Amazon Bedrock vs Together AI — the full 2026 comparison.

IWhat you are actually choosing between

Both are model-serving platforms reachable through one API, but they are built around different center-of-gravity assumptions. Together AI is optimized for the open-weight ecosystem and price-per-token; Bedrock is optimized for running many models — open and closed — under AWS’s security and governance umbrella.

Together AI is an inference-and-training cloud built primarily around open-weight models. It exposes a very large catalog — Meta Llama, Mistral and Mixtral, Qwen, DeepSeek, Google Gemma, and a long tail of community models — through an OpenAI-compatible API, with a focus on high throughput, low latency, and low per-token cost. Beyond serverless token billing, Together offers dedicated/reserved endpoints for steady high-volume workloads, fine-tuning (including full and parameter-efficient methods) on open models, and GPU access for custom training. You are buying breadth of open models plus speed and price, with a lot of control over the open-weight stack.

Amazon Bedrock is AWS’s fully managed service for accessing many foundation models through a single API, with a consistent multi-turn interface (the Converse API) across providers. The model menu spans Anthropic (Claude), Meta (Llama), Mistral, Amazon (Nova and Titan), Cohere, AI21, Stability AI, and DeepSeek — a mix of closed frontier models and open-weight models. Around the models, Bedrock provides managed Knowledge Bases (RAG), Agents, Guardrails, Flows, Prompt Management, evaluation, and fine-tuning — all running inside your AWS account, under AWS IAM, VPC, and compliance.

So the real choice is rarely “one Together model vs one Bedrock model.” It is “a specialist open-model cloud with the widest open-weight catalog and the lowest per-token price” versus “a multi-model platform inside your cloud with AWS-native security, compliance, and the closed frontier models (like Claude) alongside open ones.” Both can serve Llama; only Together leans all-in on the open long tail, and only Bedrock gives you Claude and AWS-native governance in the same place.

This page stays neutral. Both are strong in 2026. Model catalogs, prices, and features change fast in this category — treat specifics here as representative of 2026 and confirm on each vendor’s live pricing and model pages before standardizing.

IIModel availability: open-weight breadth vs curated multi-provider

The first real difference is the shape of the catalog. Together AI maximizes open-weight breadth; Bedrock curates a smaller set spanning open and closed providers, including frontier models you cannot get on an open-model cloud.

Together AI: the widest open catalog. Together’s pitch is access to a very large number of open-weight models — multiple Llama sizes, Mistral and Mixtral, Qwen, DeepSeek, Gemma, and a deep long tail of community and specialized models (chat, code, embeddings, vision, image, and more) — all under one OpenAI-compatible API. If your strategy is built on open weights (for cost, transparency, customization, or the ability to self-host later), Together gives you the broadest menu and lets you trial new open models almost as soon as they are released. The trade-off: it is overwhelmingly an open-model world — you will not find closed frontier models like Claude there.

Bedrock: curated, open + closed. Bedrock offers fewer total models, but the set is curated across providers and crucially includes closed frontier models (Anthropic’s Claude family, Amazon’s Nova) alongside open-weight ones (Llama, Mistral, and others). You can run Claude for nuanced reasoning and writing, Llama or Mistral for open-weight cost efficiency, and Amazon Nova for low-cost/low-latency volume — switching between them with minimal code change via the unified Converse API. The advantage is reach across the quality spectrum, including the frontier; the constraint is that the open long tail is narrower than a dedicated open-model cloud’s.

A candid way to frame it: if you want every notable open model and the freshest community releases, Together’s catalog is broader. If you want a curated set that also includes top closed models like Claude under one roof, Bedrock covers ground Together does not. Many teams discover the question is less “which has more models” and more “do I need a closed frontier model and AWS governance, or maximum open-weight breadth and price.”

IIIPricing shape and cost at chatbot scale (worked math)

Both bill primarily per token — per 1,000 (or per 1,000,000) input and output tokens, varying by model — so the structure is comparable and the real cost driver is which model you pick and how many tokens you push. Below is an illustrative worked example to show how to reason about it, not a price quote.

Together AI is widely positioned as a low-cost serverless option for open-weight models, with per-token rates for open models that are often very competitive, plus dedicated/reserved endpoints (you pay for capacity by the hour/minute) when steady throughput makes reserved cheaper than per-token. Bedrock also bills per token per model and adds cost levers of its own: Batch (~50% off on-demand), prompt caching, and Provisioned Throughput for reserved capacity. The honest comparison is per-model, not per-platform: for the same open-weight model and traffic, Together’s serverless rate is frequently lower, while Bedrock’s value shows up when you also need a closed frontier model, AWS-native governance, or its managed building blocks in the same bill.

IVFine-tuning, customization, and control over the model

If you intend to adapt models to your domain, the two platforms differ in philosophy. Together leans into deep open-model customization and portability; Bedrock offers managed customization with AWS-native governance and a path to closed-model adaptation.

Together AI: open-model fine-tuning and ownership. Because Together is built on open weights, it offers fine-tuning (full fine-tuning and parameter-efficient methods such as LoRA) on open models, plus GPU access for custom training. A key open-model advantage is portability: a fine-tune on an open-weight base produces weights/adapters you can reason about and, in principle, run elsewhere (including self-hosting later), reducing platform lock-in at the model layer. For teams whose moat is a customized open model, Together gives more direct control over the training stack and the resulting artifact.

Bedrock: managed customization inside AWS. Bedrock provides fine-tuning and customization for supported models, model distillation, and (depending on the model) continued-pre-training options, all run as managed jobs inside your AWS account with your data staying in your AWS boundary. Custom models are served via Provisioned Throughput. The governance story is the draw — customization data is handled under AWS IAM/KMS/VPC and your compliance program — and Bedrock also lets you customize across a curated provider set. The trade-off versus a pure open-model cloud is that customization is more managed/abstracted and the open-weight long tail you can fine-tune is narrower.

The practical read: if your strategy centers on deeply customizing open-weight models and keeping ownership/portability of the result, Together’s open-model fine-tuning is a strong fit. If you want customization performed under AWS governance, with the option to also use closed models and managed RAG/Agents around them, Bedrock fits. Both let you fine-tune; the difference is open-model depth and portability versus managed, governed customization inside AWS.

VData control, privacy, compliance, and residency

For production systems, where your data goes and which compliance regime you can satisfy often outweigh raw capability. This is where the AWS-native vs specialist-cloud difference starts to bite.

Where inference runs. With Bedrock, inference runs inside your AWS account and chosen region; prompts and outputs stay within your AWS boundary, encrypted with your KMS keys, and Bedrock does not use them to train the base models. With Together AI, inference runs on Together’s platform; Together states (on its business terms) that it does not train on your data, and it offers dedicated endpoints for isolation and enterprise plans for stricter handling. Both are defensible; the structural difference is that Bedrock keeps processing inside your cloud account, while Together is a separate processor you call out to.

Compliance. Because Bedrock lives inside AWS, it inherits AWS’s broad compliance program (SOC, ISO, HIPAA-eligibility, FedRAMP in applicable regions, and more), and your existing AWS audit artifacts and Business Associate arrangements can extend to your model usage. Together AI maintains its own enterprise compliance posture (for example SOC 2-type attestations and enterprise data-handling options) that has matured over time; verify the exact certification you need on its current documentation. For organizations whose compliance story is already written around AWS, Bedrock slots in with less net-new diligence.

Residency. Bedrock gives data-residency control by region — you choose which AWS region processes the request, which matters for GDPR, regional sovereignty, and regulated industries — and that region map is the same one your other AWS services use. Together offers its own deployment regions and, on enterprise/dedicated arrangements, more control over where workloads run; check current region and residency options against your requirement. If you need inference pinned to a specific jurisdiction and tied to the rest of your cloud footprint, Bedrock’s explicit per-region model gives finer, more familiar control.

VIEnterprise controls and AWS-native integration: IAM, VPC, audit

For larger organizations, governance and integration are frequently the deciding axis. The question is how cleanly the service fits the access-control, networking, audit, and billing model you already operate — and for AWS shops, Bedrock has a structural advantage.

Identity and access (IAM). Bedrock is governed by AWS IAM — the same policies, roles, conditions, and organization-wide guardrails you already use for the rest of your AWS estate. You scope who can invoke which models, attach permission boundaries, and centralize control via AWS Organizations and IAM Identity Center. With Together AI you manage access through Together’s own API keys, organizations, and roles — capable, but a separate control plane from your cloud IAM.

Private networking (VPC/PrivateLink). Bedrock can be reached over AWS PrivateLink so traffic never traverses the public internet, keeping model calls inside your VPC and private network — a common hard requirement in regulated environments. Together calls go to Together’s API endpoints (with private/dedicated networking options on enterprise arrangements); for a security team that mandates private connectivity to every dependency, Bedrock’s in-VPC reach is a meaningful advantage.

Audit, monitoring, and billing. Bedrock integrates with AWS CloudTrail (API-level audit logging), CloudWatch (metrics/logs), and your existing AWS cost tooling — so model usage shows up in the same audit, observability, and consolidated bill as the rest of your infrastructure. Together provides its own usage dashboards, logs, and billing. The difference is consolidation: Bedrock folds into one cloud’s governance and one invoice; Together is a strong but separate system to administer and pay alongside your cloud. For AWS-native teams, that consolidation is often the whole point.

VIITogether AI wins when / Bedrock wins when

A fair comparison has to say plainly where each is the better choice. Here it is, without hedging — match your situation to the list that fits.

The most common honest summary: if your priority is open-model breadth, the lowest per-token price, and deep open-weight customization, Together AI is hard to beat for that brief. If you are an AWS shop or have real security, compliance, residency, or closed-frontier-model requirements, Bedrock’s structural advantages typically win. And note the overlap: both can serve open models like Llama, so the decision usually turns on whether you need maximum open breadth and price (Together) or AWS-native governance plus closed frontier models (Bedrock) — not on whether you can run open weights at all.

VIIIMigrating from Together AI to Bedrock

Teams frequently start on Together AI for open-model breadth and price, then move (or add) inference to Bedrock when enterprise security, compliance, residency, AWS consolidation, or a closed frontier model become requirements. The move is well-trodden and usually modest in effort.

The high-level shape of a Together → Bedrock migration:

• 1. Pick the target model in Bedrock — Map your Together open model to its Bedrock equivalent (e.g., the same Llama or Mistral family on Bedrock), or take the chance to move up to a closed frontier model like Claude if the workload warrants it. Request model access in the AWS regions you need — no infrastructure to provision; Bedrock is serverless.
• 2. Swap the API client — Replace Together’s OpenAI-compatible client with Bedrock’s Converse API (or the AWS SDK). The request/response concepts map closely — messages, system prompt, tools/function calling, streaming — so most changes are at the client layer, not your business logic.
• 3. Port any fine-tunes — If you fine-tuned an open model on Together, plan how to reproduce it on Bedrock: re-run customization on the Bedrock-supported base where available, or keep serving the open-weight fine-tune elsewhere and route only part of traffic to Bedrock. Re-run your evaluation set either way.
• 4. Re-tune prompts per model — Different model families respond to slightly different prompting styles. Budget time to adjust prompts and re-run your evals; do not assume a Together-tuned prompt is optimal verbatim on a different Bedrock model.
• 5. Wire in AWS governance — Put model access under IAM, encrypt with KMS, route traffic over PrivateLink if required, and turn on CloudTrail/CloudWatch — the security, compliance, and audit payoff that usually motivated the move.
• 6. Evaluate, A/B, and cut over — Run both in parallel on real traffic, compare quality/latency/cost on your own eval set, and shift traffic when Bedrock meets your bar. Keeping a thin model-abstraction layer makes this and any future switch low-risk.