Lower migration friction
Start from familiar SDK conventions instead of rewriting an application around a completely different request schema.
A familiar API shape for AI applications
An OpenAI-compatible API for teams that value portability
Build against a familiar request shape, use established OpenAI SDK patterns, and keep a practical path between the vMira workspace and the applications your team ships.
Compatible
with familiar OpenAI SDK patterns
Chat
completion workflows
Embeddings
for retrieval and similarity
API keys
managed in the developer platform
Why buyers evaluate vMira
Preserve provider options
Keep the application architecture compatible with the broader OpenAI-style ecosystem.
Join product and developer work
Use vMira as both an interactive workspace and a developer platform rather than maintaining unrelated vendors.
Guide 1
What OpenAI-compatible means
Compatibility means developers can use familiar endpoints, payload conventions and SDK configuration patterns for supported operations. It does not mean every provider, model or feature behaves identically. Treat compatibility as a faster integration path and test the behavior your application depends on.
- Configure the supported SDK with the vMira API base URL and your API key.
- Use supported chat-completion patterns for conversational and generation workloads.
- Use supported embedding operations for retrieval, clustering or similarity workflows.
- Keep model names, context behavior, tool calling and output formats behind your own adapter.
- Test errors, retries, timeouts and streaming before sending production traffic.
Guide 2
A responsible migration checklist
A compatible request can still produce different latency, limits or model behavior. Migrate with an evaluation set that represents production, then compare response quality and operational behavior before changing the default provider.
- Inventory every endpoint and parameter your application currently uses.
- Create a representative test set with expected outputs or review criteria.
- Validate streaming, structured output and tool behavior if your application uses them.
- Measure latency distributions and error rates, not only average response time.
- Review current pricing, quotas and data-handling terms.
- Roll out gradually with monitoring and an easy provider fallback.
Guide 3
Where a compatible API creates business value
The strongest use case is optionality. A stable application layer makes it easier to evaluate providers, control spend and adopt new models without rebuilding the whole product. Compatibility also helps teams reuse existing libraries, observability and developer knowledge.
- Internal copilots and knowledge assistants.
- Customer-facing chat and support experiences.
- Document enrichment and structured extraction pipelines.
- Retrieval systems using embeddings and vector search.
- Content, code and workflow features inside existing software.
- Provider evaluation environments and controlled fallback paths.
Choose the next workflow
Explore a relevant product surface or buyer guide. These links are part of the same vMira product, so you can compare the specific capability before committing.
Developer documentation
Review current endpoints, authentication and supported operations.
Open API docsDeveloper pricing
Check current API plans, allowances and commercial terms.
See API pricingEngineering assistant
Use vMira interactively while you build and debug an integration.
Explore engineering AICompare ChatGPT
Read the buyer-focused comparison between vMira and ChatGPT.
Read comparisonIs vMira the right fit?
Your requirementFitWhy
Existing OpenAI SDK codeStrong fitCompatibility can reduce the initial integration change.
Chat completions and embeddingsStrong fitThese are the documented compatibility use cases.
Provider portabilityStrong fitA familiar schema supports a cleaner adapter strategy.
Exact vendor-specific parityTest carefullyCompatibility is not identical behavior for every feature.
Unverified production migrationDo not rushBenchmark quality, limits, latency and errors first.
Best for
- Teams with existing OpenAI-style integrations
- Developers building chat or retrieval features
- Products that value provider portability
- Teams willing to benchmark before migration
Compare carefully if
- Applications that require undocumented vendor-specific behavior
- Teams that will not test production-like workloads
- Buyers choosing solely from a headline price
Questions buyers ask
What is an OpenAI-compatible API?+
It is an API that supports familiar OpenAI-style endpoints and request patterns for specified operations. Developers can often reuse an existing SDK by changing configuration, but they should still test models, parameters and behavior.
Can I use an OpenAI SDK with vMira?+
vMira documents an OpenAI-compatible API. Configure the supported SDK for the vMira API base URL and authenticate with a vMira API key. Follow the current developer documentation for exact examples.
Does compatible mean identical?+
No. Providers can differ in model names, supported parameters, limits, latency and output behavior. Compatibility reduces integration friction; it does not remove the need for evaluation.
Which workloads should I test first?+
Start with the highest-volume and highest-risk paths in your application. Include long inputs, structured responses, streaming, error cases and any tool behavior you depend on.
Where can I see current API pricing?+
Use the developer pricing page on platform.vmira.ai. Pricing and allowances can change, so the live page should be treated as the source of truth.
Evaluate with real work
Run your next project in vMira
Start on the free plan. Use the same files, questions and deliverables you handle today, then compare the completed workflow—not just the first answer.