TL;DR
- Best for production inference in your own cloud or data center: Simplismart. Shared APIs, dedicated GPUs, BYOC on AWS, GCP, or Azure, and air-gapped clusters from one control plane. An AWS case study reports up to 40% lower infrastructure costs.
- Best managed platform for US AI-native teams: Baseten. Dedicated inference and model APIs used by Cursor, Notion, and OpenEvidence, with SOC 2 Type II and HIPAA on every plan.
- Best for open-model variety plus training capacity: Together AI. Serverless, batch, dedicated endpoints and GPU clusters from one vendor.
- Best for fine-tuned open models: Fireworks AI. Supervised, preference, and RL training, with tuned models served at base-model prices.
- Best for image and video generation: fal. 1,000+ media models behind one API, priced per output.
Getting a model to work is rarely the hard part anymore. Keeping it fast, available, and affordable once real traffic arrives is where most teams struggle: GPU bills climb, latency spikes during peak hours, and compliance teams start asking where prompts and data actually go. AI inference platforms solve that by deploying, scaling, and optimizing models in production so your engineers don’t have to build that layer themselves.
The 10 platforms below take very different approaches. Some sell per-token access to hosted models, some give you dedicated GPUs, and a few run inside your own cloud account or data center. This guide compares them on deployment options, pricing, workload fit, and published customer results.
What is an AI Inference Platform?
An AI inference platform sits between GPUs and your application. It takes a model (open-source, fine-tuned, or your own weights), deploys it, exposes it as an API, batch job, or streaming endpoint, autoscales it, and monitors it. An inference engine such as vLLM runs the model efficiently; a GPU cloud rents you the hardware; the platform wraps both so the model can carry production traffic.
The choice now carries real weight: Deloitte predicts inference will account for roughly two-thirds of all AI compute in 2026. At the same time, per-token prices keep falling. Stanford’s 2025 AI Index found that GPT-3.5-level inference dropped from $20 to $0.07 per million tokens between November 2022 and October 2024. At those prices, most of the bill comes from volume, idle GPUs, and utilization, which is exactly what an inference platform is meant to manage.
Recommended read: What Is an AI Inference Platform? A Complete Guide for 2026
Top 10 AI Inference Platforms Compared
The table compares each platform on where it runs, what it does best, how it charges, and where it falls short.
How We Evaluated These Platforms
We reviewed more than 20 inference vendors that appear in buyer guides, practitioner discussions, and our own competitive research. We kept the 10 that a team could realistically use to serve production models. We left out products that solve a narrower problem: raw GPU capacity providers (such as RunPod and Vast.ai), speech-only APIs (such as AssemblyAI and Deepgram), and model routers (such as OpenRouter).
Each platform was assessed on the same six criteria:
We did not run one standardized benchmark across all 10 platforms, because latency and cost depend heavily on the model, GPU, and traffic pattern. The two-week evaluation plan later in this guide shows how to run that test on your own workload.
The Top 10 AI Inference Platforms in 2026: A Detailed Breakdown
1. Simplismart
Simplismart is an AI inference platform for deploying, optimizing, fine-tuning, and scaling open-source and custom generative AI models in production. It is built on the idea that one inference setup cannot fit every workload: a voice agent needs a fast first response, a document pipeline needs throughput, and a content-generation app needs a low cost per output. Simplismart tunes the serving stack for each deployment and runs it wherever the workload has to live.
That flexibility is the main reason teams shortlist it. The same platform covers pay-as-you-go APIs for 150+ open models, dedicated GPUs from NVIDIA T4 to B200, a managed cluster that Simplismart provisions in your AWS, GCP, or Azure account (typically running in about 30 minutes, according to its BYOC guide), and any Kubernetes cluster you already run, including neoclouds and on-prem hardware. It also supports Slurm clusters and air-gapped environments. It is designed for engineering teams moving off closed APIs or DIY stacks, especially where data residency, existing cloud commitments, or non-LLM workloads such as speech, vision, and diffusion are part of the picture.
Key Capabilities
- Workload-specific optimization: quantization (FP8, INT4), custom CUDA kernels, speculative decoding and continuous batching, chosen per deployment
- One control plane across shared, dedicated, BYOC, imported Kubernetes, on-prem and air-gapped deployments.
- Autoscaling on latency, concurrency, and memory signals, with scale-to-zero
- Fine-tuning with LoRA, QLoRA, SFT, RFT, GRPO and DPO, plus a benchmarking suite for performance and quality
- Observability: metrics export to Prometheus, Datadog and New Relic, with alerts to Slack, PagerDuty and other channels
- Developer tooling: REST APIs, a Python SDK and a CLI
- Compliance and region: ISO 27001, SOC 2 Type 2, GDPR and HIPAA badges; a Mumbai endpoint and INR billing for Indian teams
Why Teams Choose It
- Data and models stay in your own account or data center, while you still get a managed scaling and optimization layer.
- Existing AWS, GCP, or Azure commitments can be used instead of paying a second GPU bill.
- The same GPUs serve more requests at lower cost because the stack is tuned per model and workload.
- Deployments reach production faster: Invideo moved from proof of concept to production in 3 to 4 days instead of about 2 weeks, and Lyric went live in 2 days.
Customer Results
- An AWS case study reports up to 40% lower infrastructure costs for customers’ generative AI workloads and 60- to 70-second scale-up using EC2 Auto Scaling warm pools.
- Tata 1mg replaced paid APIs with fine-tuned open vision-language models in its own VPC, raising prescription-parsing accuracy from about 80% to 95% and cutting training time from 3 days to about 1.5 hours.
- Sanas moved its Gemma 3 voicebot model from H100 to A10G with INT4 quantization, kept 99.99% accuracy, and cut cost by 53%.
- Ema cut p50 agent-routing latency from 10 seconds to 0.9 seconds.
Pricing
- Model APIs from $0.06 per million tokens (Gemma 3 1B); Llama 3.1 8B at $0.13; Whisper v3 Turbo at $0.0018 per audio minute; Flux Dev at $0.03 per image
- Dedicated GPUs from $1.20 (T4) to $5.20 (H200) per GPU-hour, with B200 and reserved capacity on request
- BYOC and on-prem priced by consultation.
Best fit: engineering teams running production GenAI that must stay in their own cloud or data center, teams with cloud commitments to use, and workloads such as voice agents, transcription, document AI, and image or video pipelines that need tuning for latency, throughput, or cost.
Not the right fit: teams that only need a closed frontier model such as GPT or Claude, since Simplismart serves open-source and custom models.
2. Baseten
Baseten is a managed inference platform for running open-source and custom models in production. Teams define a model with a configuration file, a Python model class, or a custom Docker server, deploy it with the Baseten CLI, and get an autoscaling endpoint on dedicated GPUs with control over hardware, scaling, and releases. Alongside dedicated deployments, it offers per-token model APIs for curated models, a training product, and specialized serving for embeddings, transcription, and text-to-speech, plus Chains for multi-step pipelines that call several models.
It is built for AI-native product companies that want inference handled by a vendor rather than an internal platform team. Its customer list includes Abridge, Cursor, Notion, OpenEvidence, Ramp, and Wayfair. Most customers run on Baseten Cloud; the Enterprise plan adds self-hosted deployment in your own VPC, hybrid setups, and data-residency control.
Key Capabilities
- Dedicated deployments with fast cold starts and per-minute billing
- Model APIs with separate input, cached-input, and output pricing
- Embeddings, transcription and TTS inference stacks; Chains for multi-model pipelines
- Training, plus self-hosted and hybrid deployment on Enterprise
Strengths
- Strong reference base: Baseten reports a 3x speed improvement for OpenEvidence’s embeddings and sub-300 ms transcription for ClickUp
- SOC 2 Type II and HIPAA included on every plan, including the free Basic tier
- Hands-on engineering support on higher plans
Limitations
- Self-hosting and data-residency control require the Enterprise plan
- On-prem, air-gapped, and Slurm deployments are not listed
Pricing: Basic plan is $0 a month plus usage. H100 is $0.10833 per minute (about $6.50 an hour) and A10G is $0.02012 per minute. gpt-oss-120B costs $0.10 per million input tokens and $0.50 per million output tokens.
Best fit: US product teams that want a widely used vendor for dedicated inference on Baseten’s cloud.
Not the right fit: teams that need on-prem or air-gapped deployment, or self-hosting without an enterprise contract.
3. Together AI
Together AI positions itself as an AI-native cloud that combines inference, model shaping, and GPU infrastructure under one account. On the inference side, it offers serverless and batch APIs across a large open-model catalog, provisioned throughput for reserved capacity, and dedicated endpoints for both listed models and your own containers. On the compute side, it rents GPU clusters (up to NVIDIA GB300) on demand or by reservation, and it adds fine-tuning, custom training, and evaluations on top.
Its research team is behind FlashAttention, ThunderKittens and the ATLAS adaptive speculative decoding work, which shows up as fast support for newly released open models. It suits AI-native companies that want many models and training capacity from one vendor; customers named on its site include Cursor, Cognition, Zoom, ElevenLabs, Salesforce and Decagon.
Key Capabilities
- Serverless and batch inference across a large open-model catalog
- Dedicated endpoints and provisioned throughput for predictable capacity
- On-demand and reserved GPU clusters for training
- Supervised and preference fine-tuning, plus evaluations
Strengths
- Broad, quickly updated open-model catalog
- Inference and training capacity from one vendor
- Customer evidence: Together reports that Decagon reached about 6x lower cost per turn than gpt-5 mini
Limitations
- Runs only on Together’s infrastructure; no BYOC, VPC, or on-prem option is listed on its pricing page.
Pricing: gpt-oss-120B at $0.15 per million input tokens and $0.60 per million output tokens. Dedicated H100 endpoints cost $5.49 per GPU-hour on demand and B200 $8.99; GPU clusters start at $3.99 per H100-hour, lower with longer reservations.
Best Fit: teams that switch between many open models, run large batch jobs, or need training clusters alongside inference.
Not the Right Fit: workloads that must run inside your own cloud account or data center.
4. Fireworks AI
Fireworks AI focuses on helping teams own a specialized model: start with an open model, shape it with training, then serve it in production. Training ranges from guided runs and configuration-led jobs to fully custom logic where you bring your own loss function, trainer, and RL loop. Inference comes in three forms (serverless per token, on-demand dedicated deployments, and reserved capacity), and fine-tuned models are served at base-model prices. Fireworks Nexus adds routing across models as a drop-in replacement for closed-model APIs.
The serverless API is compatible with both OpenAI and Anthropic clients, which keeps migration from closed models simple, and Fireworks is also available through Microsoft Azure AI Foundry, where UiPath uses it. It is aimed at product teams that want a tuned open model to replace a closed one; named customers include Cursor, Vercel, Notion, Quora, Sourcegraph, and Cresta.
Key Capabilities
- Serverless, on-demand, and reserved inference
- Managed fine-tuning (LoRA or full, SFT or DPO) and custom RL training loops
- Fireworks Nexus for routing requests across models
- OpenAI- and Anthropic-compatible APIs
Strengths
- Post-training and serving in one place, with no price premium for fine-tuned models
- Customer evidence: Fireworks reports that Notion cut latency from about 2 seconds to 350 ms after fine-tuning, and Quora saw a 3x response-time speedup
Limitations
- On-demand GPU rates are higher than several platforms in this list
- Region-restricted deployments cost 1.5x; no BYOC option is listed
Pricing: serverless usage is billed per token. On-demand deployments are billed per GPU-second, with H100 at $8.00 an hour and B200 at $13.00 an hour (pricing).
Best fit: coding assistants, agents, and search products that need a tuned open model at production scale.
Not the Right Fit: teams that need inference inside their own VPC.
5. Modal
Modal is a serverless cloud for AI workloads rather than an inference-only product. Developers describe functions, dependencies, and GPU requirements in Python; Modal builds the containers, provisions GPUs on demand from a capacity pool spanning 20+ clouds, routes work to available hardware and regions, and bills per second. The same runtime serves LLM, image, video, and audio models in real time or batch, and also runs training jobs, notebooks, and isolated sandboxes for coding agents and RL rollouts.
Because you write the serving code yourself (vLLM, SGLang or a custom server), Modal suits engineering teams that want elastic GPUs without managing Kubernetes but are happy to own the inference stack. Customers named on its site include Cognition, which uses it for RL infrastructure and production inference, DoorDash, and Ramp.
Key Capabilities
- Python SDK that maps code, dependencies, and GPU needs to containers
- Per-second autoscaling, including scale-to-zero
- HTTP endpoints, batch jobs, sandboxes, and training on the same runtime
Strengths
- Very fast path from Python code to a GPU endpoint
- Good fit for spiky and batch traffic thanks to per-second billing
- Customer evidence: TypeSafe AI says it served 1 trillion tokens within three days of launch on Modal
Limitations
- You choose, configure, and tune the serving engine yourself
- No deployment into your own cloud account; HIPAA, SSO, and audit logs are Enterprise-only
Pricing: Starter is $0 a month plus compute, with $30 of monthly credits; Team is $250 a month plus compute, with $100 of credits. H100 costs $0.001097 per second (about $3.95 an hour).
Best fit: small Python teams with bursty or batch workloads that are comfortable owning the serving stack.
Not the right fit: teams that want model-level optimization handled for them.
6. GroqCloud
Groq designs its own inference chip, the LPU (Language Processing Unit), and runs it in its own data centers rather than selling software for GPUs. Developers reach it through GroqCloud, a hosted API with free and pay-as-you-go developer tiers covering LLMs such as gpt-oss-120B, speech-to-text models such as Whisper Large V3 and text-to-speech models such as Orpheus. Larger customers can buy dedicated bare-metal capacity, an inference stack for production use and governance features, across 11 listed regions in North America, Europe, the Middle East and Australia.
Groq’s appeal is low per-user latency on the models it hosts, which matters most for chat, agents and voice. The company changed shape in December 2025, when it signed a non-exclusive licensing agreement with NVIDIA and its founder moved to NVIDIA; Groq said GroqCloud would continue operating independently.
Key Capabilities
- Hosted models such as gpt-oss-120B, Whisper Large V3 and Orpheus TTS
- Dedicated bare-metal capacity for larger customers
- Batch processing on the Developer tier
Strengths
- Custom hardware designed for low per-user latency on supported LLMs
- Simple per-token pricing for hosted models
Limitations
- You can only use models Groq hosts; custom weights and fine-tuning are not listed.
- No image or video generation, and no deployment in your own cloud
Pricing: gpt-oss-120B at $0.15 per million input tokens and $0.60 per million output tokens; Whisper Large V3 Turbo at $0.04 per hour of audio.
Best fit: chat, agent, and voice products where time to first token matters most, on a model Groq supports.
Not the right fit: teams running custom or fine-tuned models.
7. fal
fal is an inference platform built specifically for generative media. Its hosted catalog covers 1,000+ image, video, audio, and 3D models behind a single API, so a product can switch between image generators or video models without new integrations. Teams with their own models can deploy them on fal’s serverless GPUs, which scale from zero across globally distributed capacity, use private endpoints for fine-tuned or custom weights, or rent dedicated H100, H200, and B200 clusters for training.
fal is aimed at consumer and creative apps whose costs track media output rather than tokens, which is why most of its catalog is priced per image or per second of video. Enterprise customers get SOC 2 compliance, SSO, usage analytics, and 24/7 priority support.
Key capabilities
- Hosted image, video, audio, and 3D models through one API
- Serverless GPUs that scale from zero for custom models
- Private endpoints and dedicated GPU clusters
Strengths
- Very wide media model catalog with no setup
- Per-output pricing that maps directly to product usage
Limitations
- Not designed for LLM-heavy products
- Runs on Fal’s infrastructure only
Pricing: hosted models are priced per output, for example, $0.07 per second of video for Kling Video v2.5 Turbo Pro. H100 GPUs start at $4.50 an hour at list price, dropping to $2.49 on its lowest tier.
Best fit: consumer and creative apps that call many image and video models.
Not the right fit: LLM agents or workloads with strict data-residency needs.
8. Bento Inference Platform
BentoML started as an open-source framework that packages a model, its dependencies, and inference code into a versioned, portable artifact called a Bento. The managed Bento Inference Platform runs those packages as real-time APIs, asynchronous jobs, batch jobs, or multi-model pipelines, with autoscaling, scale-to-zero, CI/CD, and observability. Deployments can run on Bento’s own cloud or in your account through BYOC, with VPC and on-prem options on enterprise plans.
Its code-centric approach suits teams that already write Python serving logic and want the same package to run across clouds, including teams mixing traditional ML models (PyTorch, TensorFlow, scikit-learn) with LLMs. Modular acquired BentoML, and BentoML’s sign-up and pricing now run through Modular, so the platform is likely to converge with Modular’s own inference stack.
Key capabilities
- Portable Bento packages that run across clouds and on-prem
- Multi-framework serving for PyTorch, TensorFlow, and scikit-learn as well as LLMs
- Autoscaling and scale-to-zero on managed infrastructure or your own
Strengths
- Natural path for teams already using open-source BentoML
- Handles traditional ML and GenAI models on one platform
Limitations
- Product direction is changing as it merges into Modular’s stack
- Model-level optimization is largely left to your team
Pricing: pay-as-you-go compute billed per second, for example, a T4 at $0.00014 per second; enterprise plans by quote.
Best fit: teams with existing BentoML code or a mix of classic ML and GenAI models.
Not the right fit: teams that want the vendor to tune models for them.
9. Hyperscaler Services: SageMaker and Bedrock, Vertex AI, Azure AI Foundry
Each major cloud offers its own inference services, and they usually come in two forms. On AWS, Amazon SageMaker deploys models you bring to real-time, serverless, asynchronous, or batch endpoints on instance types you choose, while Amazon Bedrock gives per-token API access to foundation models from Amazon and other providers. Google Vertex AI offers online and batch prediction endpoints plus Model Garden, which includes Gemini and open models. Microsoft Azure AI Foundry combines serverless model APIs, including third-party providers such as Fireworks, with managed compute you size yourself.
Teams pick these services mainly because everything else already lives in that cloud: identity and access, networking, security reviews, procurement, and committed spend. They are general machine learning platforms first, so inference-specific tuning such as custom kernels, batching strategy, and autoscaling signals is largely left to you.
Strengths
- Reuse existing IAM, networking, procurement, and committed spend
- Broad catalogs that include each cloud’s own models
Limitations
- Less inference-specific optimization than specialized platforms
- GPU quotas can be hard to get in some regions, and there is no path to another cloud
Pricing: per instance-hour for managed endpoints and per token for model catalogs, varying by instance type and region.
Several platforms in this list, Simplismart included, can deploy into your AWS, GCP, or Azure account, so you can keep the hyperscaler bill while adding a specialized inference layer on top.
Best fit: teams standardized on one cloud with strict procurement rules.
Not the right fit: multi-cloud strategies or latency-critical workloads that need custom kernel and batching work.
10. Self-managed vLLM or SGLang
Building your own stack means running open-source serving engines directly on your Kubernetes or Slurm cluster. vLLM, which grew out of UC Berkeley research on PagedAttention, is the most common starting point, with continuous batching, quantization, and broad hardware support. SGLang is popular for structured output and prefix-heavy workloads, NVIDIA TensorRT-LLM compiles models for maximum throughput on NVIDIA GPUs, and NVIDIA Dynamo coordinates serving across many GPUs and nodes with KV-cache-aware routing.
These are the same engines many managed platforms build on, so self-managing gives you full control over every setting with no license cost. The trade-off is that the engine is only one layer: provisioning, autoscaling, rollouts, routing, and monitoring all become your team’s job.
Strengths
- Complete control and transparency
- No vendor fees; the cost is GPUs and engineering time
Limitations
- Your team owns GPU provisioning, autoscaling, rolling upgrades, multi-model routing, and monitoring.
- Production readiness typically takes weeks or months rather than days
Pricing: free and open source; you pay for compute and people.
Best fit: platform teams with steady, high-volume traffic and inference expertise in-house.
Not the right fit: small teams, bursty traffic, or anyone who needs production SLAs quickly.
The Four Ways to Run Inference
Every platform above is a mix of four deployment models. Most teams move through them in order as volume, SLAs, and compliance needs grow.
1. Shared model APIs
You call a model the provider already runs and pay per token, image, or audio minute. Setup takes minutes, and there is nothing to scale. The trade-offs are rate limits, a fixed catalog, latency that moves with other tenants’ traffic, and prompts that leave your account.
Recommended read: What Are Shared Inference Endpoints? A Guide to Serverless LLM Architecture
2. Dedicated GPU deployments
The provider runs your model, listed or fine-tuned, on GPUs reserved for you and bills per GPU-hour, minute, or second. Latency becomes predictable, and you can tune batching, quantization, and replica counts. You pay for idle time unless the platform supports scale-to-zero, and then cold-start time becomes the number to watch.
3. Bring your own cloud (BYOC) and on-prem
The vendor’s control plane manages deployments, but GPUs and data stay in your AWS, GCP, or Azure account, your Kubernetes cluster, or your data center. Regulated teams use this to meet data-residency rules (HIPAA in the US, the DPDP Act and RBI and SEBI guidance in India), and many companies use it to spend existing cloud commitments. The vendor either provisions a cluster in your account or connects to one you already run.
Recommended read: Running GenAI in Your Cloud: The Infrastructure Layer You Don’t Have to Build
4. Build it Yourself
You run an open-source engine such as vLLM, SGLang, or TensorRT-LLM on your own cluster. There is no license fee, and nothing is hidden from you, but your team owns GPU scheduling, autoscaling, upgrades, observability, and the 3 a.m. page. A Simplismart cost analysis for Indian AI teams found managed serverless cheaper than self-hosted vLLM below roughly 1 to 2 billion tokens a month.
Recommended read: Serverless vs. Self-Hosted vLLM: 2026 Cost Breakdown for Indian AI Teams
Which Platform Fits Which Workload?
The same company can need two different setups. A voice agent is judged on how fast it answers; a document pipeline is judged on how many pages it clears per dollar. Start from the metric your users feel, then choose the deployment model and the shortlist.
Latency-bound work (voice, agents) rewards control over batching, scheduling, and GPU placement, which shared APIs rarely expose. Throughput-bound work (transcription, documents, media) rewards scale-to-zero and packing several models onto one GPU, because idle hardware is where most of the money goes.
Recommended read: How to Reduce LLM Latency: A Practical Optimisation Guide
How to Evaluate an Inference Platform in Two Weeks
Two or three finalists and two weeks of testing on your own traffic will tell you more than any published ranking. Run each finalist through the same five checks.
Days 1 to 2: Write the SLA in Numbers
Turn “fast and cheap” into targets you can fail. For chat and agents, that means time to first token (TTFT), p50 and p95 end-to-end latency, and tokens per second per user. For audio, use time to first byte (TTFB). For batch work, use throughput and a completion deadline. Add a cost target per million tokens, per request, or per audio hour at your expected monthly volume.
Days 3 to 7: Benchmark on Your Own Traffic
Use your real prompts, input lengths, and concurrency on the GPU type you would actually buy. Independent leaderboards such as Artificial Analysis help with a first filter because they compare the same model across hosting providers, but they test public APIs with standard prompts. When you compare results, check what each number measures:
- Per-user vs aggregate tokens per second: a per-user peak and a whole-GPU figure at high concurrency are different metrics.
- Precision: FP8 or INT4 results only compare with FP16 results if you also check output quality. See model quantization explained.
- Warm vs cold: record cold-start time separately from steady-state latency.
- Batching: ask whether the platform uses continuous batching and whether you can tune it.
Days 8 to 10: Test the Bad Day
Simulate a spike of 5 to 10 times normal load and measure how long the platform takes to add capacity and what p95 latency does meanwhile. Scale to zero, then time the first request. Roll out a new model version under load and count dropped requests. Hit the rate limits on purpose, so you know where they are. Autoscalers that react to request rate, concurrency, and latency headroom respond sooner than those that wait for CPU or GPU utilization to rise, as Simplismart’s write-up on autoscaling GenAI in under 60 seconds explains.
Days 11 to 12: Trace the Data Path
Ask where model weights, prompts, outputs, and logs are stored, in which region, and what telemetry leaves your account in a BYOC setup. Request the SOC 2 report and ISO 27001 certificate rather than relying on badges, and confirm HIPAA scope and Business Associate Agreement availability if you handle US health data. Indian teams should also check the DPDP Act and sector rules.
Recommended read: Why Data Residency Matters for AI Inference in India: RBI, SEBI, and DPDP Act Explained
Days 13 to 14: Price the Whole Bill
Model a full month rather than a list price: idle GPU hours, minimum commitments, egress, storage, support tiers, reserved versus on-demand rates, and engineering time. List prices already differ for identical models. gpt-oss-120B, for example, is $0.10 in and $0.50 out per million tokens on Baseten, and $0.15 in and $0.60 out on both Together AI and GroqCloud. Rerun the estimate at three times your current volume, because the cheapest option at prototype scale is often not the cheapest in production.
Mistakes Teams Make When Choosing.
- Choosing on price per token alone. At production volume, idle GPUs, minimums, and egress often outweigh the token rate.
- Comparing speed numbers measured differently. Per-user peak throughput, aggregate throughput, and TTFT are different metrics, and headline numbers are usually measured under the conditions that suit the vendor.
- Assuming “self-hosted” means the same thing everywhere. For one vendor it means a managed cluster in your VPC on an enterprise contract; for another, it means connecting any Kubernetes cluster, including on-prem. Ask exactly what runs where and who operates it.
- Ignoring the migration path. Most teams start on shared APIs and later need dedicated GPUs or BYOC. Changing vendors at that point costs months.
- Skipping your own benchmark. Use published lists to build a shortlist, then let tests on your own traffic make the decision.
Choosing the Right AI Inference Platform
Most teams narrow the field in three steps. Decide where your models must run: a shared API, dedicated GPUs, or your own cloud or data center. Match the platform to the metric your users feel, whether that’s first-response latency, throughput or cost per output. Then test two or three finalists on your own traffic before you sign anything.
If your workload needs to stay in your AWS, GCP, or Azure account, run on-prem, or be tuned for a specific latency or cost target, Simplismart is built for that combination. Talk to a Simplismart engineer about your workload, or start with the model APIs and move to dedicated or BYOC when you’re ready.
Frequently Asked Questions (FAQ)
What’s the best platform for AI inference?
The best AI inference platform depends on your latency target, budget, and where your data must run. Teams that need production inference in their own cloud or data center often choose Simplismart, which supports shared APIs, dedicated GPUs, BYOC, and air-gapped deployment from one control plane.
Which AI inference platform is most reliable?
Reliability depends on the SLA in your contract, multi-region failover, and how the platform handles traffic spikes and rollouts. Ask for the uptime SLA, status-page history, and a reference customer at your scale, then load-test it yourself.
What is the fastest AI inference platform?
No platform is fastest for every model; speed depends on how batching, scheduling, and kernels are tuned for your model and traffic. GroqCloud is fast on the models it hosts, while Simplismart cut Ema’s p50 routing latency from 10 seconds to 0.9 seconds on custom models.
Can I run an inference platform inside my own AWS, GCP, or Azure account?
Yes. This is called bring your own cloud (BYOC): GPUs and data stay in your account while the vendor manages deployments and scaling. Simplismart can set up a managed cluster in your account, typically in about 30 minutes, or connect to an existing Kubernetes cluster.
How do I reduce AI inference costs?
Reduce inference costs by right-sizing GPUs, quantizing where accuracy holds, batching requests, and scaling to zero when idle. Using these techniques on Simplismart, Sanas cut costs by 53% by moving from H100 to A10G with INT4 quantization.
Is self-hosting vLLM cheaper than a managed inference platform?
At steady, high volume with an experienced team, it can be. At low or bursty volume, idle GPUs and engineering time usually cost more than a managed platform’s margin. Model both at your real traffic before deciding.
Which AI inference platforms are most secure?
The most secure platforms combine audited compliance (SOC 2 Type II, ISO 27001, HIPAA) with deployment options that keep data in your own environment. Simplismart displays ISO 27001, SOC 2 Type 2, GDPR, and HIPAA badges and supports VPC, on-prem, and air-gapped deployment.
Can I deploy fine-tuned or custom models?
Yes. Most dedicated and BYOC inference platforms accept custom weights. Simplismart also handles fine-tuning with LoRA, QLoRA, SFT, RFT, GRPO, and DPO, and Tata 1mg used it to raise prescription-parsing accuracy from about 80% to 95%.
Can an inference platform handle speech, vision, and image workloads?
Yes. Many inference platforms serve more than LLMs, including speech, vision, and diffusion models. Simplismart runs all of these plus time-series models, and Physics Wallah uses it to transcribe 12-hour lectures in under 15 minutes.
Can I move from a closed API to production quickly?
Yes. With a managed inference platform, moving to open or custom models can take days rather than months. Lyric went live on Simplismart in 2 days.
