Best AI Model Deployment Platforms (2026): Top 10 Compared

Amazon SageMaker AI is the AWS service for building, training and deploying machine learning models. Its inference layer covers four modes. Real-time endpoints serve low-latency requests. Serverless inference scales with traffic and bills by inference duration. Asynchronous inference handles long-running requests, and batch transform processes whole datasets.

AWS states that SageMaker AI offers more than 100 instance types. Several models can be deployed to one instance to use the accelerator more fully, and autoscaling shuts instances down when there is no traffic. The service also recommends inference configurations from a buyer's cost, throughput and latency targets, and AWS says it provisions from a prioritized instance pool when capacity is constrained.

Pricing is by instance type and duration. Buyers can pay on demand with no minimum fee, or commit to a consistent amount of usage through SageMaker Savings Plans. The free tier covers 125 hours of real-time inference and 150,000 seconds of serverless inference per month for the first two months. AWS points buyers to its pricing calculator for full estimates.

Google renamed Vertex AI as Gemini Enterprise Agent Platform. The product page says the capabilities of Vertex AI continue inside the new platform, which Google positions around building, scaling and governing AI agents. Model deployment remains part of it: teams can serve custom-trained models, AutoML models and models from Model Garden, which lists more than 200 Google, third-party and open models such as Gemini, Claude and Gemma.

The pricing page states that Agent Platform costs match the legacy AI Platform and AutoML products it replaced, with two exceptions. Some lower-cost machine types are no longer supported, and scale-to-zero, which the legacy prediction service offered, is not supported for Agent Platform Inference. To offset that, Google lists co-hosting of models, an optimized TensorFlow runtime and billing in 30-second increments.

A model deployed to an endpoint is charged until it is undeployed, whether or not it serves predictions. Buyers with idle periods should plan undeployment or co-hosting. New Google Cloud customers receive up to $300 in credits.

Databricks Model Serving deploys classical machine learning models, generative AI models and AI agents as endpoints inside the Databricks platform. It serves custom models such as PyFunc, scikit-learn and LangChain models, open models such as Llama and Mistral, and fine-tuned versions trained on a customer's own data. It can also route to proprietary models hosted elsewhere, including Azure OpenAI, AWS Bedrock and Anthropic.

Databricks builds the containers and manages the infrastructure on CPUs and GPUs. The same served models can run large-scale batch inference through AI Functions from Databricks SQL, notebooks and workflows. Governance runs through Unity Catalog and its gateway, with lineage and monitoring of outputs.

Pricing is expressed in DBUs per hour. A T4 costs 10.48 DBUs an hour, one A10G 20, one L40S 44.86 and one H100 100. The price of a DBU depends on the cloud, region and tier, so buyers need their own contract rate to compare Databricks with vendors that publish dollar prices.

Hugging Face Inference Endpoints is a managed service for deploying models to dedicated infrastructure from the Hugging Face Hub. A team picks a model, an inference engine and a hardware size, and Hugging Face runs the endpoint without the buyer managing Kubernetes, CUDA versions or network setup.

Supported engines include vLLM, TGI, SGLang, TEI and custom containers. Endpoints scale up as traffic grows and down as it falls, and logs and metrics support debugging. The catalog shows ready configurations, for example a quantized Qwen model on one Nvidia T4 at $0.50 an hour and larger models on four A100s at $10 an hour. CPU instances run on AWS, Azure and Google Cloud.

Self-serve customers pay per minute and receive a monthly bill with email support. The Enterprise plan adds lower marginal costs at volume, uptime guarantees, custom annual contracts, dedicated support and SLAs. Hugging Face also sells separate Hub team plans at $20 and $50 per user a month, which are not required for Inference Endpoints.

Baseten is an inference platform for running custom, fine-tuned and open-source models in production. Teams can start from its model library or package any model with Truss, an open-source standard for packaging and serving models from any framework. The platform also offers Model APIs, which serve pre-optimized popular models at per-token prices, and model training.

Dedicated deployments are billed per minute of compute. The published rates run from CPU instances up to an H100 at $0.10833 a minute and a B200 at $0.16633 a minute. Baseten states that customers pay while a model is deploying, scaling or serving, not while it is idle, and that customers control how it scales.

Basic includes dedicated deployments, Model APIs, training, fast cold starts and chat support. Pro adds unlimited autoscaling, priority access to scarce GPUs, higher rate limits and Slack support. Enterprise adds custom SLAs, self-hosted deployments, the use of existing cloud commitments, data residency control and custom regions.

Modal is a serverless compute platform for running Python code on CPUs and GPUs, including model inference, batch jobs, scheduled functions and web endpoints. It builds containers, scales them with request volume and shuts them down when traffic stops, so buyers pay only for compute time.

Rates are published per second. An Nvidia B200 costs $0.001736 a second and an H100 $0.001097, about $3.95 an hour. CPU is $0.0000131 per physical core per second and memory is billed per GiB. Volumes include 1 TiB a month free. Region selection adds a 1.5x to 1.75x multiplier, and non-preemptible execution costs 3x base prices.

Starter is free with $30 of monthly credits, three seats and 10 concurrent GPUs. Team costs $250 a month with $100 of credits, unlimited seats, 50 concurrent GPUs, custom domains and deployment rollbacks. Enterprise adds higher concurrency, private Slack support, embedded ML engineering, audit logs, Okta SSO and HIPAA. Modal offers credit grants for startups and academics.

Replicate runs machine learning models behind an API. Its catalog holds thousands of open-source models contributed by the community and a range of proprietary models for images, video, speech and text. Developers can also deploy their own models with Cog, Replicate's open-source packaging tool.

Billing depends on the model type. Most public models are charged for the time they run on a given GPU, and some are priced per output, for example per image or per million tokens. Each model page shows an estimate. Private models run on dedicated hardware, so they avoid a shared queue, but they are charged for all the time instances are online, including setup and idle time. Fast-booting fine-tunes are the exception and bill only while active.

Deployments let a team fix the hardware and scaling settings of any model. Replicate's billing documentation states that a well-tuned deployment costs only slightly more than a public model. Published hardware rates include an H100 at $5.49 an hour and an A100 80 GB at $5.04 an hour.

Together AI is an AI cloud built around open models. Its inference products come in three forms. Serverless inference serves a catalog of chat, vision, image, audio, video, transcription, embedding and rerank models by the token. Provisioned throughput reserves capacity. Dedicated inference runs a chosen model on reserved hardware.

The pricing page states that most teams start with serverless inference and move to dedicated endpoints at scale. Serverless prices vary by model, and several models list a lower price for cached input. Examples include gpt-oss-120B at $0.15 per million input tokens and $0.60 per million output tokens, and gpt-oss-20B at $0.05 and $0.20. A batch API offers separate prices for non-urgent work.

Beyond inference, Together AI sells GPU clusters, sandboxes, managed storage and fine-tuning. It recently announced on-demand B200s for its GPU clusters. Buyers who need dedicated capacity contact the sales team for a quote.

Fireworks AI is an inference platform for open models. Buyers can start on serverless inference, which is priced per token with no setup and no cold starts, and move to on-demand deployments, which reserve GPUs for one model. Enterprise deployments with higher rate limits are sold through sales.

On-demand deployments are billed per GPU second. The pricing page lists both the rates that applied until 31 August and the rates from 1 September 2026: an H100 or H200 rose from $7.00 to $8.00 an hour and a B200 from $10.00 to $13.00. The documentation states that deployments scale to zero replicas by default when unused, with no minimum cost at zero, but that charges accrue while replicas are active even without API calls.

Fireworks also sells managed training. Supervised and preference fine-tuning are priced per million training tokens by model size, and reinforcement fine-tuning is billed per GPU hour. Fine-tuned models are served at the same price as their base models.

Simplismart is an inference platform for open-source and custom models. Buyers can pick from more than 150 models, including language, vision, diffusion and speech models, or import custom weights from more than ten cloud repositories.

Deployment runs in Simplismart's cloud, in a private VPC or on premises. One control plane manages deployments across more than 15 clouds, with B200, H100, A100, L40S and A10G GPUs available. Simplismart states that it scales up in under 500 milliseconds, scales to zero with traffic and can scale on specific metrics to meet strict SLAs. It tunes each workload, for example lower latency for voice agents and higher throughput for document processing, using custom CUDA kernels, quantization and caching.

The pricing page offers usage-based APIs for shared models, dedicated GPUs priced per GPU hour and a pay-as-you-go option for training jobs, including LoRA, QLoRA and DPO fine-tuning. Private cloud and on-premises deployments are priced after a consultation.