Welcome to EidoStack

AI teams have more model choices than ever.

OpenAI, Anthropic, Google AI, and other providers continue to release new models with different strengths, context windows, pricing, latency profiles, and behavior. A model that looks excellent on a public benchmark may still be the wrong choice for your product. A cheaper model may be good enough for one workflow but fail repeatedly on another. A more capable model may produce better answers while making the economics of a high-volume feature difficult to justify.

That makes model selection an engineering problem.

EidoStack is a professional workspace for evaluating, comparing, and selecting AI models for your applications. It is built to help AI engineers move from intuition and one-off demos toward repeatable experiments based on their own prompts, workloads, costs, and constraints.

Welcome to EidoStack.

Why we built EidoStack

A common model-evaluation workflow is still surprisingly manual.

You open one provider playground, send a prompt, copy the result, open another provider, recreate the same setup, send the prompt again, and compare the outputs in separate tabs. Then you try to remember which system prompt you used, how much context was included, how many tokens each request consumed, and whether the quality difference is worth the price difference.

That process becomes harder as the number of models grows.

The important question is rarely:

Which AI model is the best?

The useful question is:

Which model is the best fit for this task, with this prompt, this context, this quality bar, and this cost profile?

EidoStack was created around that distinction.

The goal is not to declare a universal winner. The goal is to give engineers a consistent workspace where they can test candidate models under comparable conditions, inspect the results, understand the operational trade-offs, and make a decision they can explain.

One workspace for multiple AI providers

EidoStack currently supports models through your own API credentials from:

• OpenAI
• Anthropic
• Google AI

The model catalog brings GPT, Claude, and Gemini models into the same browser-based workflow. Instead of adapting your evaluation process to a different interface for every provider, you can work from one consistent environment and focus on the model behavior itself.

This provider-independent approach is deliberate. Model capabilities change quickly, and the model that makes sense for one application today may not be the best choice for the next workload or the next release.

EidoStack is designed so that changing providers does not require changing the way you evaluate them.

You can learn more in Supported providers and models.

Compare models side by side

One of the core EidoStack workflows is Compare mode.

It sends the same prompt to two selected models and displays the responses side by side. Free users can compare models from their one configured provider; Pro users can compare models across different configured providers while keeping the rest of the experiment as consistent as possible.

That makes differences easier to see:

• Does one model follow the instruction more precisely?
• Does one return a cleaner structure?
• Which model handles uncertainty better?
• Does the more expensive model actually produce a meaningfully better result?
• Is one response substantially longer and therefore more costly?
• Does a model that performs well on a simple prompt still hold up on edge cases?

A fair comparison is more useful when the important variables stay controlled. EidoStack lets both sides share the same conversation-context strategy, while system prompts can either remain identical for a model-only comparison or be intentionally changed when prompt design itself is under evaluation.

See Compare model responses for the full workflow.

Evaluate prompts and system instructions

The model is only one part of an AI application's behavior.

System prompts, output requirements, role instructions, formatting rules, and conversation history can change the result dramatically. EidoStack therefore treats prompt configuration as part of the evaluation process rather than as an afterthought.

You can define a default system prompt for new chats, override it for an individual evaluation, or use separate system prompts for Model A and Model B in Compare mode.

This makes it possible to run different kinds of experiments:

• keep the system prompt fixed and compare two models;
• keep the model fixed and test different instructions;
• refine a prompt until it reaches the required quality bar;
• verify that a new model still follows the rules your application depends on.

The important part is knowing what changed between experiments.

For more details, see System prompts.

Control how conversation context is used

Long conversations create another engineering trade-off: how much history should actually be sent to the model?

More context is not automatically better. It can increase token usage and cost, consume the model's context window, and introduce information that is no longer relevant to the current task.

EidoStack provides three Context Memory strategies:

• Full History sends the conversation history with the new prompt.
• Recent Messages sends a configurable window from the conversation.
• Semantic Memory retrieves earlier messages related to the current prompt.

These strategies let you test the context behavior your application is likely to use rather than evaluating every model under an unrealistic unlimited-history assumption.

EidoStack also exposes context-capacity information so you can see how the request fits within the selected model's context window.

Read Context memory to understand the available strategies and their trade-offs.

Put quality, tokens, and cost in the same decision

A good model decision cannot be based on response quality alone.

Suppose Model A gives a slightly better answer but costs several times more than Model B. That difference might be irrelevant for an internal tool handling a few requests per day, or critical for a customer-facing feature processing millions of requests.

The right choice depends on the workload.

EidoStack records usage information alongside completed requests so you can inspect:

• input tokens;
• output tokens;
• total tokens;
• estimated model cost;
• context-window usage;
• per-model breakdowns;
• usage across chats and date ranges.

Workspace-level Usage Metrics provide Overall, Per Model, and Per Chat views, making it easier to understand how experiments behave beyond a single response.

The cost values are intended as evaluation signals; provider billing remains authoritative. But keeping cost close to the response makes an important question much easier to answer:

Is the quality improvement worth what it costs?

See Usage, tokens, and costs.

Bring your own API keys, without handing them to the application server

EidoStack uses a Bring Your Own API Keys (BYOK) model.

Provider credentials are stored in an account-scoped encrypted vault in your browser. You create a separate vault password, and the browser uses it to derive the encryption material needed to protect your provider keys. The encrypted provider-key record is kept in browser storage rather than stored as a provider-key value in the EidoStack application database.

When the vault is unlocked, the browser can use the appropriate credential for the provider you selected. When it is locked, model requests cannot use those stored keys.

This architecture keeps credential handling close to the device where the evaluation is happening while still giving you the convenience of a browser-based SaaS workspace.

Security does not stop at encryption, however. Requests still go to the AI provider you select, so prompts and context should contain only information you are authorized to send to that provider.

The complete security model is documented in API key vault.

A workspace, not just another chat interface

EidoStack has a chat-based interface because conversation is a practical way to work with language models. But the product is not built around conversation for its own sake.

The chat is the experiment record.

It keeps the prompt, model choice, response, system instruction, context strategy, and usage information together so you can revisit the reasoning behind a decision. Chats and folders help organize evaluations, while automatic naming and prompt history reduce friction during repeated experiments.

The interface supports both light and dark appearance modes and is designed to keep the controls required for evaluation close to the response you are reviewing.

The broader idea is simple:

Measure. Compare. Decide.

What EidoStack does not try to do

Clear product boundaries matter.

EidoStack does not train or fine-tune AI models. It does not modify model weights, and it does not claim to "optimize" the models themselves.

Instead, it helps you optimize model selection and model usage.

It also does not replace the evaluation criteria your application needs. EidoStack can show two responses side by side and expose their token, cost, and context characteristics, but the quality bar still belongs to you.

For a coding assistant, success may mean correct code and passing tests. For extraction, it may mean schema accuracy. For customer support, it may mean factuality, tone, and safe escalation. Different workloads require different rubrics.

EidoStack gives you the workspace and evidence. Your requirements determine the winner.

Where EidoStack is going

The AI model ecosystem is moving quickly, and EidoStack is intended to evolve with it.

The direction of the product is guided by a few principles:

• Provider independence. Evaluation should not be locked to one model vendor.
• Controlled comparison. Experiments are more useful when you can explain which variable changed.
• Cost transparency. Model quality and operational cost belong in the same decision.
• Context visibility. Engineers should understand what is being sent to a model and how much capacity it consumes.
• Security by architecture. Sensitive credentials should be protected through technical boundaries, not marketing promises.
• Usability. Professional evaluation tooling should reduce friction rather than create a new layer of complexity.

New capabilities will continue to build on the same foundation: helping engineers understand model behavior and make better decisions about which models to use in their products.

Documentation for using the platform

The EidoStack documentation is already available at eidostack.com/docs.

If you are new to the platform, a good path is:

1. Read Introduction to EidoStack.
2. Create your account.
3. Complete workspace setup.
4. Configure your first AI provider.
5. Start your first chat.
6. Run a structured experiment with Evaluate a model.
7. Compare two candidates with Compare model responses.

The documentation is the place for product workflows, settings, security details, and step-by-step instructions.

What to expect from this blog

The blog will have a different purpose.

Instead of documenting buttons and settings, it will focus on the engineering questions around modern AI models and the lessons that come from testing them.

Expect posts about topics such as:

• practical model comparisons;
• new model releases;
• cost versus quality trade-offs;
• prompt and context experiments;
• agentic and software-engineering workloads;
• evaluation methodology;
• real examples built with different models;
• EidoStack product updates and technical decisions.

When a new model claims to be faster, cheaper, better at coding, or stronger at agentic workflows, we want to test what that means in practice.

Not just token price.

Not just a leaderboard score.

The useful metric is whether the model produces a successful result for the task, how reliably it gets there, and what that result costs.

Start with one real decision

You do not need a large benchmark suite to begin evaluating models more systematically.

Choose one real task from your application. Pick two candidate models. Use the same prompt and context. Define what a successful answer looks like. Run the comparison, inspect the responses, and look at the token and cost implications.

Then add the edge cases that matter to your product.

That is the workflow EidoStack is built to support: moving from "this model looks good" to "this model is the right fit for this workload, and here is the evidence."

Welcome to EidoStack.