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The difference between pivoting to AI

and being built for it.

C3.ai pioneered enterprise AI applications around custom ML models. Then foundation models changed everything. Now C3 is in the middle of a $135M restructuring, declining revenue, and an architecture retrofit. Rebase is AI infrastructure built from the ground up for the LLM era.

The foundation you build on today determines what you can build tomorrow.

What C3.ai Built

C3.ai was built around custom ML model training, proprietary data ontologies, and pre-packaged AI applications. The entire value proposition assumed enterprises needed help training custom models on their data. Then GPT-3.5, GPT-4, Claude, and Gemini arrived — foundation models that made custom ML obsolete for 80% of enterprise use cases.

What Rebase Builds

Enterprise AI Infrastructure designed from scratch for the LLM era. Context Engine builds a live knowledge graph across all your systems. AI Gateway is model-agnostic — any LLM, any provider, BYOK. Agent Studio for no-code and pro-code agent building. Self-serve deployment in your cloud in weeks. Every component built for foundation models.

Head-to-Head Comparison

Dimension

C3.ai

Rebase

Architecture

Pre-LLM. Custom ML models, proprietary ontologies. Bolt-on 'Generative AI' and 'Agentic AI' features on a platform not designed for them.

LLM-native. AI Gateway, Agent Studio, Context Engine, Memory — every component built for foundation models.

Business Health

Revenue declining: $75M -> $53M QoQ. $135M restructuring. FY2026 guidance lowered below consensus. New CEO six months in.

Growing. Production customers. Seed-stage with clear path to scale.

Deployment

Heavy professional services. Services-dependent model acknowledged as revenue risk in SEC filings.

Self-serve. BYOC, on-prem, air-gapped. Deploy in weeks, not quarters. No mandatory services.

Model Approach

Pivoting from custom ML to LLM features. Retrofit on existing architecture.

Model-agnostic AI Gateway. Any LLM, any provider, BYOK. Native to the platform.

Customer Risk

Dependent on limited number of large customers. SEC-disclosed revenue concentration risk.

Platform model designed for broad adoption. No concentration dependency.

Integration

Custom integration projects per system. Long deployment timelines.

50+ native integrations. Connect enterprise systems in days, not months.

Data Sovereignty

Cloud-managed. Limited deployment flexibility.

BYOC, on-prem, air-gapped. Zero data retention. Your data never leaves your environment.

Target Market

Large enterprise and Federal with long sales cycles and high ACV.

Mid-market to large enterprise (200-10K employees). Accessible without 7-figure budgets.

Head-to-Head Comparison

Dimension

C3.ai

Rebase

Architecture

Pre-LLM. Custom ML models, proprietary ontologies. Bolt-on 'Generative AI' and 'Agentic AI' features on a platform not designed for them.

LLM-native. AI Gateway, Agent Studio, Context Engine, Memory — every component built for foundation models.

Business Health

Revenue declining: $75M -> $53M QoQ. $135M restructuring. FY2026 guidance lowered below consensus. New CEO six months in.

Growing. Production customers. Seed-stage with clear path to scale.

Deployment

Heavy professional services. Services-dependent model acknowledged as revenue risk in SEC filings.

Self-serve. BYOC, on-prem, air-gapped. Deploy in weeks, not quarters. No mandatory services.

Model Approach

Pivoting from custom ML to LLM features. Retrofit on existing architecture.

Model-agnostic AI Gateway. Any LLM, any provider, BYOK. Native to the platform.

Customer Risk

Dependent on limited number of large customers. SEC-disclosed revenue concentration risk.

Platform model designed for broad adoption. No concentration dependency.

Integration

Custom integration projects per system. Long deployment timelines.

50+ native integrations. Connect enterprise systems in days, not months.

Data Sovereignty

Cloud-managed. Limited deployment flexibility.

BYOC, on-prem, air-gapped. Zero data retention. Your data never leaves your environment.

Target Market

Large enterprise and Federal with long sales cycles and high ACV.

Mid-market to large enterprise (200-10K employees). Accessible without 7-figure budgets.

Why Enterprises Choose Rebase

Built for this era, not retrofitted from the last one.

C3.ai is retrofitting LLM features onto a custom ML architecture. Rebase was built from the ground up for foundation models, agents, and real-time context.

Deploy in weeks. No services engagement required.

C3.ai's model is services-dependent. Rebase is a self-serve platform. You deploy in your VPC or on-prem in weeks, not quarters.

Growing architecture vs. restructuring platform.

C3 is spending $135M to restructure and rebuild. Rebase is a modern, growing architecture with production customers and a clear path to scale.

Open standards. No proprietary lock-in.

C3 uses proprietary ontologies and custom models. Rebase uses open standards, model-agnostic gateways, and BYOK access to any LLM.

50+ integrations out of the box.

C3 requires custom integration projects for every system. Rebase connects to your enterprise stack in days with native integrations.

What C3.ai's restructuring tells you about the market.

C3.ai is spending $135M to restructure — pivoting from custom ML toward LLM-based features, replacing leadership, and rebuilding their go-to-market. Revenue is declining. Guidance has been lowered. The market cap has compressed to ~$1.45B. These aren't signs of a company that got the architecture right early.

This isn't a criticism — it's a recognition that the AI landscape fundamentally shifted. C3 built well for a different era. Foundation models created a new one. The question for enterprise buyers is whether to bet on a platform being rebuilt mid-flight, or one that was designed for where the industry is heading.

Every platform is constrained by its foundation. The architecture you choose now determines what you can build for the next five years.

When Each Makes Sense

C3.ai May Still Work If

Existing C3 deployment with working ML-based use cases
Large energy or manufacturing enterprise with an active C3 contract
US Federal with an existing C3 relationship
Custom ML models are still central to your AI strategy
Comfortable with services-dependent deployment model

Choose Rebase If

Building with LLMs, agents, and real-time cross-system intelligence
Need self-serve deployment in weeks, not months
Mid-market enterprise without a services overhead budget
Want open standards and no vendor lock-in
Data must stay in your environment — BYOC, on-prem, air-gapped
Making an architecture decision for the next five years

When Each Makes Sense

C3.ai May Still Work If

Existing C3 deployment with working ML-based use cases
Large energy or manufacturing enterprise with an active C3 contract
US Federal with an existing C3 relationship
Custom ML models are still central to your AI strategy
Comfortable with services-dependent deployment model

Choose Rebase If

Building with LLMs, agents, and real-time cross-system intelligence
Need self-serve deployment in weeks, not months
Mid-market enterprise without a services overhead budget
Want open standards and no vendor lock-in
Data must stay in your environment — BYOC, on-prem, air-gapped
Making an architecture decision for the next five years

FAQS

REBASE vs BUILDING IN-HOUSE (DIY)

We're evaluating C3.ai for enterprise AI. How does Rebase compare?

C3.ai was built for custom ML model training — a paradigm that foundation models have largely replaced. They're currently in a $135M restructuring with declining revenue as they pivot toward LLM-based features. Rebase was built for the LLM era from day one. If you need agents, cross-system intelligence, and model-agnostic infrastructure, you're comparing a platform built for this era with one being retrofitted for it.

Can Rebase handle the same enterprise use cases as C3.ai?

We already use C3.ai. How hard is it to migrate to Rebase?

How long does it take to deploy Rebase vs. C3.ai?

Is Rebase enterprise-ready? C3.ai has been around for over a decade.

Ready to build on AI-native infrastructure?

See why engineering teams choose Rebase over duct-taping open-source frameworks — and ship AI capabilities 10x faster.

Ready to build on AI-native infrastructure?

See why engineering teams choose Rebase over duct-taping open-source frameworks — and ship AI capabilities 10x faster.

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an AI-first company

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