ML Inference & GPU Hosting Platforms: Modal, Replicate, RunPod, Beam, Together AI, Fireworks, Anyscale, Baseten, Lambda Labs

⬅️ Cloud & Hosting Overview

If you're a SaaS in 2026 building AI features that go beyond "call OpenAI/Anthropic" — fine-tuned models, custom embeddings, open-source LLMs (Llama, Qwen, DeepSeek), Stable Diffusion / Flux for images, Whisper for transcription, custom-trained classifiers, vector search at scale, or GPU-heavy data processing — this is the consolidated comparison of GPU/ML inference hosting platforms. The naive shape: "We'll use Vercel + OpenAI." Works for v0; falls over when you need (a) custom models, (b) cheaper inference at scale, (c) lower latency than third-party APIs, or (d) on-prem-like control over data.

This is distinct from AI Models (the foundation models themselves — Claude, GPT, Gemini, Llama). It's also distinct from Vercel AI Gateway (a gateway over hosted model APIs). This is the layer below: where YOU host model weights, batched inference, fine-tunes, custom models. Pick the right shape (serverless GPU / managed inference / dedicated GPU rental / fine-tune-and-host platform) and you get state-of-the-art AI at 1/10th the cost of frontier APIs. Pick wrong and you're either paying frontier prices for boilerplate workloads or operating Kubernetes GPU clusters that nobody understands.

TL;DR Decision Matrix

The first decision is what shape of hosting you actually need: serverless burst (Modal, Beam, Cerebrium), per-inference pre-built models (Replicate, HF Endpoints), per-token hosted LLMs (Together, Fireworks), GPU rental for training (RunPod, Lambda Labs), or full ML platform with MLOps (SageMaker, Vertex, Databricks). Each shape has a clearly best tool. Picking wrong is the most common mistake — usually defaulting to SageMaker when Modal would have shipped 5x faster.

Decide What You Need First

ML infra platforms are not interchangeable. Get the shape wrong and you'll either pay too much, hit operational complexity, or fail to ship.

Custom Python code on GPU (the 60% case for indie ML)

You have Python code (model.predict, batch processing, fine-tune loop) and want to run it on GPU without managing infrastructure.

Right tools:

• Modal — Python-decorator API; serverless GPU
• Beam — Python-first alternative
• Cerebrium — modern alternative

Pre-built model hosting (Stable Diffusion, Whisper, etc.)

You want to call common open-source models without writing your own inference code.

Right tools:

• Replicate — pay-per-inference; massive model catalog
• Hugging Face Inference Endpoints — HF ecosystem
• Modal — if you want the model in your account/code path

LLM inference at scale (Llama, Qwen, DeepSeek)

You want to call open-source LLMs at price competitive with OpenAI/Anthropic.

Right tools:

• Together AI — broad OSS LLM catalog; per-token
• Fireworks AI — best latency for OSS LLMs
• Modal / Replicate — if you have specific custom needs
• Vercel AI Gateway routing to Together/Fireworks (often the right pattern)

Fine-tuning + hosting your fine-tune

You have data; you want to fine-tune an open model and serve it.

Right tools:

• Together AI — fine-tune + host in one platform
• Fireworks — same
• Modal + your own training code — DIY route; full control
• Replicate — fine-tune via their API; deploy

GPU rental for training

You're training models; need raw GPU time.

Right tools:

• RunPod — flexible, cheap, spot pricing
• Lambda Labs — bare-metal GPU rental
• CoreWeave — enterprise scale
• Modal — if your training fits serverless model

Production model deployment + monitoring

You have models in production; need observability, A/B testing, version management.

Right tools:

• Baseten — model deployment platform with monitoring
• AWS SageMaker — full enterprise platform
• Modal + your own monitoring layer
• Anyscale — Ray-based; large-scale workloads

Enterprise / data-warehouse-integrated ML

You're already on Databricks / Snowflake; want ML-on-data.

Right tools:

• Databricks ML — native Lakehouse ML
• Snowflake Cortex — native Snowflake ML
• AWS SageMaker — if AWS-aligned

Provider Deep-Dives

Modal

The Python-first serverless GPU platform. Modal (founded 2021) gave Python developers a one-decorator way to run code on GPUs without provisioning infrastructure. Default for indie + startup ML in 2026.

Strengths:

• @modal.function decorator → run on any GPU you want.
• Per-second pricing; no idle cost.
• Massive GPU pool (T4, A10, A100, H100, H200).
• Excellent DX; deploy in minutes.
• Webhooks → triggers serverless function (great for inference APIs).
• Cron jobs, dependency mgmt, secrets, custom Docker — all clean.
• Strong community; active development.
• Volumes for persistent storage between runs.
• Good observability built-in.
• Free tier real ($30/mo credits).

Weaknesses:

• Python-only (no Node, Go, Rust SDKs as first-class).
• Cold starts can be 10-30s for large models (mitigated by keep-warm).
• Vendor lock-in: code is decorator-syntax-tied.
• At very high scale, dedicated GPU may be cheaper.

Pricing: $30/mo credits; per-second compute beyond. Typical SaaS spend: $50-2000/mo.

Best for: Python ML teams; serverless GPU workloads; ML startups; rapid prototyping; the default for indie ML in 2026.

Replicate

Pre-built model hosting at scale. Replicate (founded 2019) is "pay-per-inference for popular open-source models" — Stable Diffusion, Flux, Whisper, Llama, Mistral, hundreds more.

Strengths:

• Largest catalog of pre-built models (1000+).
• Per-inference pricing (no idle cost).
• Cog (their OSS framework) lets you ship custom models too.
• Webhooks for async inference (don't wait for image generation).
• API is dead-simple: HTTP POST → result.
• Strong community of model creators.
• Quick experiments with cutting-edge models (Flux 1.x, etc., available within hours of release).

Weaknesses:

• Per-inference pricing can be expensive at scale.
• Cold starts visible (5-30s for warm; 30-120s for cold).
• Less custom-deployment-friendly than Modal/Baseten.
• Quality of community models varies.

Pricing: Per-inference; e.g. $0.0023/image for SD3.5 on A40; $0.005/video-second; varies wildly per model.

Best for: Quick integration of popular AI models; non-LLM AI (image, video, audio); experimentation; Cog packaging.

RunPod

The cheapest GPU rental + serverless. RunPod (founded 2022) provides bare GPU rental + serverless inference, often at 30-50% lower cost than competitors.

Strengths:

• Cheapest GPU on the market (often 30-50% below competitors).
• Spot pricing for further savings.
• Wide GPU availability (4090s, 5090s, A100s, H100s).
• Both serverless (auto-scale) and dedicated rental (fixed billing).
• Templates for common use cases.
• Modal-like serverless API.

Weaknesses:

• DX rougher than Modal.
• Fewer integrations.
• Smaller community.
• Reliability has had hiccups (improving).
• Less polished docs.

Pricing: Per-hour rental from $0.20/hr (4090s); per-second serverless. Often 30-50% cheaper than Modal at scale.

Best for: Cost-sensitive teams; high-volume inference; flexibility; teams comfortable trading DX for cost.

Beam

Modal alternative. Beam (founded 2022) is similar in shape — serverless Python on GPU — but with some different design choices.

Strengths:

• Python-decorator API.
• Per-second pricing.
• Good DX.
• Pre-built templates.
• Volume / shared-storage support.
• Smaller team, but well-loved by users.

Weaknesses:

• Smaller catalog of features than Modal.
• Smaller community.
• Less brand recognition.

Pricing: Per-second; comparable to Modal at low volume.

Best for: Modal alternative; teams who prefer Beam's design; sometimes 10-20% cheaper.

Together AI

OSS LLM inference at scale. Together AI specializes in hosting open-source LLMs (Llama, Qwen, DeepSeek, Mixtral, etc.) at SOTA latency + competitive pricing.

Strengths:

• Wide catalog of OSS LLMs (50+).
• Per-token pricing competitive with OpenAI.
• Fine-tuning service available.
• OpenAI-compatible API (drop-in replacement for OSS models).
• Production-grade SLA.
• Pairs with Vercel AI Gateway natively.

Weaknesses:

• LLMs only (not for SD / Whisper / etc.).
• Less customizable than Modal / Baseten.
• Vendor risk (smaller than AWS / Google).

Pricing: Per-token; e.g. $0.20-0.90 per million input tokens for Llama variants.

Best for: OSS LLM inference at production scale; replacing OpenAI/Anthropic for cost-sensitive use cases; fine-tuning + hosting.

Fireworks AI

Best-latency OSS LLM hosting. Fireworks (founded 2022) competes with Together AI on OSS LLM hosting; differentiates on latency optimization and fine-tune-host pipelines.

Strengths:

• Industry-leading latency for OSS LLMs (often 2-5x faster than Together).
• Speculative decoding + custom kernels.
• Fine-tune + host workflow integrated.
• OpenAI-compatible API.
• Production-grade SLAs.
• Excellent docs.

Weaknesses:

• Smaller catalog than Together.
• Fewer non-LLM options.
• Vendor risk.

Pricing: Per-token; competitive with Together; sometimes higher for premium latency.

Best for: Latency-critical LLM applications (chat UIs, real-time agents); fine-tuned-model hosting.

Baseten

Production model deployment + monitoring. Baseten focuses on the production-deployment shape — your model, deployed with monitoring, autoscaling, A/B testing.

Strengths:

• Production-ready: monitoring, alerting, autoscaling.
• Truss (their model packaging format) is clean.
• Fine-tune + deploy pipeline.
• Integrates with major model registries.
• Per-second compute.
• Good for teams shipping multiple models.

Weaknesses:

• Smaller community than Modal.
• Pricing can be high at scale.
• Fewer pre-built models than Replicate.
• Less rapid-prototype-friendly than Modal.

Pricing: Per-second compute; $30 credits to start.

Best for: Production model deployment with serious monitoring needs; ML platform teams managing 5+ models.

Anyscale (Ray)

Enterprise ML platform built on Ray. Anyscale productizes Ray (the OSS distributed compute framework from UC Berkeley); positions for enterprise ML training + serving.

Strengths:

• Ray-native (best-in-class for distributed ML).
• Strong for training-heavy workloads.
• Multi-cloud deployment.
• Enterprise governance + multi-tenancy.

Weaknesses:

• Steep learning curve (Ray itself is a substantial framework).
• Pricing is enterprise.
• Less indie-friendly than Modal.

Pricing: Custom enterprise; expect $5K-100K+/mo.

Best for: Enterprise ML teams; large-scale distributed training; Ray-aligned shops.

Lambda Labs

Bare-metal GPU rental. Lambda Labs is hardware-first — rent A100s / H100s by the hour for training.

Strengths:

• Cheaper than AWS/GCP/Azure for raw GPU.
• Reliable hardware availability.
• 1-Click clusters (multi-GPU training).

Weaknesses:

• DIY orchestration; not serverless.
• No auto-scale.
• Operate your own model deployment.

Pricing: $1.10-2.49/hr per H100 typical; A100 cheaper.

Best for: Long-running training jobs where dedicated GPU > serverless. Not for inference.

Hugging Face Inference Endpoints

Quick deploy of HF-hosted models. Inference Endpoints lets you 1-click deploy any HF model on AWS / GCP / Azure.

Strengths:

• Massive HF model ecosystem.
• 1-click deploy.
• Pay-per-instance-hour.
• Multi-cloud.

Weaknesses:

• More expensive than serverless (per-hour billing).
• Less flexible for custom code.
• Per-hour billing punishes idle time.

Pricing: Per-instance-hour; e.g., $0.50-4.00/hr for various GPU sizes.

Best for: Quick POC of HF models; teams already using HF heavily.

AWS SageMaker

AWS's ML platform. Comprehensive: training, inference, MLOps, AutoML, experiment tracking.

Strengths:

• Comprehensive (everything in one place).
• AWS-deep integration.
• Enterprise governance.
• Studio (Jupyter-style notebooks).
• AutoML offerings.

Weaknesses:

• Pricing complex; expensive at low volume.
• Steep learning curve.
• Slower iteration vs. Modal.
• Lock-in to AWS.

Pricing: Per-instance; varies wildly by feature; expect $200-50K+/mo.

Best for: AWS-bound enterprise; comprehensive ML platform needs.

Google Vertex AI

GCP's ML platform. Comparable to SageMaker for GCP-bound teams.

Strengths:

• Strong AutoML.
• Vertex AI Agent Builder for AI applications.
• Gemini integration.
• BigQuery ML integration.

Weaknesses:

• Lock-in to GCP.
• Fewer model types than SageMaker.
• Pricing complex.

Best for: GCP-bound teams; Gemini-heavy workloads.

Databricks ML

Lakehouse-integrated ML. Strong for data-platform-driven ML.

Strengths:

• Native Lakehouse integration (data + ML in one).
• MLflow integrated.
• Spark + GPU at scale.
• Enterprise governance.

Weaknesses:

• Tied to Databricks.
• Pricing enterprise.

Best for: Databricks-shop teams; data-engineering-led ML.

What These Platforms Won't Do

Useful to be clear-eyed:

• They won't replace foundation-model APIs for general use. OpenAI, Anthropic, Google for general-purpose tasks remain dominant. Self-hosted OSS LLMs win when (a) cost matters, (b) custom fine-tune matters, or (c) data sovereignty matters.
• They won't fix bad models. Garbage in, garbage out. The platform doesn't make a poorly-trained model good.
• They won't replace MLOps discipline. Versioning, monitoring, evaluation — these are practices, not features.
• They won't give you frontier-model quality on smaller/older OSS. Llama 3 70B is great; not as good as Claude Sonnet 4.6 for many tasks. Test before assuming.
• They won't scale to zero cost. Even serverless has a non-zero baseline (cold starts, persistent volumes). Manage idle.
• They won't solve the eval problem. You still need to evaluate model quality continuously. (See LLM Observability Providers.)
• They won't handle compliance for you. SOC 2 / HIPAA / EU residency is YOUR responsibility (with vendor support).

Pragmatic Stack Patterns

Common 2026 patterns:

Indie / pre-PMF AI feature

Vercel AI Gateway → Anthropic / OpenAI / Gemini (frontier APIs)
+ Replicate for one-off image/audio
+ NO custom hosting yet

Rationale: don't pay for hosting until traffic justifies. Frontier APIs are good enough.

Early-stage with first AI features

Vercel AI Gateway → frontier APIs (chat, summarization, etc.)
+ Modal for any custom Python (e.g. embeddings batch jobs)
+ Replicate for image generation (if applicable)
+ NO fine-tuning yet

Rationale: stay agile; low ops cost.

Growth-stage AI-heavy SaaS

Vercel AI Gateway → mix of frontier APIs + Together/Fireworks for OSS
+ Modal for custom inference logic (RAG retrievers, embeddings, classifiers)
+ Together AI fine-tunes for specific domain tasks (cost down 5-10x)
+ Replicate for image/audio
+ Baseten for production-deployed custom models

Rationale: cost optimization through OSS routing where quality is good enough; custom models for repetitive domain tasks.

ML-first product (image gen, voice, transcription as core feature)

Modal (custom inference + fine-tuning)
+ Replicate (rapid integration of new models)
+ RunPod for high-volume / cost-critical inference
+ Custom fine-tunes hosted via Modal or Baseten
+ Vercel AI Gateway for any frontier-API touchpoints

Rationale: ML is the product; control matters.

Enterprise ML team

SageMaker (or Vertex AI / Databricks ML) for governance + experimentation
+ Anyscale or DIY Ray for distributed training
+ Custom inference via Baseten or in-house deployment
+ Fireworks for LLM inference where third-party acceptable

Rationale: enterprise governance; existing cloud commitments.

Cost-sensitive / high-volume inference

Together AI / Fireworks for LLM inference (per-token)
+ RunPod for non-LLM GPU workloads (per-second/hour)
+ Modal only for custom logic that needs serverless
+ Aggressive caching upstream

Rationale: optimize $/inference; volume justifies optimization.

AI agent / multi-step workflows

Frontier APIs (Claude/GPT) for reasoning steps
+ Modal for custom tools the agent calls
+ Together/Fireworks for fast OSS LLM steps
+ Vercel AI Gateway for routing + observability
+ Vercel Sandbox for code-execution steps

Rationale: agent shape mixes frontier reasoning + custom tool execution.

Decision Framework

1. What's the workload?

• Frontier-quality LLM: Frontier APIs (Anthropic / OpenAI / Google) via Vercel AI Gateway.
• OSS LLM at production scale: Together AI or Fireworks.
• Image / video / audio: Replicate.
• Custom Python on GPU: Modal, Beam, RunPod.
• Long training jobs: Lambda Labs, RunPod dedicated, Anyscale.

2. Scale?

• <$200/mo inference: Frontier APIs; Replicate; Modal free tier.
• $200-2K/mo: Modal, Together, Replicate, Vercel AI Gateway.
• $2K-20K/mo: Modal at scale, Together/Fireworks, Baseten.
• $20K+/mo: Mix; consider RunPod for cost; SageMaker for enterprise.

3. Compliance / data residency?

• Standard: any.
• EU-strict: Together / Fireworks have EU regions; Modal has region selection.
• HIPAA / FedRAMP: AWS SageMaker, Azure ML, dedicated tenancy.

4. Team profile?

• Python ML team: Modal, Beam.
• Data team: Databricks ML, Vertex AI.
• Enterprise: SageMaker, Anyscale.
• Indie / startup: Modal + Replicate + Vercel AI Gateway.

5. Iteration speed needs?

• Daily experiments: Modal, Replicate.
• Production stability: Baseten, SageMaker.
• Both: Modal for dev, Baseten for prod.

Verdict

For 2026 ML inference + GPU hosting:

• Default for Python ML teams: Modal. Boring, works, fits Python workflows.
• Pre-built OSS models: Replicate. Click and go.
• OSS LLM inference: Together AI (broad catalog) or Fireworks (latency).
• Cheapest GPU: RunPod. Cost-sensitive workloads.
• Production deployment + monitoring: Baseten.
• Bare GPU rental: Lambda Labs or CoreWeave.
• Enterprise platform: SageMaker, Vertex AI, or Anyscale.
• Vercel-native AI: Vercel AI Gateway + Modal for custom.

The most common mistake in 2026: hosting OSS LLMs yourself when Together/Fireworks would be 10x simpler at competitive prices. Self-host only when (a) you've outgrown them on cost, (b) data residency requires it, or (c) you have a custom model not on those platforms.

The second most common mistake: defaulting to AWS SageMaker for everything because the team is already on AWS. SageMaker is comprehensive but slow to iterate. Modal + Vercel AI Gateway ships 5x faster for most use cases.

The third mistake: ignoring caching. Inference is expensive; caching is free. Cache aggressively at every layer (response cache, prompt cache, embedding cache, RAG cache).

See Also

• AI Models — foundation models hosted by these platforms
• AI SDK — pairs for application-side AI
• LLM Observability Providers — pairs for monitoring
• LLM Cost Optimization (VibeWeek) — implementation discipline
• Voice AI Providers — adjacent (specific to voice)
• Vector Database Providers — adjacent (vector storage)
• Vector Databases — adjacent
• Cloud Development Environments — sister category (dev not inference)
• Vercel Sandbox — adjacent for code-execution sandboxes
• Cloud-and-Hosting Providers — broader hosting context
• Replicate — provider-specific deeper guide
• Modal in CDE article — Modal also covered there for ML CDE use case