Amazon Bedrock Model Lifecycle: Plan and Migrate Models Without Service Disruption

Amazon Bedrock model lifecycle: how to plan and migrate without disruption

Updating foundation models in production can feel like changing a tire on a moving car. Amazon Bedrock’s Active → Legacy → End‑of‑Life (EOL) lifecycle gives you predictable windows to plan migrations—so you can move deliberately instead of panicking when a model version is sunset.

Executive summary: Bedrock exposes a modelLifecycle flag you can poll to detect state changes. Models remain available at least 12 months after launch; once a model enters Legacy you get a minimum six‑month runway before EOL (with special 3+3 rules for certain future EOLs). During Legacy some capacities and quota increases are restricted and pricing may change in extended access. Build a migration plan now: inventory usage, pick replacement models, test with shadow/A‑B, request quotas early, optimize prompts/tokens, and keep rollback options ready.

At‑a‑glance timeline for Amazon Bedrock model lifecycle

Active: Available immediately after launch; minimum guaranteed availability is 12 months.
Legacy: Minimum six months before EOL. For models with EOL after Feb 1, 2026, Legacy must include at least three months before a public extended access phase.
Extended access (public): For some models, at least ~3 additional months following the three‑month Legacy floor before EOL.
EOL: Model no longer available for new use; plan to stop production traffic before this date.

“Models on Bedrock are classified into three lifecycle states—Active, Legacy, and EOL—which you can see in the console or via a modelLifecycle flag returned by the Bedrock API.”

What this means for your team

Product: New launches can stall if Legacy blocks new customers—pin replacement models in roadmaps and avoid last‑minute dependencies.
SRE / Engineering: Treat lifecycle transitions like a maintenance window: plan quota requests, performance testing, and rollback paths in advance.
Procurement / Legal: Expect pricing shifts during extended access; private pricing and pre‑booked provisioned capacity usually keep existing terms—document those clauses.

Operational constraints and notifications

Bedrock displays model lifecycle state in the console and via API responses (poll the modelLifecycle flag periodically).
AWS sends lifecycle notifications at least six months before EOL via email (root/alternate contacts), the AWS Health Dashboard, console alerts, and programmatic APIs—configure recipients in the User Notifications console.
During Legacy you may be blocked from creating new accounts on that model; existing accounts that stop calling the model for ~15 days risk losing active access.
New provisioned capacity (pre‑booked throughput) is typically not available during Legacy and customization/fine‑tuning may be restricted.
Pricing can change during extended access; AWS notifies customers in advance. Private pricing and provisioned throughput customers generally retain their existing commercial terms.

Bedrock migration roadmap (practical sequence)

Assess (days 0–7): Inventory where each Bedrock model is used, traffic volumes, SLAs, compliance constraints, and long‑running or batch jobs. Mark “must not break” flows.
Research (weeks 1–2): Evaluate replacement models or newer versions from the same vendor. Validate regional availability and whether global cross‑Region inference is required.
Plan quotas & costs (weeks 2–4): Request quota increases early via AWS Service Quotas; model any cost delta (tokens/inference rates) and estimate peak vs typical spend.
Test (2–8 weeks): Run shadow, side‑by‑side and A/B tests. Measure latency, accuracy, hallucination rates and cost. Use canaries for phased rollout.
Migrate (phased): Start with low‑risk traffic splits (1–10%), scale to 25–50% with monitoring, then full cutover with an ability to rollback instantly to the previous model ID.
Monitor & iterate (ongoing): Track P95/P99 latency, error rates, cost per 1K tokens, and quality metrics. Keep alerts tied to your acceptance thresholds.

Testing strategies for Bedrock model migrations

Testing reduces risk. Aim for both statistical rigor and business context.

Shadow testing: Mirror production traffic to the candidate model for 1–2 weeks. Capture outputs and compare offline. No user impact.
Side‑by‑side / A‑B: Route a small slice of real requests to the new model (typical schedule: 1% → 5% → 10% → 25%). Keep each step at least 48–72 hours for stable metrics.
Regression & edge‑case testing: Sample N = 500–1,000 historical prompts, run against both models, and measure change in hallucination rate, incorrect outputs, or policy classification mismatches.
Performance testing: Validate throughput and tail latency. Define target SLAs (example targets: P95 latency ≤ baseline + 20ms; P99 within 2× baseline). Stress test to expected peak RPS.

Suggested quantitative acceptance criteria

Latency: P95 ≤ baseline + 20% and P99 ≤ baseline + 50%.
Quality: Hallucination rate delta ≤ +5 percentage points, or business‑defined tolerance.
Cost: Monthly token cost delta within budgeted variance (e.g., ≤ +15%); if higher, require executive sign‑off.
Errors: Increase in error rate ≤ 1.5× baseline during canary stages.

Technical checklist (developer & infra)

Update model IDs in config and CI/CD: versioned names may change (example pattern: Anthropic Claude Sonnet 3.5 → Sonnet 4.5).
Poll modelLifecycle flag programmatically and create automated tickets when state ≠ Active.
Request service quota increases well before migration windows; assume approvals are slower during Legacy/extended access.
Retune prompts and response parsing: tokenization and output format can differ between model versions—version prompts and keep a prompt history.
Optimize token usage: shorten instructions, use context windows smartly, and employ prompt caching where supported to reduce repeated token costs.
Use provisioned capacity for workloads needing predictable performance; be aware new provisioned capacity may not be creatable during Legacy.
Design rollback toggles: feature flags or DNS‑style routing to instantly revert traffic to the previous model ID.
Confirm regional availability and plan multi‑region failover if necessary; test global cross‑Region inference paths for latency impacts.

Mini case study: e‑commerce search migrating Sonnet 3.5 → 4.5

A large e‑commerce company ran product search and recommendation scoring on Claude Sonnet 3.5 hosted in Bedrock. When Sonnet 3.5 entered Legacy, the team followed a four‑week plan:

Week 1: Inventory & cost model—identified 120 services using the model; three high‑risk flows (search ranking, fraud detection enrichment, email generation) were flagged.
Week 2: Research & quota—validated Sonnet 4.5 regional availability and requested quota increases covering 2× peak throughput. Procurement confirmed private pricing covered existing provisioned capacity.
Weeks 3–4: Testing—ran a two‑week shadow test at full traffic volume, plus a canary A/B split that routed 5% of searches to Sonnet 4.5. Measured P95 latency, top‑K relevance delta, and hallucination change. All metrics passed acceptance criteria.
Cutover: Phased to 25% → 75% → 100% over 48 hours, with an immediate rollback toggle. Post‑migration monitoring showed cost per 1K tokens increased 8% but latency improved slightly; the product team accepted the tradeoff.

Outcome: migration completed with no user‑visible regressions, a documented rollback path, and an updated procurement contract reflecting the new pricing baseline.

Risk matrix: common failure modes and mitigations

Quota shortfalls (High): Mitigate by requesting increases early and validating fallbacks to lower‑cost modes.
Regional unavailability (High): Mitigate by testing global cross‑Region inference and multi‑region failover strategies.
Prompt regressions / hallucinations (Medium): Mitigate with regression testing, prompt versioning, and conservative canary percentages.
Unplanned pricing changes (Medium): Mitigate by modeling monthly spend for peak and sustained usage; secure private pricing where possible.

Practical automation: poll Bedrock lifecycle and alert

Simple workflow you can run daily to detect lifecycle changes and create tickets:

Call Bedrock DescribeModel or ListModels and read the modelLifecycle flag.
If modelLifecycle ≠ “Active”, create a ticket in your tracking system with the reported EOL date and notify Product, SRE, and Procurement.
If Legacy has been detected, schedule a migration kickoff meeting and begin quota and replacement model planning.

Sample stakeholder email (trim and send to product, SRE, procurement, legal):

Subject: Bedrock model lifecycle alert — [ModelName] now in Legacy (EOL: YYYY‑MM‑DD)

Team — Bedrock reports that [ModelName] is now in Legacy with an EOL date of YYYY‑MM‑DD. Action items: 1) Product: confirm replacement model candidate; 2) Engineering: request quotas and start shadow testing; 3) Procurement: review pricing & contract. Recommend a migration plan within 6 weeks. — [Your name]

Key takeaways & common questions

How long is a Bedrock model guaranteed to remain available after launch?

At least 12 months after launch.
How much notice do you get before EOL once a model enters Legacy?

At least six months’ notice before EOL. For models with EOL after Feb 1, 2026, Legacy must include at least three months before a public extended access period, which adds roughly another three months until EOL.
Can you create provisioned capacity or request new quotas during Legacy?

Generally, no. New provisioned capacity is typically blocked during Legacy and quota increases are unlikely—request capacity early.
Will pricing stay the same during extended access?

Pricing may change during extended access; AWS will notify customers in advance. Customers on private pricing or with provisioned throughput generally keep existing terms.
What’s the shortest practical migration checklist?

Inventory usage → pick replacement → request quotas → run shadow & A/B tests → phased rollout with rollback toggle → monitor P95/P99, cost, and hallucination metrics.

Model churn is a feature of the AI ecosystem, not a surprise. Amazon Bedrock gives clear signals and minimum timelines—use them to build predictable, testable migration processes. Request the one‑page migration checklist tailored for engineering and product teams, or ask for the executive slide deck that maps risk, timelines, and action items for your stakeholders.