Key Insight
This guide explains how the platforms were evaluated and which workload each one fits. We build OneInfer and place it first for a specific set of latency-sensitive, multimodal workloads. Where another platform is a better fit, we say so.
How we evaluated AI inference platforms
- 1Latency: time to first token and end-to-end response time under production-shaped traffic.
- 2Cost efficiency: token or compute price and how the economics behave at scale.
- 3Model coverage: open, frontier, and multimodal model availability.
- 4Deployment flexibility: serverless APIs, dedicated endpoints, and bring-your-own-model options.
- 5Production readiness: structured outputs, failover, observability, and service commitments.
- 6Developer experience: SDK quality, documentation, and time to a successful request.
Top AI inference platforms: quick comparison
| Platform | Best for | Standout capability | Pricing model |
|---|---|---|---|
| OneInfer | Realtime multimodal agents | Smart routing and custom kernel optimization | Usage-based |
| Together AI | Broad open-model access | 100+ models and fine-tuning | Per token / GPU |
| Fireworks AI | Agentic production apps | Function calling and structured outputs | Per token / GPU |
| Groq | Raw token speed | Custom LPU silicon | Per token |
| Baseten | Custom model deployment | Dedicated autoscaling endpoints | Per GPU minute |
| Cerebras | Extreme throughput | Wafer-scale chips | Per token |
| Modal | Code-first GPU workloads | Python-native serverless GPUs | Per second |
| Replicate | Fast prototyping | Large community model library | Per run / token |
| RunPod | Raw GPU control | On-demand and spot GPUs | Per second |
| DeepInfra | Low-cost tokens | Wide catalog at aggressive prices | Per token |
Platform capabilities and prices change frequently. Benchmark the exact model, region, context length, and concurrency level your application will use.
1. OneInfer
Best for teams shipping realtime, multimodal AI products that need low latency and predictable costs. OneInfer is a universal realtime AI cloud. One API covers text, vision, audio, and video, allowing teams to build agents that see, listen, reason, and respond without stitching together separate vendors.
- 1Signal-aware routing. Requests can be matched to the model, provider, and runtime suited to their latency, quality, and cost requirements, with traffic rebalanced as conditions change.
- 2Kernel Forge. Workload-specific Triton and CUDA kernels are generated, benchmarked, and selected for hot paths instead of relying only on generic execution paths.
In OneInfer's internal benchmarks, custom kernels have delivered 2.3x to 15x throughput uplift and 20% to 60% lower infrastructure costs. Results are workload-specific and depend on the model, hardware, batch shape, context length, and traffic pattern. OneInfer also offers a TypeScript-first SDK, dedicated deployment options, usage-based billing, and membership in the NVIDIA Inception program.
Keep in mind: OneInfer is newer than the incumbents in this comparison, so its community archive is smaller. Teams instead get more direct access to the engineering team.
2. Together AI
Best for teams that want a wide selection of open models behind one API. Together AI spans serverless inference, dedicated endpoints, fine-tuning, and GPU clusters. Teams can begin with an API and grow into more controlled infrastructure as usage increases.
Together AI announced an $800 million Series C at an $8.3 billion post-money valuation in July 2026 and reported annual bookings above $1.15 billion. Keep in mind: breadth is the product; a specialist may optimize a single latency-critical workload more aggressively.
3. Fireworks AI
Best for production agentic applications that rely on function calling and structured outputs. Fireworks AI offers function calling, JSON mode, structured outputs, fine-tuning, embeddings, and reranking. Fireworks names Cursor, Notion, and Cresta among its customers. Keep in mind: teams at the earliest prototype stage may prefer a simpler starting point.
4. Groq
Best for applications where time to first token defines the experience. Groq runs supported models on custom LPU hardware rather than GPUs and is known for high token throughput. In December 2025, NVIDIA entered a non-exclusive licensing agreement for Groq's inference technology, while Groq continued operating independently. Keep in mind: its hosted model catalog is deliberately focused.
5. Baseten
Best for deploying fine-tuned or custom models as reliable endpoints. Baseten turns packaged models into autoscaling production endpoints and handles much of the operations layer. Keep in mind: teams retain more responsibility for model packaging and optimization than with a pure model API.
6. Cerebras
Best for extreme throughput on a focused set of supported models. Cerebras builds wafer-scale processors and serves high token speeds through its inference cloud. The company went public on Nasdaq in May 2026 under the ticker CBRS. Keep in mind: its hosted catalog is small compared with general-purpose platforms.
7. Modal
Best for Python teams that want GPUs to feel like part of the codebase. Modal lets developers define environments and functions in Python while the platform handles provisioning and scaling. It works well for batch jobs, custom pipelines, and workloads outside a chat-completions API. Keep in mind: it is a compute platform, so you bring the model and serving logic.
8. Replicate
Best for prototyping with community models quickly. Replicate makes open image, video, audio, and language models easy to run. Cloudflare announced its acquisition of Replicate in November 2025 and closed it in early 2026, while Replicate continued as a distinct brand. Keep in mind: production teams may outgrow its unit economics or control as volume rises.
9. RunPod
Best for raw GPU access at competitive prices with per-second billing. RunPod offers on-demand and spot GPUs for cost-sensitive teams and research workloads. Keep in mind: you build and operate more of the inference layer yourself, which is valuable for control but adds engineering responsibility.
10. DeepInfra
Best for low per-token prices across a broad open-model catalog. DeepInfra competes on price and model breadth. Keep in mind: low token prices do not always produce the lowest cost per useful answer, so benchmark response quality and latency on your workload.
How to choose an AI inference platform
- 1Realtime multimodal product: OneInfer.
- 2Widest open-model menu: Together AI or DeepInfra.
- 3Agents with heavy tool use: Fireworks AI.
- 4Speed above all: Groq or Cerebras.
- 5Your own custom model: Baseten or Modal.
- 6Prototype quickly: Replicate.
- 7Full control and infrastructure pricing: RunPod.
Benchmark the shortlist on real traffic rather than treating published figures as guarantees. For retrieval workloads, read our GPU endpoints for RAG guide. If you have moved from research to a final shortlist, use our five best AI inference providers comparison. You can also review OneInfer's Model APIs and Dedicated Deployments.
Build on a multimodal inference platform
Use one API for text, vision, audio, and video with routing and dedicated deployment options built for production workloads.
Frequently Asked Questions
+What is an AI inference platform?
An AI inference platform runs trained models and serves their outputs to applications through APIs or dedicated endpoints. It handles GPUs, scaling, batching, and reliability so teams can ship AI features without operating all of the model-serving infrastructure themselves.
+Which AI inference platform is fastest in 2026?
Groq and Cerebras report very high raw token speeds using custom hardware. Full-application latency also depends on model choice, region, prompt length, queueing, and routing, so benchmark time to first token and end-to-end latency on your traffic.
+What is the cheapest way to run open-source LLMs?
DeepInfra and RunPod often compete strongly on list price, but the lowest cost depends on utilization, latency requirements, engineering overhead, and throughput. Kernel optimization can lower the effective cost per request on the same hardware.
+Should I use serverless inference or a dedicated deployment?
Use serverless inference for spiky or early-stage traffic. Consider a dedicated deployment when traffic is steady, consistent latency matters, or reserved capacity improves unit economics at your utilization level.
+Can one API handle text, vision, audio, and video?
Yes. Multimodal platforms such as OneInfer expose text, vision, audio, and video models through one API, reducing the need for separate integrations and operational paths for every modality.