Top 5 AI Inference and Model Hosting Platforms (2026): Together AI vs Fireworks AI vs Replicate vs Groq vs Baseten

Multi-LoRA and Fine-Tuning

Fireworks supports full fine-tuning, LoRA, reinforcement learning, and quantization-aware training, but Multi-LoRA is the feature that changes the economics of this category. Instead of paying for a dedicated GPU per fine-tuned model, Multi-LoRA serves hundreds of LoRA adapters on a single base-model deployment through cross-model continuous batching, so a customer-specific fine-tune costs the same per token as the shared base model. Dynamic loading and caching mean adapters can be added or removed within seconds rather than requiring a redeploy. For a team that fine-tunes per customer or per use case, this turns an otherwise expensive GPU-per-tenant problem into a shared-infrastructure one.

FireAttention and Pricing Mechanics

FireAttention is Fireworks' custom CUDA attention kernel, built to outperform standard implementations like vLLM on the same hardware. The company also offers a Priority tier for teams that need guaranteed throughput and lower queue time under load, at roughly 1.25 to 1.5 times the standard token price. Combined with 50% discounts on cached input tokens and batch inference, the platform gives production teams several concrete levers to control cost instead of a single flat rate that only moves one direction.

Fine-Tuning to Dedicated Endpoints

Together AI's fine-tuning product now covers tool calling, reasoning, and vision fine-tunes, and a fine-tuned model can be pushed straight to a dedicated endpoint with an optional 99.9% uptime SLA. Dedicated H100 endpoints start at $2.99/hour, and fine-tuning itself starts at $0.48 per million tokens processed. The pipeline from fine-tune to production endpoint is one continuous workflow rather than an export-and-redeploy-elsewhere step, which is the platform's main structural advantage over piecing together separate fine-tuning and hosting vendors.

Capacity and Reliability Reality

Together's own dedicated-endpoints documentation acknowledges that available GPU capacity is finite: if a customer requests a minimum replica count higher than what is currently free, the endpoint queues until capacity opens up rather than provisioning instantly. Public reviews also surface recurring complaints about unexpected charges and slow support responses. None of this makes Together unusable, but it means teams should treat dedicated-endpoint provisioning as a lead-time item, not an on-demand button, especially around high-traffic launches.

LPU Hardware and Speed

Groq's Language Processing Unit is purpose-built for the decode phase of autoregressive generation, the token-by-token step that GPUs handle less efficiently than prompt processing. That focus produces 500 or more tokens per second on supported models, fast enough that NVIDIA structured a non-exclusive licensing deal for the underlying inference technology in December 2025, reportedly worth around $20 billion. For latency-bound products, voice agents, live coding assistants, real-time chat, that speed is a genuine product differentiator, not a marketing number.

The Custom Model Gap

Groq's core limitation for this category is model flexibility. The catalog is a curated set of open-weight models the company has optimized for its hardware; there is no self-serve path to upload an arbitrary custom fine-tune. LoRA fine-tune hosting was added in 2026, but it ships gated behind Enterprise-tier approval and a request process, not an API endpoint a solo developer can hit. Teams building on a custom fine-tune should plan to host that model on Fireworks, Together, or Baseten and reserve Groq for the parts of their stack running an unmodified supported model.

Model Catalog and Pay-Per-Use

Replicate's core product is a marketplace-style catalog of thousands of community-published and official models spanning text, image, audio, and video generation, callable through a single consistent API. Public models bill only for active processing time on a per-second basis, and cold starts on those public models are not charged to the caller, which makes Replicate genuinely cheap for low-volume or exploratory use. Popular models like FLUX skip GPU-second billing entirely in favor of a flat per-image price, simplifying cost forecasting for image workloads specifically.

The Private Deployment Cold-Start Trap

The billing model changes once a team deploys its own custom or private model instead of calling a public one. Private deployments are billed for the entire time the instance is online, including boot and idle time, not just active inference. A cold start on a private H100 deployment can take around two minutes, and if a request only needs 20 seconds of actual inference, the effective cost of that request is roughly ten times higher than the raw compute time suggests. Cloudflare's 2026 acquisition of Replicate has not changed this mechanic; teams running custom models at real traffic volume need to plan around it or keep instances warm.

Truss and Custom Deployment Control

Baseten's deployment model centers on Truss, a Python-based abstraction where a model is defined by load() and predict() methods plus a config.yaml describing hardware and dependencies. That gives teams real control: any model architecture, any custom pre- or post-processing pipeline, pinned to specific GPU hardware, not limited to a vendor's pre-selected catalog. The trade-off is that autoscaling parameters and deployment configuration require actual ML infrastructure knowledge, closer to running your own inference service than calling a hosted API.

Cold Starts and Pricing

Large model containers can take 30 to 90 seconds to cold-start on first request after scaling to zero, a real latency cost for spiky traffic. The Baseten Delivery Network, introduced to address this, cuts cold-start time by roughly 2 to 3x for big models, but does not eliminate the trade-off between paying for idle warm replicas and accepting cold-start variance. Pricing is per-minute on dedicated GPU instances, from $0.01052/min on a T4 up to $0.16633/min on a B200, with popular open models also available serverless per-million-tokens for teams that do not need custom deployment.