The first oracles in crypto date as far back as 2016 and 'Oraclize'. Oraclize is in fact still live and providing data feeds on Ethereum. However, unlike later platforms it is entirely centralized, in a market that does not like single points of failure.
Another weakness shared by most oracle platforms is that the range of data they support is very narrow - typically limited to financial data, especially price feeds. Given the high premium on volume in crypto, the information provided is typically also limited in other ways. Outside the blockchain, tapping into for example an exchange API can give you a live feed not only on the latest spot price for Bitcoin, but also order depth, trade volume, price slippage for large orders, etc. This kind of rich information rarely gets transmitted by oracle services - the bloat required to print so much information would cost too much for most data buyers who might otherwise be interested... In addition, oracle platforms up to now have varied greatly in their approach on ensuring high quality data. Rating and reputation systems work differently, and some platforms do not even have them.
For Chainlink, a data provider improves their reputation score by receiving and completing assignments, fulfilling orders quickly, and by staking a greater number of tokens. The more tokens they stake, the more they have to lose by getting caught selling bad data, and the more exposed they are to a fall in the price of the token, should the network develop a bad reputation. This system has afforded data buyers a high degree of confidence in the data they buy, but there have been a few unintended tradeoffs. Data providers are penalized for failing to fulfill even spam orders, which can force the providers to choose between paying high network fees, losing them money, or abandoning requests, penalizing their reputation score. In August 2020 the Chainlink network was hit with a sustained spam attack that caused data providers to lose over 700 ETH over a few hours to protect their reputation scores.
DOS Network also has a reputation system of sorts, but it is not one consumers can interact with directly. Multiple data providers are randomly selected to handle any query. Malicious and excessively inactive members lose their stake. There is no human interaction involved – if the other randomly selected data providers reach a consensus that disagrees with you, your tokens are gone. For data requests where only one reasonable answer can be given, any deviation from the correct answer is of course suspect, however this approach does also limit DOS Network Oracles to providing fairly terse data, with little room for requests where the answer might change by tiny degrees from one second to the next. The system also requires multiple data providers to perform the same data collection and computations, necessitating a significant amount of wasteful, redundant work which eats into the profits of each data provider and raises the cost for the buyer.
Our third example, Band, does not have a traditional Reputation system. Instead they allow stakers to challenge any data provided, resulting in a trial with fellow stakers as jury, with stake determining voting strength. To prevent wealthy attackers from buying enough voting power to steal trials they are implementing time delays to begin staking and to unstake, but importantly they are also using a bonded curve for the generation and sale of the specific sub-tokens staked to be a data provider. This means that a wealthy attacker will pay an increasingly higher price for every token they need to secure their voting power, making it impractical and expensive to join the network only to attack it. However, it also means that honest participants seeking to join the ecosystem have to pay that same increasing price, in what is effectively an unavoidable wealth transfer from all new participants to the oldest. While this approach is understandably popular with very early adopters, it cannot help but struggle to gain acceptance with a maturing market.