Building something with AI usually means two very different jobs. First you (or someone else) trains a model. Then, every single time your app needs an answer, that model has to actually run — read the input, do the math, produce an output. That second part is called inference, and it's the part that costs you money and time on every request, forever, unlike training which happens once. Renting your own GPU server for this is expensive and wasteful if traffic is spiky, so a whole category of platforms exists to handle inference for you: some run your own custom code on GPUs, some give you one API key that talks to hundreds of ready-made models, and some skip the cloud entirely and run models on the device in front of you. Here are 10 real platforms covering all three approaches.
The short version
If you need to run your own custom Python code (fine-tuning, custom pipelines) on GPUs with per-second billing, Modal is the cleanest option, with TrueFoundry as the cloud-agnostic, more enterprise-flavored alternative. If you just want to call an open-source language model through an API without owning any infrastructure, Fireworks AI and Zro both fit, with Zro specifically tuned for AI coding agents. For image, video and audio generation models, fal and WaveSpeedAI are the two to compare. If you want one endpoint that routes across dozens of providers instead of integrating each one separately, look at Requesty or Oxlo — Requesty for a flat 5% markup on usage, Oxlo for a flat-rate monthly plan instead of per-token billing. And if the goal is running models locally with zero cloud cost or data leaving the machine, Cactus Compute (mobile/edge) and KTransformers (desktop, huge models) are both free and open source.
1. Modal — best for running your own code on GPUs
Modal is a serverless cloud built specifically for Python: you write plain Python, and it runs on GPU-backed containers with sub-second cold starts and true pay-per-second billing (no charge while your code isn't running). It's aimed at teams that need to run custom training or inference code, not just call a pre-built model API.
Price: Starter free with $30/month in compute credits; Team $250/month plus compute costs with $100/month credits; GPU compute from $0.000164/second, no idle charges.
Good for: AI/ML engineers who need to run custom Python workloads, not just hit an existing model's API.
Watch out for: the platform itself is proprietary — only the client SDK is open source — and always-on, high-volume inference can end up cheaper on reserved instances than on Modal's per-second pricing.
2. Fireworks AI — best for fine-tuning plus inference in one place
Fireworks AI serves and fine-tunes open-source language models through an API that's compatible with the OpenAI and Anthropic formats, so switching over from a closed-model provider doesn't mean rewriting your integration. Fine-tuning (including LoRA, DPO and RL) is bundled in rather than sold as a separate product.
Price: Serverless from about $0.10 per million input tokens on small models up to $0.90 on large ones; dedicated GPUs from $7-$12/hour (H100 to B300); fine-tuning from about $0.50 per million training tokens (LoRA).
Good for: teams that want inference and fine-tuning under one bill instead of stitching two vendors together.
Watch out for: pricing is broadly similar to Together AI, so there's little differentiation on cost alone, and Groq's custom chips beat it on raw speed for the models Groq supports.
3. fal — best marketplace for image, video and audio models
fal works like an app store for generative AI: over 1,000 ready-to-call models for images, video, audio and 3D, all behind one API, so you don't have to host each model yourself. You're only billed for generations that actually succeed — failed runs and queue time are free.
Price: Pay-per-output on prepaid credits. Images from $0.003-$0.04 each, video $0.05-$0.40/second; dedicated GPU compute from $1.89/hour (H100) up to $8.50/hour (B300) if you need a custom model at scale.
Good for: developers and product teams adding AI image/video/audio generation without managing GPU infrastructure themselves.
Watch out for: output quality depends entirely on which third-party model you pick — fal has no model identity of its own — and comparing total cost across such a large catalog of per-model prices is genuinely hard.
4. WaveSpeedAI — best for latency-sensitive generation
WaveSpeedAI is fal's most direct competitor: also 1,000+ image, video, audio and language models behind one endpoint, but positioned specifically on speed — sub-1-second latency and no cold starts, with a real open-source project (waverless) backing the marketing claim.
Price: Pay-per-use, no monthly commitment, $1 free credit with no card required. Images $0.005-$0.008 each, video $0.01-$0.15/second, LLMs billed per million tokens (e.g. Claude Opus at $5/$25 input/output per million). Four volume tiers (Bronze to Ultra) add discounts.
Good for: developers building latency-sensitive, interactive AI features (real-time generation, not batch jobs).
Watch out for: it's a 2025-founded challenger going up against more established players like fal and Replicate, and its latency claims are worth benchmarking on your own workload before committing.
5. TrueFoundry — best cloud-agnostic deployment control panel
TrueFoundry is a Kubernetes-based control panel for getting models and AI agents from a laptop into production — deployment, scaling and governance — built to run on whatever cloud you already use rather than locking you into one. It acquired Seldon AI in 2024 to strengthen AI-agent deployment specifically.
Price: Developer free (50K requests/month, 3 users); Pro $499/month (1M requests/month, 10 users); Pro Plus $2,999/month (same 1M request cap, 25 users); Enterprise custom (10M+ requests/month, VPC/on-prem).
Good for: MLOps/platform teams deploying models and agents across multiple clouds who specifically want to avoid vendor lock-in.
Watch out for: the jump from Pro to Pro Plus ($499 to $2,999) for the same request cap is confusing — the real cost driver is seats and support, not usage — and it's more setup than a small team needs compared to a fully managed serverless option like Modal.
6. Requesty — best flat-markup model gateway
Requesty gives you one OpenAI-compatible endpoint that talks to 600+ models across 30+ providers, so switching models — or spreading requests across several for cost or reliability — doesn't mean rewriting your app. It charges a flat 5% markup on top of whatever the underlying model costs, instead of a subscription.
Price: Free tier (200 requests/day, free models only); pay-as-you-go with a flat 5% markup on model cost, no subscription, $10 free credit to start; Enterprise on request (SSO, RBAC, audit logs, EU data residency).
Good for: teams that want a simple, predictable fee structure rather than per-model markup tables.
Watch out for: it's entering an already crowded field (OpenRouter, Portkey are well established), and its usage stats are self-reported with no public GitHub trail to gauge real traction.
7. Zro — best model gateway for AI coding agents
Zro is a narrower gateway: it lets coding agents like Claude Code or Cursor run on open-weight models instead of a big-name API, with a guarantee that no code is ever stored or used for training. It's specifically optimized for long, multi-turn coding sessions and is compatible with the OpenAI/Anthropic API format.
Price: Pro $20/month (~300M tokens); Max $60/month (~1.5B tokens); Enterprise on request; pay-as-you-go packs from $5-$100 (expire after 90 days). No free tier.
Good for: developers who want a cheaper, private backend for AI coding tools without switching tools.
Watch out for: there's no free tier to try before paying, and open-weight models served here won't match frontier models like Claude or GPT on hard reasoning tasks.
8. Oxlo — best flat-rate alternative to per-token billing
Oxlo takes a phone-plan approach instead of a taxi-meter one: one predictable monthly price for a set number of requests across 40+ models, rather than watching a per-token bill climb with every word generated. It's compatible with a one-line base-URL swap from the OpenAI API.
Price: Free (60 requests/day, no card); Pro $80/month (1,000 requests/day); Premium $350/month (5,000 requests/day); Enterprise on request with volume discounts up to $20,000/month spend.
Good for: teams that want a predictable infrastructure bill instead of a variable per-token one.
Watch out for: the cap is on request count, not tokens, so a handful of very long conversations can burn through your quota fast, and it's a worse deal for low or irregular usage.
9. Cactus Compute — best free on-device runtime for mobile
Cactus Compute is an open-source runtime that runs language, vision and speech models directly on phones, laptops and wearables — no round-trip to the cloud, no per-request cost. It supports hybrid routing between on-device and cloud rather than forcing an all-or-nothing choice.
Price: Free, MIT license, no paid tier disclosed publicly.
Good for: mobile and edge developers building privacy-sensitive AI features that need to work offline.
Watch out for: it's a real engineering integration, not a drop-in API, and on-device hardware still caps which model sizes are practical.
10. KTransformers — best for running huge models on ordinary hardware
KTransformers is a free, open-source tool that lets a regular desktop run AI models that would normally need a room full of server GPUs, by splitting the work intelligently between your CPU and a much smaller GPU. It's specialized for Mixture-of-Experts models like DeepSeek-V3 and Qwen3.
Price: Completely free and open source (Apache 2.0).
Good for: researchers, ML engineers and advanced hobbyists who want to run large MoE models without a GPU cluster.
Watch out for: it's a technical setup, not a beginner-friendly tool, performance gains depend heavily on your specific hardware, and documentation is thinner than more established projects like llama.cpp.
| Tool | Best for | Starting price | Deployment |
|---|---|---|---|
| Modal | Custom Python code on GPUs | Free + $30/mo credits | Cloud, serverless |
| Fireworks AI | LLM inference + fine-tuning | ~$0.10/1M tokens | Cloud, serverless |
| fal | Image/video/audio model marketplace | $0.003/image | Cloud, serverless |
| WaveSpeedAI | Low-latency generation | $0.005/image | Cloud, serverless |
| TrueFoundry | Multi-cloud deployment control panel | Free (50K req/mo) | Cloud, self-hosted (K8s) |
| Requesty | Flat-markup model gateway | Free + 5% markup | Cloud, API |
| Zro | Gateway for AI coding agents | $20/mo | Cloud, API |
| Oxlo | Flat-rate model access | Free (60 req/day) | Cloud, API |
| Cactus Compute | On-device mobile/edge AI | Free, open source | On-device |
| KTransformers | Huge models on a desktop | Free, open source | On-device |
The right pick here depends less on which tool is "best" and more on which of the three jobs you actually have: running your own code (Modal, TrueFoundry), calling someone else's model through an API (Fireworks AI, fal, WaveSpeedAI, Requesty, Zro, Oxlo), or avoiding the cloud entirely (Cactus Compute, KTransformers). Several of these are young companies with self-reported benchmarks and undisclosed funding, so treat latency and cost claims as a starting point for your own testing, not a guarantee.