MODEL DIRECTORY

MODEL DIRECTORY

PaLM

PaLM

PaLM is Google’s superseded language-model family. Existing implementations should identify the old endpoint, select a current Gemini replacement, and run a full migration regression.

CURRENT MODEL SNAPSHOT

Provider: Google
Current anchor: Migrate to Gemini
Lifecycle: Migration
Weights: Closed
Reviewed: 20 July 2026

PaLM as a migration reference

PaLM is no longer the right model family for a new implementation. Google’s active generative-AI catalogue is Gemini, with different model IDs, capabilities, release stages, tool patterns, and operational limits. This page exists to help owners find an old PaLM dependency, select a current Gemini target, and migrate without treating a provider rename as a drop-in replacement.

A PaLM migration inventory should capture:

  • The exact PaLM model and endpoint, client library, region, quotas, and calling application.

  • Prompt templates, examples, output parsers, safety settings, retries, and monitoring tied to the old behaviour.

  • A stable Gemini candidate selected from Google’s current model catalogue.

  • Regression results, fallback, cutover owner, decommission date, and removal of obsolete credentials.

Do not extend a PaLM dependency for convenience. Freeze the current behaviour, choose a stable Gemini target, run representative regression tests, and plan a reversible cutover. Update content and architecture documents so future teams do not mistake this page for a current model recommendation.

Lifecycle note: PaLM is a superseded Google model family. This route is retained as a migration guide and should direct new implementations to the current Gemini catalogue.

Where PaLM is distinctive

The value of this route is operational continuity and search clarity. It gives teams landing on an old model name a current action: inventory, compare, migrate, and retire. It should not reproduce a historical feature comparison or imply that all Gemini tiers are equivalent successors.

Strengths to test

  • Finding and documenting old PaLM API, prompt, parser, and credential dependencies.

  • Selecting a stable Gemini target based on the actual workload rather than name similarity.

  • Running controlled regression, dual-run, and rollback before a model cutover.

  • Preserving route equity while preventing new teams from adopting a retired family.

Trade-offs and failure modes

  • Prompt and output behaviour can change even when Google provides migration guidance.

  • Gemini models have distinct release stages, modalities, tools, safety settings, and quotas.

  • Old credentials, libraries, endpoints, and monitoring can remain after a partial migration.

  • A redirect without migration content can strand existing users who need operational detail.

Build the migration suite from production inputs and downstream acceptance criteria. Compare factuality, schema validity, safety behaviour, latency, cost, and human corrections. Test rollback, update monitoring, and decommission the old path only after the owner accepts the evidence.

Deployment and enterprise decision notes

PaLM should not receive a new deployment. Use Google’s current Gemini API or supported cloud surface for the selected successor. Confirm stable model ID, region, data settings, tools, quotas, and deprecation schedule before cutover.

Best fit

Best suited as a migration and SEO reference for teams with a real PaLM dependency, old documentation, or bookmarks. Its purpose is to drive a controlled successor decision and remove obsolete endpoints safely.

Not the best fit

Not suitable for a new AI application, model comparison shortlist, or claim about current Google capability. Do not use this route to justify extending a superseded model solely to avoid regression work.

Data and governance

Assign a migration owner, inventory data and credentials, record the Gemini target and model stage, run security and privacy review, update the data-flow diagram, and set a decommission date. Preserve logs needed for validation without retaining unnecessary prompt data.

Official sources

Beam AI support status

Under evaluation. This page is a model-selection reference, not confirmation of a Beam integration, benchmark result, data-residency promise, or production recommendation. Validate the exact provider surface and model version in the intended workflow before release.

Use case 1

PaLM dependency inventory

Locate old model IDs, clients, prompts, parsers, credentials, dashboards, and downstream systems. Record owners and production impact so migration work covers the entire dependency rather than only replacing the API name in one service.

Use case 2

Gemini successor evaluation

Select stable Gemini candidates from current documentation and run the PaLM production regression set. Compare output schemas, factuality, safety, latency, cost, tools, quotas, and human corrections before choosing the target.

Use case 3

Dual-run migration

Send a controlled sample through PaLM and the selected Gemini model, compare downstream outcomes, and investigate material differences. Protect personal data, cap duplicate processing, define rollback, and obtain owner approval before cutover.

Use case 4

Legacy retirement

Remove obsolete endpoints, libraries, credentials, alerts, and documentation after the Gemini path is accepted. Preserve necessary audit evidence, update data flows and runbooks, monitor post-cutover drift, and close the migration with a named owner.

Related LLMs

Curated alternatives to compare before selecting a model family.

ChatGPT

ChatGPT is OpenAI’s conversational product; enterprise automations use the underlying GPT models and APIs. This guide separates the product from the model family and explains how to evaluate the current GPT-5.6 lineup.

ChatGPT

Combine sections from a range of categories to easily assemble pages that meet the needs of your growing business.

DeepSeek

DeepSeek’s current V4 family provides Pro and Flash API models with thinking and non-thinking modes, a documented one-million-token context window, and compatible API interfaces.

DeepSeek

Combine sections from a range of categories to easily assemble pages that meet the needs of your growing business.

Perplexity

Perplexity’s Sonar API family combines language models with web search and citations. The core enterprise decision is whether provider-managed retrieval and source handling fit the workflow.

Perplexity

Combine sections from a range of categories to easily assemble pages that meet the needs of your growing business.

Gemini

Google’s Gemini family covers production and preview models for multimodal, reasoning, audio, media, and tool-oriented workloads. Model stage and endpoint stability are central to an enterprise selection.

Gemini

Combine sections from a range of categories to easily assemble pages that meet the needs of your growing business.

Claude

Anthropic’s Claude family spans frontier reasoning, coding, vision, and faster production models. The practical choice is a specific pinned model ID on the Claude API or a supported cloud platform.

Claude

Combine sections from a range of categories to easily assemble pages that meet the needs of your growing business.

Llama

Meta’s Llama 4 family provides open-weight, natively multimodal models. Scout emphasizes very long context and efficient deployment; Maverick targets stronger general multimodal capability.

Llama

Combine sections from a range of categories to easily assemble pages that meet the needs of your growing business.

Start Today

Build AI agents with the right model

See how Beam can orchestrate governed AI workflows across the model family that fits your requirements.

Start Today

Build AI agents with the right model

See how Beam can orchestrate governed AI workflows across the model family that fits your requirements.

Start Today

Build AI agents with the right model

See how Beam can orchestrate governed AI workflows across the model family that fits your requirements.

FAQs

Frequently Asked Questions

Model selection, deployment, governance, and Beam support questions answered.

What is the current PaLM model lineup?

PaLM has no current lineup for new selection; teams should use Google’s active Gemini model catalogue and migration guidance. Model names and lifecycle labels can change quickly, so record the exact model ID or release used in testing and confirm it against the linked provider documentation before production.

How can an enterprise access or deploy PaLM?

Do not create a new PaLM deployment. Select a supported Gemini API or cloud endpoint and migrate the application through a controlled regression and cutover. Availability, regional controls, service terms, and feature parity can vary by route. Evaluate the exact provider surface that will carry production traffic, rather than assuming every hosted or self-managed option behaves identically.

What workloads are a strong fit for PaLM?

This page fits owners of an existing PaLM dependency who need an inventory, successor selection, regression, rollback, and retirement plan. Treat that as a shortlist hypothesis, not a universal ranking. Use representative prompts, tools, documents, languages, and failure cases to compare quality, latency, reliability, and total operating cost.

What should security and governance teams review for PaLM?

Review old credentials and data paths, Gemini release stage and region, prompt and parser changes, monitoring, rollback, and a dated decommission owner. Document the data path, retention settings, model version, region, subprocessors or hosting stack, human-review points, and incident fallback before the workflow is approved.

Does this page confirm Beam support for PaLM?

No. Beam support is marked Under evaluation because no approved integration or production-support evidence is attached to this CMS record. The page can guide discovery and evaluation, but the implementation owner must verify access, controls, tool behaviour, and operational fit before making a customer commitment.