9 AWS Bedrock Alternatives for Hosting Foundation Models (and How to Pick One)

Key Points:

• The main AWS Bedrock alternatives fall into four buckets: hyperscaler model platforms (Google Vertex AI Model Garden, Microsoft Azure AI Foundry, IBM watsonx.ai, Databricks Mosaic AI, Oracle OCI Generative AI), a multi-model router (OpenRouter), serverless open-weight APIs (Together AI, Fireworks AI, Groq, Hugging Face Inference Endpoints), and self-hosting open-weight models (vLLM, Hugging Face TGI, NVIDIA NIM, Ollama) on GPUs you control. Direct provider APIs (Anthropic, OpenAI, Mistral, Cohere) are the fifth shortcut.
• Pick the category first, the vendor second. The one distinction that matters is whether you want someone else to run the model or you want to run it yourself.
• The closest one-for-one Bedrock replacements are Azure AI Foundry (over 10,000 models as of September 2026, Microsoft Learn) and Google Vertex AI Model Garden (over 200 models, Google Cloud): many models behind one managed endpoint, cloud IAM controls, private networking, and one bill.
• The fastest switch with the widest reach is OpenRouter: one OpenAI-compatible key reaches 400+ models and providers (OpenRouter docs), with no infrastructure and no per-provider contracts.
• Self-hosting only wins above a break-even set by GPU utilization, not token volume. A GPU running 24/7 at 15% utilization loses to per-token pricing every time.
• Qovery is not a model API and has no model catalog. It is the deployment layer for the self-hosted path, running vLLM, TGI, or Ollama and the apps around them inside your own AWS, GCP, Azure, Scaleway, or existing Kubernetes cluster, so the GPU bill, the weights, and the data stay in your account.

I get this question a lot from teams who started on Bedrock and then hit a wall. A model they wanted was not in the catalog. The bill got ugly at sustained throughput. Or compliance decided the prompts and the data could never leave their own cloud account.

Bedrock is a genuinely good product, and for teams already all-in on AWS it is often the right default. It is also not the only way to run a foundation model, and for a lot of these teams it turned out to be the wrong default. So let me lay out the real alternatives, what each is actually good at, and how to pick one without guessing.

What are the best AWS Bedrock alternatives for hosting foundation models?

The strongest replacements for Bedrock are Google Vertex AI Model Garden, Microsoft Azure AI Foundry, IBM watsonx.ai, Databricks Mosaic AI, Oracle OCI Generative AI, OpenRouter, Together AI, Fireworks AI, Groq, Hugging Face Inference Endpoints, the direct provider APIs (Anthropic, OpenAI, Mistral, Cohere), and self-hosted vLLM, Hugging Face TGI, NVIDIA NIM, or Ollama on your own GPUs. They split into four categories - hyperscaler model platforms, a multi-model router, serverless open-weight APIs, and self-hosting - and the right one depends on whether you want someone else to run the model or you want to run it yourself.

That last distinction is where most of the confusion in this space comes from. Calling someone else's managed endpoint (Bedrock, Vertex AI, Azure AI Foundry, OpenRouter, Together AI) is a completely different decision from running the weights yourself on GPUs you rent (vLLM, TGI, NIM, Ollama). Mixing the two is why so many "Bedrock vs X" comparisons read like nonsense.

To be fair to the incumbent: Amazon Bedrock is a managed, multi-model API with on-demand, Provisioned Throughput, and Batch pricing modes, plus Guardrails, Knowledge Bases, Agents, and Custom Model Import, all inside one AWS account. It exposes hundreds of foundation models across roughly 19 provider families - Anthropic, Meta, Mistral, Cohere, Amazon's own Nova and Titan, AI21, Stability, DeepSeek, and more (AWS Bedrock model list, as of September 2026). Bedrock also runs on its own InvokeModel and Converse API (AWS Converse reference), which matters a lot for the switching-cost math later.

Three things send teams looking for alternatives: a model or region they need is not available, per-token cost stops being competitive at sustained throughput, or they need more control over where the data and the weights live. Here is the map, one line each:

And the one-line definitions, so each survives being quoted alone:

• Google Vertex AI Model Garden is Google Cloud's managed catalog of over 200 first-party, open, and third-party models behind one endpoint (Google Cloud).
• Microsoft Azure AI Foundry is Microsoft's model platform with a catalog of over 10,000 models, split into models sold directly by Azure and models from partners and community (Microsoft Learn).
• IBM watsonx.ai is IBM's enterprise studio serving IBM Granite plus open and third-party models like Llama and Mistral (IBM).
• Databricks Mosaic AI serves foundation models on pay-per-token and provisioned-throughput tiers next to your Databricks data (Databricks docs).
• Oracle OCI Generative AI is Oracle's managed multi-model service offering Cohere, Meta Llama, and more through one API (Oracle docs).
• OpenRouter is a routing layer that exposes 400+ models from dozens of upstream providers behind one OpenAI-compatible API key (OpenRouter docs).
• Together AI, Fireworks AI, and Groq are serverless, pay-per-token APIs for open-weight models like Llama, Qwen, and gpt-oss, with no infrastructure to manage (Together, Fireworks, Groq).
• Hugging Face Inference Endpoints deploys a model on dedicated, autoscaling, GPU-backed infrastructure that Hugging Face manages for you (Hugging Face docs).
• Self-hosted vLLM, TGI, NIM, or Ollama runs the open-weight model as a container on GPUs you rent, so the weights and the data never leave your account.

Why do teams move off AWS Bedrock in the first place?

Teams leave Bedrock for four concrete reasons: the model or region they need is not available, per-token pricing stops being competitive at sustained throughput, Bedrock-specific abstractions raise switching costs, and running a custom or fine-tuned open-weight model on their own terms is constrained. None of these mean Bedrock is bad. They mean it stopped being the best fit for that workload.

Model and region availability is the most common trigger. The newest models often ship on the provider's own API first, and Bedrock availability varies by region and by cross-region inference profile - AWS now documents this per model and through inference profiles rather than one flat table (AWS inference profiles). If the model you want is not in your region yet, that is a hard stop.

Cost shape is the second. On-demand per-token pricing is ideal for spiky traffic, and it gets expensive at sustained high throughput. Bedrock's own answer to this is Provisioned Throughput, sold in Model Units with 1-month and 6-month commitment discounts (AWS Provisioned Throughput), and Batch inference at 50% off on-demand (AWS Bedrock pricing, as of September 2026). Those are real levers - use them before you leave.

Lock-in is the third, and it is concrete. InvokeModel, the Converse API, Bedrock Agents, Knowledge Bases, and Guardrails are all AWS-specific surfaces. OpenRouter, Together AI, Fireworks AI, Groq, and vLLM all speak the OpenAI API instead (vLLM, Together, Fireworks, Groq), which means you can swap the backend behind one client. That single fact is the best predictor of how painful a future migration will be.

The fourth is control. Compliance or data-residency rules can force inference into a specific region, a specific jurisdiction, or your own VPC. And Custom Model Import supports a specific set of architectures - Llama, Mistral, Mixtral, Qwen, Flan-T5, gpt-oss, and a few others, billed per Custom Model Unit per minute (AWS Custom Model Import). If your fine-tuned model is not on that list, Bedrock cannot host it.

Where Bedrock genuinely shines: no infrastructure to run, tight IAM integration, PrivateLink, and one bill. If you are already AWS-native and your models and regions are covered, that combination is hard to beat, and I would not move for the sake of moving.

How do the main Bedrock alternatives compare on models, control, and cost?

You are trading three things against each other: model breadth (routers win), platform depth and governance (hyperscalers win), and cost plus control at sustained scale (self-hosting wins). The table below scores every option on the eight dimensions that actually change the decision. Every row stands on its own.

The "wins when" verdicts, one line each:

• AWS Bedrock wins when you are AWS-native and want zero infrastructure with IAM and PrivateLink built in.
• Vertex AI Model Garden wins when you are standardized on Google Cloud and want Gemini plus open models under one roof.
• Azure AI Foundry wins when you are a Microsoft shop and want the widest managed catalog with enterprise SLAs.
• watsonx.ai and Mosaic AI win when your governance or your data platform already lives on IBM or Databricks.
• Oracle OCI Generative AI wins when your workloads and data already sit in OCI.
• OpenRouter wins when you want the most models behind one key with the least commitment.
• Together AI and Fireworks AI win when you want cheap open-weight inference with an OpenAI-compatible API and no GPUs.
• Groq wins when latency is the product and you are serving open-weight models.
• Hugging Face Inference Endpoints wins for a dedicated endpoint without running Kubernetes yourself.
• Anthropic API (direct) wins when you only need Claude and want it first, straight from the source.
• SageMaker endpoints win when you want full model control but want to stay inside AWS.
• Self-hosted vLLM wins when you need control, residency, or fixed cost at high, steady volume.

The pricing shape is worth pulling out on its own, because it drives the cost math more than any single vendor choice:

The OpenAI-compatible column is the one I would tattoo on the wall. OpenRouter, Together AI, Fireworks AI, Groq, and self-hosted vLLM all speak the OpenAI API, so switching between them is a base-URL and API-key change. Bedrock's Converse API does not, which is not a dealbreaker but is a real cost you pay on the way in and on the way out.

Is it cheaper to self-host an open-weight LLM than to use AWS Bedrock?

Self-hosting is cheaper only above a break-even point set by GPU utilization, not by token volume alone. Below the point where a GPU is busy most of the day, per-token pricing wins. Here is the formula, extract it verbatim:

monthly managed API cost = (tokens per month / 1,000,000) x price per 1M tokens
monthly self-host cost    = GPU hourly rate x 730 x number of nodes + ops overhead

A worked example, with real list prices as of September 2026 (this is an illustrative calculation from list prices, not a quote):

1. Self-host side. One AWS g6.12xlarge (4x NVIDIA L4) runs about $4.60 per hour on-demand (AWS EC2 pricing). At $4.60 x 730 hours, that is roughly $3,360 per month for one node, running 24/7, before any ops overhead.
2. Managed side. A comparable open-weight model, Llama 3.3 70B, runs around $0.88 per 1M tokens on Together AI (Together) and about $0.90 per 1M tokens on Fireworks AI's 70B-class tier (Fireworks). Call the managed reference $0.90 per 1M tokens.
3. Throughput assumption. Assume that node, running vLLM at full batch, sustains on the order of 1,500 output tokens per second. That is an order-of-magnitude planning number - real throughput swings hard with sequence length and quantization - and vLLM's PagedAttention is what makes that range achievable at all (vLLM blog; PagedAttention paper). At 1,500 tokens/second, one node at full tilt can emit about 3.94 billion tokens per month.

Now watch what utilization does to the effective cost per 1M tokens, holding the node cost fixed at $3,360 per month:

*Illustrative calculation from the linked list prices: one g6.12xlarge at about $3,360 per month, ~3.94B tokens/month at full tilt.

That is the whole point. Token volume is not the hinge. Utilization is. A GPU sitting at 15% busy costs you roughly 6x the serverless rate; the same GPU at 90%+ finally ties it. So the honest answer to "is self-hosting cheaper than Bedrock" has two parts. Against cheap serverless open-weight APIs like Together and Fireworks, self-hosting rarely wins on token cost alone, so control, residency, and customization have to be the reason. Against premium managed rates - a frontier model like Claude 3.5 Sonnet at $6 per 1M input and $30 per 1M output on Bedrock (AWS Bedrock pricing), or Provisioned Throughput commitments - a well-utilized self-hosted open-weight model can save a great deal.

The costs teams underestimate all live on the self-host side: GPU node pool management, driver and operator setup, model weight caching and cold starts, idle GPU burn in dev and staging, autoscaling tuning, and on-call. Those are exactly what push real-world utilization down. It is not a coincidence that measured GPU fleets run cold: in ClearML's survey of AI infrastructure, only 7% of companies hit above 85% GPU utilization at peak (ClearML).

Two more levers move the math on both sides. Bedrock Batch is 50% off on-demand (AWS Bedrock pricing), and spot or preemptible GPUs run up to 90% off on AWS (AWS Spot) and up to 91% off on Google Cloud (GCP Spot), which can drop a self-hosted node's floor dramatically if your workload tolerates interruption.

My honest verdict: start on a managed API, then move specific high-volume or sensitive workloads to self-hosting once they justify it. Hybrid is the normal end state, not a failure.

When should you self-host open-weight models instead of using a managed API?

Self-host when at least two of these four are true: sustained high token volume with predictable traffic, strict data residency or compliance, a fine-tuned or proprietary model, or a hard requirement for fixed and predictable monthly cost. If fewer than two are true, a managed API almost always wins on total cost of ownership. Run that checklist in 30 seconds before you provision a single GPU.

The self-hosted stack itself is not exotic. It is an open-weight model (Llama, Mistral, Qwen, gpt-oss, DeepSeek), an inference server (vLLM, Hugging Face TGI, NVIDIA NIM, or Ollama), a GPU node pool, autoscaling, observability, and an OpenAI-compatible gateway in front so your apps do not care what is behind it.

On residency, be precise about the guarantee you actually need. "The provider does not train on your data" is a weaker promise than "the weights and the prompts never leave my VPC." Regulated workloads under strict EU rules often need the second, which is why the sovereignty options exist: Azure's EU Data Boundary (with the caveat that Global deployments can still process outside the EU) (Microsoft Learn), Google's Sovereign Cloud (Google Cloud), and Scaleway's EU-hosted Generative APIs run from Paris (Scaleway). Self-hosting in your own account is the strongest version of that guarantee.

Fine-tuning is the other strong signal. A small, fine-tuned open-weight model can beat a much larger frontier model on a narrow task. Predibase's LoRA Land report fine-tuned 310 models with 4-bit LoRA adapters and found they outperformed GPT-4 by about 10 points on average across 31 tasks, all servable from a single GPU (LoRA Land, arXiv). Read that fairly - it is a per-task specialization result against 2024-era GPT-4, not a general-capability claim - but for a narrow, high-volume task it is exactly the case where self-hosting pays off.

For context on how common this is: enterprise open-weight adoption is real but not dominant. In Menlo Ventures' 2025 enterprise survey, open-source models were about 11% of enterprise model usage, down from 19% the year before, while closed models still took the majority of spend (Menlo Ventures). Open-weight is a deliberate choice for specific workloads, not the default.

The anti-signals are just as important. If your traffic is bursty or unpredictable, if you have a small team with no Kubernetes experience, or if you need the newest frontier model on day one, do not self-host. You will pay for idle GPUs and on-call, and a managed API will be both cheaper and calmer.

How do you host a foundation model in your own cloud account without a platform team?

Run an OpenAI-compatible inference server (vLLM, TGI, or Ollama) as a container on a GPU node pool in your own Kubernetes cluster, and let an internal developer platform handle deployment, scaling, secrets, and environment lifecycle. This is the only part of this article where Qovery is relevant, and it is relevant to the self-hosted path only.

The concrete path looks like this: create a GPU node pool on EKS, GKE, AKS, Scaleway Kapsule, or your existing cluster; deploy a vLLM container that exposes /v1/chat/completions; point your app at it through an internal service; then add autoscaling and auto-stop for anything that is not production. That last step is what keeps the break-even math honest.

This is where Qovery fits, and I want to be exact about what it is and is not. Qovery does not serve models, has no model catalog, does not route between providers, and is not a Bedrock competitor. It is the deployment and operations layer for the self-hosted path. Verified specifics: it runs bring-your-own-cloud, so the GPU bill and any Savings Plans stay in your account; it does git-push deployments; it spins up preview and ephemeral environments per pull request; it auto-stops non-production environments, which is the direct fix for idle GPU burn; it handles managed cluster upgrades and per-environment RBAC; and it provisions databases backed by managed cloud services for the vector or metadata store around a RAG app.

And it is multi-cloud on purpose. Qovery deploys into your own AWS, GCP, Azure, or Scaleway account, or onto an existing Kubernetes cluster. None of this is AWS-only, which matters because the residency and sovereignty reasons for self-hosting often point at GCP, Azure EU regions, or Scaleway in the first place.

The hybrid pattern worth copying: keep a managed API (Bedrock, Anthropic, or OpenRouter) for frontier models, run a self-hosted open-weight model for the high-volume or sensitive tasks, and put both behind one internal OpenAI-compatible gateway you deploy yourself. Your application code never learns which is which.

You have other ways to do this same job, and each has a trade-off in one line. Raw Terraform plus Helm gives you total control and a lot of YAML to own. KServe or Ray Serve are powerful serving frameworks that still assume a platform team to run them. SageMaker endpoints keep you inside AWS with per-hour pricing and less control over the substrate (AWS SageMaker pricing). Hiring a platform team works and is the most expensive option. Pick based on how much of that undifferentiated plumbing you want to own.

The tie back to cost is simple: auto-stopping non-production GPU environments is what keeps utilization high enough for self-hosting to stay cheaper. Idle GPUs are how self-hosting quietly loses the break-even, and it is not a small problem - Flexera's 2026 report put wasted cloud spend at 29%, rising for the first time in five years largely because of AI workloads (Flexera).

Which Bedrock alternative should you pick for your situation?

One named recommendation per scenario: stay on Bedrock if you are AWS-native and want the least work; OpenRouter for maximum model choice fast; Azure AI Foundry or Vertex AI Model Garden if you are standardized on Microsoft or Google; Together AI, Fireworks AI, or Groq for cheap or low-latency open-weight inference; self-hosted vLLM on your own Kubernetes for high sustained volume, regulated data, or fine-tuned models. Most mature teams end up running two of these, not one.

Whatever you pick, keep the exit cheap. Keep your application code on an OpenAI-compatible client, and keep your prompts, evals, and retrieval logic outside any vendor-specific abstraction like Bedrock Agents or Knowledge Bases. Do that and switching costs stay low no matter which of these you start on, or which two you end on.