vsAmazon Bedrock vs Thinking Machines
Bedrock wraps 100+ models in AWS security with managed fine-tuning. Thinking Machines gives researchers low-level control of post-training on open weights.
By The Subconscious Team · Updated
Amazon Bedrock vs Thinking Machines: key differences
Bedrock is a managed model service for enterprises already on AWS. One API reaches Claude, GPT-6 Astra, Nova, DeepSeek and 100+ other models, and every call inherits IAM, PrivateLink, KMS and CloudTrail. Customization comes through managed fine-tuning and Custom Model Import for open-weight checkpoints. Thinking Machines targets a different buyer. Tinker exposes forward_backward, optim_step, sample and save_state, so teams write their own SFT or RL loop instead of filling out a fine-tuning job, and the lab runs the distributed GPU work. Training is LoRA only, across bases like Kimi K2.6, GLM-5.3, Qwen3.5 and Inkling, billed per million prefill, sample and train tokens.
Bedrock wins on production serving and compliance. AgentCore adds a serverless agent runtime with memory, an MCP gateway and policy controls, and billing options include batch at 50% off and provisioned throughput. Third-party analyses put most Bedrock models 20 to 35% above direct prices, with Claude at parity. Thinking Machines cannot yet serve user traffic at scale: its serverless API is beta and Inkling-only, and checkpoint sampling is scoped to testing. Its edge is depth of control over training, including RL, on models that are hard to train in-house. One plausible path is training LoRA adapters on Tinker and bringing the result into a governed host.
What Amazon Bedrock and Thinking Machines do
Amazon Bedrock
Amazon Bedrock is AWS's managed model service and has become the default AI control plane for many enterprises. One API reaches 100+ models from 18+ providers, including Anthropic's Claude family, Meta, Mistral, DeepSeek, Amazon's own Nova models, and, since an April 2026 partnership expansion, OpenAI models up to GPT-6 Astra. Switching models is usually just a new model ID. Every call inherits IAM, PrivateLink, KMS encryption and CloudTrail logging, and provider models never train on customer data.
Example models: Claude Opus, GPT-6 Astra
Full Amazon Bedrock profileThinking Machines
Thinking Machines Lab is the San Francisco lab Mira Murati, formerly CTO of OpenAI, founded in February 2025, with OpenAI co-founder John Schulman as chief scientist. It raised about $2 billion at a $12 billion valuation in July 2025 in a round led by Andreessen Horowitz, and in March 2026 signed a multi-year Nvidia deal for one gigawatt of Vera Rubin capacity. Its main developer product is Tinker, an API for post-training open-weight models that launched in October 2025 and is now generally available. Tinker exposes four low-level calls, forward_backward, optim_step, sample and save_state, so teams write their own supervised or reinforcement learning loops while Thinking Machines runs the distributed GPU work. Training uses LoRA adapters rather than full weight updates.
Example models: Inkling, Inkling-Small, Qwen3.5, Kimi K2.6
Full Thinking Machines profileShould you choose Amazon Bedrock or Thinking Machines?
Amazon Bedrock
Choose Amazon Bedrock for
- Regulated teams that need IAM, KMS and CloudTrail on every call
- Multi-model agents on AgentCore
- Simple managed fine-tuning inside AWS
Thinking Machines
Choose Thinking Machines for
- Custom RL loops that managed fine-tuning cannot express
- Post-training Kimi K2.6 or Inkling without a cluster
- Research iteration with mid-training checkpoint sampling
Amazon Bedrock vs Thinking Machines at a glance
| Attribute | Amazon Bedrock | Thinking Machines |
|---|---|---|
| Model access | Closed and open, 100+ models | Open weights |
| Flagship models | Claude, GPT-6 Astra, Nova, DeepSeek | Inkling, Inkling-Small |
| Speed | Latency-optimized option on some models | Unknown |
| Price | ~20–35% above direct; Claude at parity | Per 1M tokens by prefill, sample, train; Inkling $1.00 in, $4.05 out |
| Customization | Fine-tuning, Custom Model Import | LoRA SFT and RL via Tinker |
| Deployment | Managed on AWS, AgentCore | Training API, beta serverless (Inkling only) |
| Long context | Varies by model | Inkling up to 1M; Tinker 32K–256K |
Frequently asked questions
What is the difference between Amazon Bedrock and Thinking Machines?
Bedrock wraps 100+ models in AWS security with managed fine-tuning. Thinking Machines gives researchers low-level control of post-training on open weights.
When should I choose Amazon Bedrock over Thinking Machines?
Regulated teams that need IAM, KMS and CloudTrail on every call; Multi-model agents on AgentCore; Simple managed fine-tuning inside AWS.
When should I choose Thinking Machines over Amazon Bedrock?
Custom RL loops that managed fine-tuning cannot express; Post-training Kimi K2.6 or Inkling without a cluster; Research iteration with mid-training checkpoint sampling.
Is Amazon Bedrock or Thinking Machines cheaper?
Amazon Bedrock: ~20–35% above direct; Claude at parity. Thinking Machines: Per 1M tokens by prefill, sample, train; Inkling $1.00 in, $4.05 out. The cheaper choice depends on the model and workload.
Which has more context, Amazon Bedrock or Thinking Machines?
Amazon Bedrock: Varies by model. Thinking Machines: Inkling up to 1M; Tinker 32K–256K.
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Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.