How to choose between OpenAI, Anthropic, and open-source models
TL;DR
- There's no single best provider. The right choice depends on your task, your data residency requirements, your latency budget, and how much control you actually need.
- Proprietary models (OpenAI, Anthropic) win on raw capability and speed-to-ship. Open-source wins when you need data to stay in-house, predictable cost at scale, or full control over the stack.
- Build a thin abstraction layer and route across providers per task. The teams that avoid lock-in treat the model as a swappable component, not a foundation.
Start with the task, not the brand
Most teams pick a provider the way they pick a phone: brand loyalty, a demo that impressed them, or whatever a competitor mentioned on a podcast. That's backwards. The model is a component inside a system, and the system has requirements. Figure those out first.
The pattern we see across the systems we ship: the 'which model is best' question dissolves the moment you write down what the workload actually does. A nightly batch job that summarizes ten thousand support tickets has nothing in common with a live chat agent that needs sub-second responses. One cares about cost per million tokens. The other cares about time-to-first-token. Asking which provider is better in the abstract is like asking whether a van is better than a motorbike.
- Classify the workload: interactive (latency-sensitive) vs batch (throughput-sensitive) vs offline (cost-sensitive).
- Write down the quality bar: does this task tolerate a 5% error rate, or is it a legal document where one mistake is unacceptable?
- Note the volume: a thousand calls a day and a hundred million calls a day push you toward completely different economics.
- Flag the data: is any of it personal, regulated, or commercially sensitive enough that where it gets processed matters?
- Decide the failure mode: what happens when the provider has an outage at 2am? That answer shapes everything downstream.
The seven axes that actually decide it
Once the task is clear, every provider decision comes down to the same set of trade-offs. We use seven axes. None of them is optional, and they pull against each other, which is exactly why there's no universal winner.
The trick is to weight them per use case rather than treating them as a single score. A customer-facing assistant weights latency and capability heavily. An internal tool processing payroll data weights privacy and control above almost everything else. Same company, same week, two different correct answers.
- Capability: can the model do the task at the quality you need? Test on your data, not on public benchmarks that everyone has trained against.
- Cost: not just price per token, but total cost including retries, long context, and the engineering time to run it.
- Latency: time-to-first-token and tokens-per-second. Proprietary APIs are usually fast and consistent; self-hosted depends entirely on your hardware.
- Data privacy: where does the data physically go, who can see it, and is it used for training? Read the data processing terms, don't assume.
- Control: can you pin a model version, tune it, run it offline, and guarantee it won't change under you? Open weights give you all of this.
- Ecosystem: tooling, SDKs, function calling, structured output, and the community that has already solved your edge cases.
- Lock-in: how painful is it to leave? Measure it in engineering weeks, then assume you'll need to one day.
When proprietary models earn their keep
OpenAI and Anthropic (and a handful of others) sell you the frontier plus the operational headache of running it removed. For most teams starting out, that's the right deal. You get top-tier capability behind an API, someone else handles the GPUs, and you ship this quarter instead of next year.
Proprietary wins clearly when you need the absolute best reasoning available, when your volume is moderate enough that hosting your own would cost more than the API, or when you don't have an ML platform team and don't want to build one. The honest version: if you're asking whether to self-host and you don't already run GPU infrastructure, you probably shouldn't yet.
- You need frontier-level capability today and can't wait for an open model to catch up on your specific task.
- Your call volume is low to moderate, so the per-token price never crosses the line where owning hardware pays off.
- You want managed reliability, autoscaling, and a support contract instead of a pager rotation.
- You're prototyping and need to validate the product before committing to any infrastructure.
- You benefit from features that ship first on the big platforms, like advanced tool use and long-context handling.
When open-source is the smarter call
Open-weight models (the Llama, Mistral, Qwen, and Gemma families, among others) have closed enough of the gap that for a large share of real production tasks, the quality difference no longer matters. Classification, extraction, summarization, routing, and most retrieval-augmented work run fine on a well-chosen open model. You're not always buying frontier reasoning; often you're buying 'good enough, on my terms.'
The economics flip at scale. An API that costs a few dollars per million tokens is cheap until you're doing billions of tokens a month, at which point a rented or owned GPU cluster running an open model can cut the bill by a large factor. Control is the other half: you pin the exact weights, nothing changes without your say-so, and the model still works if the vendor raises prices, deprecates your version, or goes down.
- Data has to stay inside your network or a specific jurisdiction (more on this below).
- Volume is high enough that self-hosting is cheaper than per-token pricing, even after you count engineering time.
- You need a model that never changes underneath you, for reproducibility, compliance, or audit reasons.
- The task is narrow and well-defined, so a smaller fine-tuned open model beats a giant general one on both cost and accuracy.
- You want to fine-tune deeply on proprietary data without sending that data to a third party.
Self-hosting open models for data residency
Data residency is the cleanest reason to run your own models, and often the only one that survives a hard cost analysis. If you operate under rules that say customer data cannot leave a region (or your own infrastructure at all), self-hosting an open-weight model isn't a preference, it's the requirement. You can put the whole inference path inside a VPC in the right region and prove the data never crossed a boundary.
Be clear-eyed about what you're taking on, though. Self-hosting means GPU capacity planning, model serving infrastructure, autoscaling, monitoring, and a team that can debug a stuck inference server at midnight. The model weights are free; the operations are not. The teams that do this well treat it as a platform investment, not a one-off deployment, and they only commit once the residency or cost case is undeniable.
- Confirm the actual requirement: 'data stays in-region' and 'data never leaves our servers' are different rules with very different costs.
- Check whether the proprietary providers already offer a compliant regional or private deployment before you build anything.
- Size the GPU footprint against peak load, not average, or your latency falls apart exactly when traffic spikes.
- Pick a serving stack you can operate, and budget for the on-call burden that comes with owning inference.
- Right-size the model: a smaller model that fits on cheaper hardware often beats a giant one you can barely afford to run.
The route-across-providers approach
The most resilient setup we deploy doesn't bet on one provider at all. It puts a thin routing layer between your application and the models, then sends each request to whichever model fits that request best. Cheap, high-volume classification goes to a small open model. Hard reasoning goes to a frontier API. A sensitive workload gets pinned to your self-hosted instance. The application code doesn't know or care which one answered.
This sounds like more work, and at the start it is, but it pays back fast. You stop being held hostage by any single price change or outage. You can A/B a new model against your current one by flipping a config value. And when a provider ships something better next quarter (they always do), adopting it is a routing change, not a rewrite. The cost is real, so don't over-engineer it on day one, but design the seam in early even if you only use one model at first.
- Define a single internal interface for 'generate text' so swapping models never touches business logic.
- Route by task type, cost ceiling, and data sensitivity, not by whichever provider you signed up with first.
- Keep a fallback chain so an outage on the primary fails over instead of failing the user.
- Normalize prompts and outputs across providers, since structured output and tool-calling formats differ.
- Log which model handled each request so you can compare quality and cost with real data, not vibes.
Designing the exit before you need it
Lock-in is rarely a single decision. It accumulates. You adopt one provider's tool-calling format, then their structured-output schema, then a feature only they offer, and six months later switching costs a quarter of engineering time nobody has. The way out is to assume from day one that you will change providers, because over a multi-year horizon you almost certainly will.
Avoiding lock-in doesn't mean refusing to use a provider's best features. It means isolating them. Wrap provider-specific behavior behind your own abstraction, keep your prompts and evaluation suite in your own repo, and own your data and embeddings rather than parking them in a format only one vendor reads. Then run a regular drill: can you actually point your traffic at a different model and pass your evals? If you've never tried, you're more locked in than you think.
- Own your evaluation suite so you can score any candidate model against your real tasks on demand.
- Keep prompts, fine-tuning data, and embeddings in portable formats you control, not vendor-only stores.
- Abstract provider-specific features (tool calling, structured output) behind your own stable interface.
- Avoid building core workflows on a single proprietary feature with no equivalent anywhere else.
- Periodically test a full provider switch in staging so 'we could leave' stays true instead of aspirational.
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Book a strategy callCommon questions
Is OpenAI or Anthropic better?
Neither is universally better. They trade leads on different tasks and release new models constantly, so any ranking is stale within months. The right move is to test both on your actual workload with your own evaluation suite, then route to whichever wins per task rather than picking one for everything.
Are open-source models good enough for production?
For a large share of production tasks, yes. Classification, extraction, summarization, routing, and most retrieval work run well on well-chosen open models. Where frontier reasoning genuinely matters, the proprietary models still lead. Test on your task instead of trusting public benchmarks, which everyone optimizes against.
When does self-hosting an open model actually save money?
When your volume is high enough that per-token API pricing exceeds the cost of running your own GPUs, including engineering and on-call time. At low to moderate volume, APIs are almost always cheaper. The other case where self-hosting pays is data residency, where it can be the only compliant option regardless of cost.
How do I keep my data private when using a model provider?
Read the data processing terms rather than assuming. Check where data is processed, who can access it, and whether it's used for training (the major API providers offer terms that exclude business API traffic from training). If the rules require data to stay in a region or on your own infrastructure, self-host an open model inside a controlled environment.
What's the best way to avoid vendor lock-in with AI models?
Treat the model as a swappable component. Put a thin abstraction layer between your application and any provider, keep your prompts, evaluation suite, and embeddings in formats you own, and avoid building core workflows on a single proprietary-only feature. Then periodically test an actual provider switch so your ability to leave stays real.
Neither is universally better. They trade leads on different tasks and release new models constantly, so any ranking is stale within months. The right move is to test both on your actual workload with your own evaluation suite, then route to whichever wins per task rather than picking one for everything.
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