Deploy any AI model. Switch anytime. Own everything. No lock-in — ever.
Most enterprise AI platforms quietly lock you in. They tie your workflows, your data, and your operations to a single model provider — and when that provider raises prices, changes terms, or falls behind, you're stuck.
ibl.ai is built differently. Our platform is fully model-agnostic, meaning you can run Claude, GPT-4, Gemini, Llama, Mistral, or any custom fine-tuned model — and swap between them at any time without changing your platform, your integrations, or your workflows.
With 1.6M+ users across 400+ organizations and partnerships with Google, Microsoft, and AWS, ibl.ai gives enterprises the freedom to choose the best model for every task — today and as the AI landscape evolves.
Enterprise AI adoption is accelerating, but most organizations are unknowingly building on a foundation of dependency. When you deploy AI through a single-model vendor platform, your entire operation becomes hostage to that vendor's pricing decisions, API availability, model deprecations, and compliance posture. A single policy change can break production systems overnight.
The deeper problem is architectural. Vendor-locked platforms are designed to make switching painful — your prompts, agent logic, data pipelines, and user interfaces are all tightly coupled to one model's behavior. When a better model emerges, or when your compliance team demands a different deployment model, you face a costly, risky rebuild from scratch. Organizations that don't solve this problem early pay for it at scale.
When your AI platform is built around a single model provider, you have no negotiating leverage. API costs can double overnight and your only alternative is a full platform migration.
Operational budgets spiral unpredictably, and finance teams lose confidence in AI ROI projections.AI providers regularly deprecate models with short notice windows. Organizations running GPT-3.5, PaLM 2, or early Claude versions have already experienced forced emergency migrations.
Engineering teams scramble to re-test, re-prompt, and re-validate entire workflows under pressure, causing outages and eroding trust.Government, defense, healthcare, and financial organizations often require data to stay within their own infrastructure. Most model vendors only offer cloud-hosted APIs with no air-gapped option.
High-value use cases are blocked entirely, or organizations accept unacceptable compliance risk to move forward.General-purpose models underperform on domain-specific tasks. Organizations that invest in fine-tuning custom models on proprietary data can't deploy them on locked vendor platforms.
Competitive advantages from proprietary data and domain expertise go unrealized, and AI performance remains generic.Different tasks demand different models — a fast, cheap model for classification, a powerful model for reasoning, a local model for sensitive data. Single-vendor platforms can't orchestrate across model boundaries.
Organizations over-spend on premium models for simple tasks, or under-power complex tasks with cheaper models, with no middle ground.Connect any model endpoint to the ibl.ai platform — OpenAI, Anthropic, Google Vertex, AWS Bedrock, Azure OpenAI, Hugging Face, Ollama, or a custom self-hosted inference server. Each model is registered with its credentials, parameters, and routing rules.
Each autonomous AI agent, workflow, or use case is independently configured to use a specific model — or a fallback chain. A legal review agent can run on Claude while a data extraction agent runs on a local Llama instance, all within the same platform.
The entire platform runs on your own infrastructure — cloud, on-premise, or air-gapped. Local models run on your hardware via Ollama or compatible inference runtimes. No data leaves your environment unless you explicitly configure it to.
When you want to switch models — for cost, performance, compliance, or capability reasons — you update the model configuration. Your agents, workflows, APIs, and integrations continue operating without code changes or redeployment.
The platform's complete audit trail logs every model call, response, latency, and cost metric. Compare model performance across identical tasks to make data-driven decisions about which model to use where.
As new models are released — GPT-5, Gemini Ultra 2, next-generation open-source models — you add them to your registry and test them against existing workflows. Adopt the best new capabilities without platform migrations or vendor negotiations.
Native integrations with OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Hugging Face, and Ollama. Any model with an API or local inference runtime can be connected and managed from a single control plane.
Define intelligent routing rules that direct requests to the optimal model based on task type, cost thresholds, latency requirements, or data sensitivity. Configure automatic fallback chains so operations continue if a model endpoint is unavailable.
Run open-source models like Llama, Mistral, and Mixtral entirely on your own hardware using Ollama or compatible runtimes. Zero external API calls, zero data egress — full performance on classified or sensitive workloads.
Deploy models you've fine-tuned on your proprietary data alongside commercial models. The platform treats custom models as first-class citizens — they can be assigned to agents, monitored, and swapped like any other model.
Every autonomous agent in the platform has its own model assignment. Orchestrate complex workflows where different agents use different models — optimizing for cost, speed, and capability at the task level, not the platform level.
Every model invocation is logged with full context: input, output, model version, latency, token usage, and cost. Audit logs are immutable, exportable, and available for compliance review, performance analysis, and cost attribution.
Customers receive the complete platform source code. The model integration layer is fully transparent and modifiable. If a new model provider emerges, your team can build the connector yourself without waiting for a vendor roadmap.
| Aspect | Without | With ibl.ai |
|---|---|---|
| Model Flexibility | Locked to one provider's model family. Switching requires rebuilding integrations, re-engineering prompts, and re-validating every workflow from scratch. | Any model — commercial or open-source — connects to the same platform. Swap models in configuration, not in code. Workflows continue without changes. |
| Pricing Power | Zero negotiating leverage. When the vendor raises API prices, you pay or you rebuild. Budget forecasting is impossible when costs are controlled by a third party. | Run competing models in parallel. Route cost-sensitive tasks to cheaper or local models. Use commercial APIs only where they deliver clear ROI. You control the cost curve. |
| Air-Gapped & Classified Deployments | Impossible. Commercial model APIs require internet connectivity and send data to third-party servers. Classified and regulated workloads are blocked entirely. | Full air-gapped operation with locally deployed open-source models. Zero data egress. Runs on your hardware in your facility with no external dependencies. |
| Custom & Fine-Tuned Models | Vendor platforms only support their own model catalog. Proprietary fine-tuned models trained on your data cannot be deployed or integrated into workflows. | Custom and fine-tuned models are first-class citizens. Deploy your proprietary models alongside commercial APIs and route tasks to whichever model performs best. |
| Model Deprecation Risk | When a vendor deprecates a model, production systems break. Emergency migrations under time pressure cause outages, data integrity risks, and engineering burnout. | Add the replacement model to the registry, test it against existing workflows, and cut over on your schedule. No emergency. No outage. No rebuild. |
| Multi-Model Orchestration | Every task routes through the same model regardless of fit. Simple classification tasks cost the same as complex reasoning tasks. No optimization is possible. | Each agent uses the optimal model for its task. Fast cheap models handle classification. Powerful models handle reasoning. Local models handle sensitive data. Cost and performance are both optimized. |
| Platform Ownership | The platform is a black box. You have no visibility into how model calls are made, no ability to modify routing logic, and no recourse if the vendor changes behavior. | Full source code ownership. The model integration layer is transparent, auditable, and modifiable. Your team can build new model connectors without waiting for a vendor roadmap. |
Locked to one provider's model family. Switching requires rebuilding integrations, re-engineering prompts, and re-validating every workflow from scratch.
Any model — commercial or open-source — connects to the same platform. Swap models in configuration, not in code. Workflows continue without changes.
Zero negotiating leverage. When the vendor raises API prices, you pay or you rebuild. Budget forecasting is impossible when costs are controlled by a third party.
Run competing models in parallel. Route cost-sensitive tasks to cheaper or local models. Use commercial APIs only where they deliver clear ROI. You control the cost curve.
Impossible. Commercial model APIs require internet connectivity and send data to third-party servers. Classified and regulated workloads are blocked entirely.
Full air-gapped operation with locally deployed open-source models. Zero data egress. Runs on your hardware in your facility with no external dependencies.
Vendor platforms only support their own model catalog. Proprietary fine-tuned models trained on your data cannot be deployed or integrated into workflows.
Custom and fine-tuned models are first-class citizens. Deploy your proprietary models alongside commercial APIs and route tasks to whichever model performs best.
When a vendor deprecates a model, production systems break. Emergency migrations under time pressure cause outages, data integrity risks, and engineering burnout.
Add the replacement model to the registry, test it against existing workflows, and cut over on your schedule. No emergency. No outage. No rebuild.
Every task routes through the same model regardless of fit. Simple classification tasks cost the same as complex reasoning tasks. No optimization is possible.
Each agent uses the optimal model for its task. Fast cheap models handle classification. Powerful models handle reasoning. Local models handle sensitive data. Cost and performance are both optimized.
The platform is a black box. You have no visibility into how model calls are made, no ability to modify routing logic, and no recourse if the vendor changes behavior.
Full source code ownership. The model integration layer is transparent, auditable, and modifiable. Your team can build new model connectors without waiting for a vendor roadmap.
Full compliance with data sovereignty requirements while accessing state-of-the-art AI capabilities on sensitive missions.
PHI never leaves the hospital network while operational AI costs are optimized across the full workflow portfolio.
Proprietary financial models deliver superior accuracy on specialized tasks without sacrificing the breadth of general-purpose AI capabilities.
Law firms maintain best-in-class AI performance without rebuilding their platform every time a superior model is released.
Critical infrastructure AI operates reliably in remote or network-restricted environments without cloud API dependencies.
Manufacturers leverage their unique operational data as a competitive AI advantage while maintaining flexibility across the enterprise.
Each stage of the claims workflow runs on the optimal model, reducing cost by 40-60% versus routing everything through a single premium API.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.