Amazon Bedrock usually releases new basis mannequin (FM) variations with higher capabilities, accuracy, and security. Understanding the mannequin lifecycle is important for efficient planning and administration of AI functions constructed on Amazon Bedrock. Earlier than migrating your functions, you possibly can take a look at these fashions by the Amazon Bedrock console or API to guage their efficiency and compatibility.
This put up reveals you the best way to handle FM transitions in Amazon Bedrock, so you can also make positive your AI functions stay operational as fashions evolve. We focus on the three lifecycle states, the best way to plan migrations with the brand new prolonged entry function, and sensible methods to transition your functions to newer fashions with out disruption.
Amazon Bedrock mannequin lifecycle overview
A mannequin supplied on Amazon Bedrock can exist in one in every of three states: Energetic, Legacy, or Finish-of-Life (EOL). Their present standing is seen each on the Amazon Bedrock console and in API responses. For instance, while you make a GetFoundationModel or ListFoundationModels name, the state of the mannequin shall be proven within the modelLifecycle area within the response.
The next diagram illustrates the main points round every mannequin state.
The state particulars are as follows:
- ACTIVE – Energetic fashions obtain ongoing upkeep, updates, and bug fixes from their suppliers. Whereas a mannequin is Energetic, you should utilize it for inference by APIs like InvokeModel or Converse, customise it (if supported), and request quota will increase by AWS Service Quotas.
- LEGACY – When a mannequin supplier transitions a mannequin to Legacy state, Amazon Bedrock will notify prospects with a minimum of 6 months’ advance discover earlier than the EOL date, offering important time to plan and execute a migration to newer or various mannequin variations. Throughout the Legacy interval, present prospects can proceed utilizing the mannequin, although new prospects may be unable to entry it, and present prospects may lose entry for inactive accounts if they don’t name the mannequin for a interval of 15 days or extra. Organizations ought to word that creating new provisioned throughput by mannequin models turns into unavailable, and mannequin customization capabilities may face restrictions. For fashions with EOL dates after February 1, 2026, Amazon Bedrock introduces an extra part inside the Legacy state:
- Public prolonged entry interval – After spending a minimal of three months in Legacy standing, the mannequin enters this prolonged entry part. Energetic customers can proceed utilizing it for a minimum of one other 3 months till EOL. Throughout prolonged entry, quota enhance requests by AWS Service Quotas usually are not anticipated to be accredited, so plan your capability wants earlier than a mannequin enters this part. Throughout this era, pricing could also be adjusted (see Pricing throughout prolonged entry under), and prospects will obtain notifications in regards to the transition date and any adjustments.
- END-OF-LIFE (EOL) – When a mannequin reaches its EOL date, it turns into utterly inaccessible throughout all AWS Areas until particularly famous within the EOL listing. API requests to EOL fashions will fail, rendering them unavailable to most prospects until particular preparations exist between the shopper and supplier for continued entry. The transition to EOL requires proactive buyer motion—migration doesn’t occur routinely. Organizations should replace their software code to make use of various fashions earlier than the EOL date arrives. When EOL is reached, the mannequin turns into utterly inaccessible for many prospects.
After a mannequin launches on Amazon Bedrock, it stays obtainable for a minimum of 12 months after launch and stays in Legacy state for a minimum of 6 months earlier than EOL. This timeline helps prospects plan migrations with out dashing.
Pricing throughout prolonged entry
Throughout the prolonged entry interval, pricing could also be adjusted by the mannequin supplier. If pricing adjustments are deliberate, you may be notified within the preliminary legacy announcement and earlier than any subsequent adjustments take impact, so there shall be no shock retroactive worth will increase. Prospects with present personal pricing agreements with mannequin suppliers or these utilizing provisioned throughput will proceed to function underneath their present pricing phrases through the prolonged entry interval. This makes positive prospects who’ve made particular preparations with mannequin suppliers or invested in provisioned capability won’t be unexpectedly affected by any pricing adjustments.
Communication Course of for Mannequin State Modifications
Prospects will obtain a notification 6 months previous to a mannequin’s EOL date when the mannequin supplier transitions a mannequin to Legacy state. This proactive communication method ensures that prospects have enough time to plan and execute their migration methods earlier than a mannequin turns into EOL.
Notifications embody particulars in regards to the mannequin being deprecated, essential dates, prolonged entry availability, and when the mannequin shall be EOL. AWS makes use of a number of channels to make sure these essential communications attain the appropriate individuals, together with:
- Electronic mail notifications
- AWS Well being Dashboard
- Alerts within the Amazon Bedrock console
- Programmatic entry by the API.
To be sure you obtain these notifications, confirm and configure your account contact e mail addresses. By default, notifications are despatched to your account’s root person e mail and alternate contacts (operations, safety, and billing). You possibly can evaluation and replace these contacts in your AWS Account web page within the Alternate contacts part. So as to add extra recipients or supply channels (equivalent to Slack or e mail distribution lists), go to the AWS Consumer Notifications console and select AWS managed notifications subscriptions to handle your supply channels and account contacts. In case you are not receiving anticipated notifications, test that your e mail addresses are accurately configured in these settings and that notification emails from well being@aws.com usually are not being filtered by your e mail supplier.
Migration methods and finest practices
When migrating to a more recent mannequin, replace your software code and test that your service quotas can deal with anticipated quantity. Planning forward helps you transition easily with minimal disruption.
Planning your migration timeline
Begin planning as quickly as a mannequin enters Legacy state:
- Evaluation part – Consider your present utilization of the legacy mannequin, together with which functions depend upon it, typical request patterns, and particular behaviors or outputs that your functions depend on.
- Analysis part – Examine the really useful substitute mannequin, understanding its capabilities, variations from the legacy mannequin, new options that might improve your functions, and the brand new mannequin’s Regional availability. Overview API adjustments and documentation.
- Testing part – Conduct thorough testing with the brand new mannequin and evaluate efficiency metrics between fashions. This helps establish changes wanted in your software code or immediate engineering.
- Migration part – Implement adjustments utilizing a phased deployment method. Monitor system efficiency throughout transition and keep rollback functionality.
- Operational part – After migration, constantly monitor your functions and person suggestions to ensure they’re performing as anticipated with the brand new mannequin.
Technical migration steps
Check your migration completely:
- Replace API references – Modify your software code to reference the brand new mannequin ID. For instance, altering from anthropic.claude-3-5-sonnet-20240620-v1:0 to anthropic.claude-sonnet-4-5-20250929-v1:0 or international cross-Area inference international.anthropic.claude-sonnet-4-5-20250929-v1:0. Replace immediate buildings in accordance with new mannequin’s finest practices. For extra detailed steerage, seek advice from Migrate from Anthropic’s Claude Sonnet 3.x to Claude Sonnet 4.x on Amazon Bedrock.
- Request quota will increase – Earlier than absolutely migrating, be sure you have enough quotas for the brand new mannequin by requesting will increase by the AWS Service Quotas console if mandatory.
- Alter prompts – Newer fashions may reply in another way to the identical prompts. Overview and refine your prompts accordingly to the brand new mannequin specs. You can too use instruments such because the immediate optimizer in Amazon Bedrock to help with rewriting your immediate for the goal mannequin.
- Replace response dealing with – If the brand new mannequin returns responses in a special format or with completely different traits, replace your parsing and processing logic accordingly.
- Optimize token utilization – Reap the benefits of effectivity enhancements in newer fashions by reviewing and optimizing your token utilization patterns. For instance, fashions that assist immediate caching can scale back the price and latency of your invocations.
Testing methods
Thorough testing is vital for a profitable migration:
- Aspect-by-side comparability – Run the identical requests towards each the legacy and new fashions to match outputs and establish any variations that may have an effect on your software. For manufacturing environments, contemplate shadow testing—sending duplicate requests to the brand new mannequin alongside your present mannequin with out affecting end-users. With this method, you possibly can consider mannequin efficiency, latency and errors charges, and different operational elements earlier than full migration. Carry out A/B testing for person impression evaluation by routing a managed share of dwell visitors to the brand new mannequin whereas monitoring key metrics equivalent to person engagement, job completion charges, satisfaction scores, and enterprise KPIs.
- Efficiency testing – Measure response instances, token utilization, and different efficiency metrics to grasp how the brand new mannequin performs in comparison with the legacy model. Validate business-specific success metrics.
- Regression and edge case testing – Make sure that present performance continues to work as anticipated with the brand new mannequin. Pay particular consideration to uncommon or complicated inputs that may reveal variations in how the fashions deal with difficult eventualities.
Conclusion
The mannequin lifecycle coverage in Amazon Bedrock provides you clear levels for managing FM evolution. Transition intervals supply prolonged entry choices, and provisions for fine-tuned fashions make it easier to stability innovation with stability.
Keep knowledgeable about mannequin states by the AWS Well being Dashboard, plan migrations when fashions enter the Legacy state, and take a look at newer variations completely. These tips will help you keep continuity in your AI functions whereas utilizing improved capabilities in newer fashions.
When you have additional questions or issues, attain out to your AWS crew. We wish to make it easier to and facilitate a easy transition as you proceed to benefit from the most recent developments in FM know-how.
For continued studying and implementation assist, discover the official AWS Bedrock documentation for complete guides and API references. Moreover, go to the AWS Machine Studying Weblog and AWS Structure Heart for real-world case research, migration finest practices, and reference architectures that may assist optimize your mannequin lifecycle administration technique.
In regards to the authors
Saurabh Trikande is a Senior Product Supervisor for Amazon Bedrock and Amazon SageMaker Inference. He’s obsessed with working with prospects and companions, motivated by the purpose of democratizing AI. He focuses on core challenges associated to deploying complicated AI functions, inference with multi-tenant fashions, value optimizations, and making the deployment of generative AI fashions extra accessible. In his spare time, Saurabh enjoys climbing, studying about modern applied sciences, following TechCrunch, and spending time along with his household.
Melanie Li, PhD, is a Senior Generative AI Specialist Options Architect at AWS based mostly in Sydney, Australia, the place her focus is on working with prospects to construct options utilizing state-of-the-art AI/ML instruments. She has been actively concerned in a number of generative AI initiatives throughout APJ, harnessing the ability of LLMs. Previous to becoming a member of AWS, Dr. Li held information science roles within the monetary and retail industries.
Derrick Choo is a Senior Options Architect at AWS who accelerates enterprise digital transformation by cloud adoption, AI/ML, and generative AI options. He makes a speciality of full-stack improvement and ML, designing end-to-end options spanning frontend interfaces, IoT functions, information integrations, and ML fashions, with a specific give attention to pc imaginative and prescient and multi-modal programs.
Jared Dean is a Principal AI/ML Options Architect at AWS. Jared works with prospects throughout industries to develop machine studying functions that enhance effectivity. He’s desirous about all issues AI, know-how, and BBQ.
Julia Bodia is Principal Product Supervisor for Amazon Bedrock.
Pooja Rao is a Senior Program Supervisor at AWS, main quota and capability administration and supporting enterprise improvement for the Bedrock Go-To-Market crew. Exterior of labor, she enjoys studying, touring, and spending time along with her household.
