployment, per-second billing, fast cold starts (2–4 seconds), and Python infrastructure-as-code. The positioning is for AI-native engineering teams who want to write arbitrary GPU-accelerated Python code without managing Kubernetes or containerization. Best for AI-native teams building custom inference pipelines, organizations needing arbitrary Python GPU code execution beyond commodity model serving, and fine-tuning experiments where flexibility matters more than per-token optimization. Strengths include category-leading developer experience for code-first GPU workloads, fast cold starts, Python infrastructure-as-code, and per-second billing for variable workloads. Trade-offs are that effective H100 pricing under sustained load (~$3.95/hr) is significantly higher than dedicated GPU providers, the SDK creates platform lock-in (migrating means rewriting), and the platform is better-suited to experimentation and burst workloads than always-on inference.
Public-model marketplace for instant API access
Replicate, founded in 2019, offers instant access to thousands of pre-hosted open-source models through public HTTP endpoints — no deployment work required, pay-per-prediction billing, and a model marketplace covering everything from frontier LLMs to image, video, and audio generation models. The positioning is for the developer top-of-funnel: instant access to a broad model catalog for MVP demos, prototyping, and pay-as-you-go production at modest scale. Best for MVP demos, prototyping, public-model APIs, side projects, and any use case where instant access matters more than per-token cost optimization. Strengths include zero deployment friction, very broad public model catalog spanning all modalities, mature pay-per-prediction billing, and strong developer community. Trade-offs are that pricing is expensive at production scale (per-prediction billing penalizes high-volume workloads), the API format is Replicate-specific rather than OpenAI-compatible, and it's less suited for custom-model production deployment.
Custom dataflow silicon with broad model coverage
SambaNova Systems, founded in 2017 by former Sun Microsystems engineers, builds Reconfigurable Dataflow Units (RDUs) — custom silicon based on dataflow architecture optimized at the compute-graph level. SambaNova competes with Groq and Cerebras on custom-silicon speed but differentiates with broader model coverage and an explicit focus on enterprise and government deployment (defense, federal, regulated industries). Best for enterprises and government buyers wanting custom-silicon speed with broader model flexibility than Groq or Cerebras, organizations needing on-premise deployment of custom silicon, and high-throughput enterprise inference. Strengths include custom-silicon throughput advantages, broader model catalog than other custom-silicon providers, strong enterprise and government sales motion, and on-premise deployment capability. Trade-offs are a smaller developer ecosystem than commodity GPU providers and Groq's free-tier mindshare, and pricing typically requires enterprise engagement rather than self-service.